research learning skills

What are research skills?

Last updated

26 April 2023

Reviewed by

Broadly, it includes a range of talents required to:

Find useful information

Perform critical analysis

Form hypotheses

Solve problems

It also includes processes such as time management, communication, and reporting skills to achieve those ends.

Research requires a blend of conceptual and detail-oriented modes of thinking. It tests one's ability to transition between subjective motivations and objective assessments to ensure only correct data fits into a meaningfully useful framework.

As countless fields increasingly rely on data management and analysis, polishing your research skills is an important, near-universal way to improve your potential of getting hired and advancing in your career.

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

What are basic research skills?

Almost any research involves some proportion of the following fundamental skills:

Organization

Decision-making

Investigation and analysis

Creative thinking

What are primary research skills?

The following are some of the most universally important research skills that will help you in a wide range of positions:

Time management — From planning and organization to task prioritization and deadline management, time-management skills are highly in-demand workplace skills.

Problem-solving — Identifying issues, their causes, and key solutions are another essential suite of research skills.

Critical thinking — The ability to make connections between data points with clear reasoning is essential to navigate data and extract what's useful towards the original objective.

Communication — In any collaborative environment, team-building and active listening will help researchers convey findings more effectively through data summarizations and report writing.

What are the most important skills in research?

Detail-oriented procedures are essential to research, which allow researchers and their audience to probe deeper into a subject and make connections they otherwise may have missed with generic overviews.

Maintaining priorities is also essential so that details fit within an overarching strategy. Lastly, decision-making is crucial because that's the only way research is translated into meaningful action.

• Why are research skills important?

Good research skills are crucial to learning more about a subject, then using that knowledge to improve an organization's capabilities. Synthesizing that research and conveying it clearly is also important, as employees seek to share useful insights and inspire effective actions.

Effective research skills are essential for those seeking to:

Analyze their target market

Investigate industry trends

Identify customer needs

Detect obstacles

Find solutions to those obstacles

Develop new products or services

Develop new, adaptive ways to meet demands

Discover more efficient ways of acquiring or using resources

Why do we need research skills?

Businesses and individuals alike need research skills to clarify their role in the marketplace, which of course, requires clarity on the market in which they function in. High-quality research helps people stay better prepared for challenges by identifying key factors involved in their day-to-day operations, along with those that might play a significant role in future goals.

• Benefits of having research skills

Research skills increase the effectiveness of any role that's dependent on information. Both individually and organization-wide, good research simplifies what can otherwise be unwieldy amounts of data. It can help maintain order by organizing information and improving efficiency, both of which set the stage for improved revenue growth.

Those with highly effective research skills can help reveal both:

Opportunities for improvement

Brand-new or previously unseen opportunities

Research skills can then help identify how to best take advantage of available opportunities. With today's increasingly data-driven economy, it will also increase your potential of getting hired and help position organizations as thought leaders in their marketplace.

• Research skills examples

Being necessarily broad, research skills encompass many sub-categories of skillsets required to extrapolate meaning and direction from dense informational resources. Identifying, interpreting, and applying research are several such subcategories—but to be specific, workplaces of almost any type have some need of:

Searching for information

Attention to detail

Taking notes

Problem-solving

Communicating results

Time management

• How to improve your research skills

Whether your research goals are to learn more about a subject or enhance workflows, you can improve research skills with this failsafe, four-step strategy:

Make an outline, and set your intention(s)

Know your sources

Learn to use advanced search techniques

Practice, practice, practice (and don't be afraid to adjust your approach)

These steps could manifest themselves in many ways, but what's most important is that it results in measurable progress toward the original goals that compelled you to research a subject.

• Using research skills at work

Different research skills will be emphasized over others, depending on the nature of your trade. To use research most effectively, concentrate on improving research skills most relevant to your position—or, if working solo, the skills most likely have the strongest impact on your goals.

You might divide the necessary research skills into categories for short, medium, and long-term goals or according to each activity your position requires. That way, when a challenge arises in your workflow, it's clearer which specific research skill requires dedicated attention.

How can I learn research skills?

Learning research skills can be done with a simple three-point framework:

Clarify the objective — Before delving into potentially overwhelming amounts of data, take a moment to define the purpose of your research. If at any point you lose sight of the original objective, take another moment to ask how you could adjust your approach to better fit the original objective.

Scrutinize sources — Cross-reference data with other sources, paying close attention to each author's credentials and motivations.

Organize research — Establish and continually refine a data-organization system that works for you. This could be an index of resources or compiling data under different categories designed for easy access.

Which careers require research skills?

Especially in today's world, most careers require some, if not extensive, research. Developers, marketers, and others dealing in primarily digital properties especially require extensive research skills—but it's just as important in building and manufacturing industries, where research is crucial to construct products correctly and safely.

Engineering, legal, medical, and literally any other specialized field will require excellent research skills. Truly, almost any career path will involve some level of research skills; and even those requiring only minimal research skills will at least require research to find and compare open positions in the first place.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 11 January 2024

Last updated: 15 January 2024

Last updated: 17 January 2024

Last updated: 12 May 2023

Last updated: 30 April 2024

Last updated: 18 May 2023

Last updated: 25 November 2023

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free

Explore Jobs

• Jobs Near Me
• Remote Jobs
• Full Time Jobs
• Part Time Jobs
• Entry Level Jobs
• Work From Home Jobs

Find Specific Jobs

• $15 Per Hour Jobs
• $20 Per Hour Jobs
• Hiring Immediately Jobs
• High School Jobs
• H1b Visa Jobs

Explore Careers

• Business And Financial
• Architecture And Engineering
• Computer And Mathematical

Explore Professions

• What They Do
• Certifications
• Demographics

Best Companies

• Health Care
• Fortune 500

Explore Companies

• CEO And Executies
• Resume Builder
• Career Advice
• Explore Majors
• Questions And Answers
• Interview Questions

The Most Important Research Skills (With Examples)

• What Are Hard Skills?
• What Are Technical Skills?
• What Are What Are Life Skills?
• What Are Social Media Skills Resume?
• What Are Administrative Skills?
• What Are Analytical Skills?
• What Are Research Skills?
• What Are Transferable Skills?
• What Are Microsoft Office Skills?
• What Are Clerical Skills?
• What Are Computer Skills?
• What Are Core Competencies?
• What Are Collaboration Skills?
• What Are Conflict Resolution Skills?
• What Are Mathematical Skills?
• How To Delegate

Find a Job You Really Want In

Research skills are the ability to find out accurate information on a topic. They include being able to determine the data you need, find and interpret those findings, and then explain that to others. Being able to do effective research is a beneficial skill in any profession, as data and research inform how businesses operate.

Whether you’re unsure of your research skills or are looking for ways to further improve them, then this article will cover important research skills and how to become even better at research.

Key Takeaways

Having strong research skills can help you understand your competitors, develop new processes, and build your professional skills in addition to aiding you in finding new customers and saving your company money.

Some of the most valuable research skills you can have include goal setting, data collection, and analyzing information from multiple sources.

You can and should put your research skills on your resume and highlight them in your job interviews.

What are research skills?

Why are research skills important, 12 of the most important research skills, how to improve your research skills, highlighting your research skills in a job interview, how to include research skills on your resume, resume examples showcasing research skills, research skills faqs.

• Sign Up For More Advice and Jobs

Research skills are the necessary tools to be able to find, compile, and interpret information in order to answer a question. Of course, there are several aspects to this. Researchers typically have to decide how to go about researching a problem — which for most people is internet research.

In addition, you need to be able to interpret the reliability of a source, put the information you find together in an organized and logical way, and be able to present your findings to others. That means that they’re comprised of both hard skills — knowing your subject and what’s true and what isn’t — and soft skills. You need to be able to interpret sources and communicate clearly.

Research skills are useful in any industry, and have applications in innovation, product development, competitor research, and many other areas. In addition, the skills used in researching aren’t only useful for research. Being able to interpret information is a necessary skill, as is being able to clearly explain your reasoning.

Research skills are used to:

Do competitor research. Knowing what your biggest competitors are up to is an essential part of any business. Researching what works for your competitors, what they’re doing better than you, and where you can improve your standing with the lowest resource expenditure are all essential if a company wants to remain functional.

Develop new processes and products. You don’t have to be involved in research and development to make improvements in how your team gets things done. Researching new processes that make your job (and those of your team) more efficient will be valued by any sensible employer.

Foster self-improvement. Folks who have a knack and passion for research are never content with doing things the same way they’ve always been done. Organizations need independent thinkers who will seek out their own answers and improve their skills as a matter of course. These employees will also pick up new technologies more easily.

Manage customer relationships. Being able to conduct research on your customer base is positively vital in virtually every industry. It’s hard to move products or sell services if you don’t know what people are interested in. Researching your customer base’s interests, needs, and pain points is a valuable responsibility.

Save money. Whether your company is launching a new product or just looking for ways to scale back its current spending, research is crucial for finding wasted resources and redirecting them to more deserving ends. Anyone who proactively researches ways that the company can save money will be highly appreciated by their employer.

Solve problems. Problem solving is a major part of a lot of careers, and research skills are instrumental in making sure your solution is effective. Finding out the cause of the problem and determining an effective solution both require accurate information, and research is the best way to obtain that — be it via the internet or by observation.

Determine reliable information. Being able to tell whether or not the information you receive seems accurate is a very valuable skill. While research skills won’t always guarantee that you’ll be able to tell the reliability of the information at first glance, it’ll prevent you from being too trusting. And it’ll give the tools to double-check .

Experienced researchers know that worthwhile investigation involves a variety of skills. Consider which research skills come naturally to you, and which you could work on more.

Data collection . When thinking about the research process, data collection is often the first thing that comes to mind. It is the nuts and bolts of research. How data is collected can be flexible.

For some purposes, simply gathering facts and information on the internet can fulfill your need. Others may require more direct and crowd-sourced research. Having experience in various methods of data collection can make your resume more impressive to recruiters.

Data collection methods include: Observation Interviews Questionnaires Experimentation Conducting focus groups

Analysis of information from different sources. Putting all your eggs in one source basket usually results in error and disappointment. One of the skills that good researchers always incorporate into their process is an abundance of sources. It’s also best practice to consider the reliability of these sources.

Are you reading about U.S. history on a conspiracy theorist’s blog post? Taking facts for a presentation from an anonymous Twitter account?

If you can’t determine the validity of the sources you’re using, it can compromise all of your research. That doesn’t mean just disregard anything on the internet but double-check your findings. In fact, quadruple-check. You can make your research even stronger by turning to references outside of the internet.

Examples of reliable information sources include: Published books Encyclopedias Magazines Databases Scholarly journals Newspapers Library catalogs

Finding information on the internet. While it can be beneficial to consulate alternative sources, strong internet research skills drive modern-day research.

One of the great things about the internet is how much information it contains, however, this comes with digging through a lot of garbage to get to the facts you need. The ability to efficiently use the vast database of knowledge that is on the internet without getting lost in the junk is very valuable to employers.

Internet research skills include: Source checking Searching relevant questions Exploring deeper than the first options Avoiding distraction Giving credit Organizing findings

Interviewing. Some research endeavors may require a more hands-on approach than just consulting internet sources. Being prepared with strong interviewing skills can be very helpful in the research process.

Interviews can be a useful research tactic to gain first-hand information and being able to manage a successful interview can greatly improve your research skills.

Interviewing skills involves: A plan of action Specific, pointed questions Respectfulness Considering the interview setting Actively Listening Taking notes Gratitude for participation

Report writing. Possessing skills in report writing can assist you in job and scholarly research. The overall purpose of a report in any context is to convey particular information to its audience.

Effective report writing is largely dependent on communication. Your boss, professor , or general reader should walk away completely understanding your findings and conclusions.

Report writing skills involve: Proper format Including a summary Focusing on your initial goal Creating an outline Proofreading Directness

Critical thinking. Critical thinking skills can aid you greatly throughout the research process, and as an employee in general. Critical thinking refers to your data analysis skills. When you’re in the throes of research, you need to be able to analyze your results and make logical decisions about your findings.

Critical thinking skills involve: Observation Analysis Assessing issues Problem-solving Creativity Communication

Planning and scheduling. Research is a work project like any other, and that means it requires a little forethought before starting. Creating a detailed outline map for the points you want to touch on in your research produces more organized results.

It also makes it much easier to manage your time. Planning and scheduling skills are important to employers because they indicate a prepared employee.

Planning and scheduling skills include: Setting objectives Identifying tasks Prioritizing Delegating if needed Vision Communication Clarity Time-management

Note-taking. Research involves sifting through and taking in lots of information. Taking exhaustive notes ensures that you will not neglect any findings later and allows you to communicate these results to your co-workers. Being able to take good notes helps summarize research.

Examples of note-taking skills include: Focus Organization Using short-hand Keeping your objective in mind Neatness Highlighting important points Reviewing notes afterward

Communication skills. Effective research requires being able to understand and process the information you receive, either written or spoken. That means that you need strong reading comprehension and writing skills — two major aspects of communication — as well as excellent listening skills.

Most research also involves showcasing your findings. This can be via a presentation. , report, chart, or Q&A. Whatever the case, you need to be able to communicate your findings in a way that educates your audience.

Communication skills include: Reading comprehension Writing Listening skills Presenting to an audience Creating graphs or charts Explaining in layman’s terms

Time management. We’re, unfortunately, only given 24 measly hours in a day. The ability to effectively manage this time is extremely powerful in a professional context. Hiring managers seek candidates who can accomplish goals in a given timeframe.

Strong time management skills mean that you can organize a plan for how to break down larger tasks in a project and complete them by a deadline. Developing your time management skills can greatly improve the productivity of your research.

Time management skills include: Scheduling Creating task outlines Strategic thinking Stress-management Delegation Communication Utilizing resources Setting realistic expectations Meeting deadlines

Using your network. While this doesn’t seem immediately relevant to research skills, remember that there are a lot of experts out there. Knowing what people’s areas of expertise and asking for help can be tremendously beneficial — especially if it’s a subject you’re unfamiliar with.

Your coworkers are going to have different areas of expertise than you do, and your network of people will as well. You may even know someone who knows someone who’s knowledgeable in the area you’re researching. Most people are happy to share their expertise, as it’s usually also an area of interest to them.

Networking involves: Remembering people’s areas of expertise Being willing to ask for help Communication Returning favors Making use of advice Asking for specific assistance

Attention to detail. Research is inherently precise. That means that you need to be attentive to the details, both in terms of the information you’re gathering, but also in where you got it from. Making errors in statistics can have a major impact on the interpretation of the data, not to mention that it’ll reflect poorly on you.

There are proper procedures for citing sources that you should follow. That means that your sources will be properly credited, preventing accusations of plagiarism. In addition, it means that others can make use of your research by returning to the original sources.

Attention to detail includes: Double checking statistics Taking notes Keeping track of your sources Staying organized Making sure graphs are accurate and representative Properly citing sources

As with many professional skills, research skills serve us in our day to day life. Any time you search for information on the internet, you’re doing research. That means that you’re practicing it outside of work as well. If you want to continue improving your research skills, both for professional and personal use, here are some tips to try.

Differentiate between source quality. A researcher is only as good as their worst source. Start paying attention to the quality of the sources you use, and be suspicious of everything your read until you check out the attributions and works cited.

Be critical and ask yourself about the author’s bias, where the author’s research aligns with the larger body of verified research in the field, and what publication sponsored or published the research.

Use multiple resources. When you can verify information from a multitude of sources, it becomes more and more credible. To bolster your faith in one source, see if you can find another source that agrees with it.

Don’t fall victim to confirmation bias. Confirmation bias is when a researcher expects a certain outcome and then goes to find data that supports this hypothesis. It can even go so far as disregarding anything that challenges the researcher’s initial hunch. Be prepared for surprising answers and keep an open mind.

Be open to the idea that you might not find a definitive answer. It’s best to be honest and say that you found no definitive answer instead of just confirming what you think your boss or coworkers expect or want to hear. Experts and good researchers are willing to say that they don’t know.

Stay organized. Being able to cite sources accurately and present all your findings is just as important as conducting the research itself. Start practicing good organizational skills , both on your devices and for any physical products you’re using.

Get specific as you go. There’s nothing wrong with starting your research in a general way. After all, it’s important to become familiar with the terminology and basic gist of the researcher’s findings before you dig down into all the minutia.

A job interview is itself a test of your research skills. You can expect questions on what you know about the company, the role, and your field or industry more generally. In order to give expert answers on all these topics, research is crucial.

Start by researching the company . Look into how they communicate with the public through social media, what their mission statement is, and how they describe their culture.

Pay close attention to the tone of their website. Is it hyper professional or more casual and fun-loving? All of these elements will help decide how best to sell yourself at the interview.

Next, research the role. Go beyond the job description and reach out to current employees working at your desired company and in your potential department. If you can find out what specific problems your future team is or will be facing, you’re sure to impress hiring managers and recruiters with your ability to research all the facts.

Finally, take time to research the job responsibilities you’re not as comfortable with. If you’re applying for a job that represents increased difficulty or entirely new tasks, it helps to come into the interview with at least a basic knowledge of what you’ll need to learn.

Research projects require dedication. Being committed is a valuable skill for hiring managers. Whether you’ve had research experience throughout education or a former job, including it properly can boost the success of your resume .

Consider how extensive your research background is. If you’ve worked on multiple, in-depth research projects, it might be best to include it as its own section. If you have less research experience, include it in the skills section .

Focus on your specific role in the research, as opposed to just the research itself. Try to quantify accomplishments to the best of your abilities. If you were put in charge of competitor research, for example, list that as one of the tasks you had in your career.

If it was a particular project, such as tracking the sale of women’s clothing at a tee-shirt company, you can say that you “directed analysis into women’s clothing sales statistics for a market research project.”

Ascertain how directly research skills relate to the job you’re applying for. How strongly you highlight your research skills should depend on the nature of the job the resume is for. If research looks to be a strong component of it, then showcase all of your experience.

If research looks to be tangential, then be sure to mention it — it’s a valuable skill — but don’t put it front and center.

Example #1: Academic Research

Simon Marks 767 Brighton Blvd. | Brooklyn, NY, 27368 | (683)-262-8883 | [email protected] Diligent and hardworking recent graduate seeking a position to develop professional experience and utilize research skills. B.A. in Biological Sciences from New York University. PROFESSIONAL EXPERIENCE Lixus Publishing , Brooklyn, NY Office Assistant- September 2018-present Scheduling and updating meetings Managing emails and phone calls Reading entries Worked on a science fiction campaign by researching target demographic Organizing calendars Promoted to office assistant after one year internship Mitch’s Burgers and Fries , Brooklyn, NY Restaurant Manager , June 2014-June 2018 Managed a team of five employees Responsible for coordinating the weekly schedule Hired and trained two employees Kept track of inventory Dealt with vendors Provided customer service Promoted to restaurant manager after two years as a waiter Awarded a $2.00/hr wage increase SKILLS Writing Scientific Research Data analysis Critical thinking Planning Communication RESEARCH Worked on an ecosystem biology project with responsibilities for algae collection and research (2019) Lead a group of freshmen in a research project looking into cell biology (2018) EDUCATION New York University Bachelors in Biological Sciences, September 2016-May 2020

Example #2: Professional Research

Angela Nichols 1111 Keller Dr. | San Francisco, CA | (663)-124-8827 |[email protected] Experienced and enthusiastic marketer with 7 years of professional experience. Seeking a position to apply my marketing and research knowledge. Skills in working on a team and flexibility. EXPERIENCE Apples amp; Oranges Marketing, San Francisco, CA Associate Marketer – April 2017-May 2020 Discuss marketing goals with clients Provide customer service Lead campaigns associated with women’s health Coordinating with a marketing team Quickly solving issues in service and managing conflict Awarded with two raises totaling $10,000 over three years Prestigious Marketing Company, San Francisco, CA Marketer – May 2014-April 2017 Working directly with clients Conducting market research into television streaming preferences Developing marketing campaigns related to television streaming services Report writing Analyzing campaign success statistics Promoted to Marketer from Junior Marketer after the first year Timberlake Public Relations, San Francisco, CA Public Relations Intern – September 2013–May 2014 Working cohesively with a large group of co-workers and supervisors Note-taking during meetings Running errands Managing email accounts Assisting in brainstorming Meeting work deadlines EDUCATION Golden Gate University, San Francisco, CA Bachelor of Arts in Marketing with a minor in Communications – September 2009 – May 2013 SKILLS Marketing Market research Record-keeping Teamwork Presentation. Flexibility

What research skills are important?

Goal-setting and data collection are important research skills. Additional important research skills include:

Using different sources to analyze information.

Finding information on the internet.

Interviewing sources.

Writing reports.

Critical thinking.

Planning and scheduling.

Note-taking.

Managing time.

How do you develop good research skills?

You develop good research skills by learning how to find information from multiple high-quality sources, by being wary of confirmation bias, and by starting broad and getting more specific as you go.

When you learn how to tell a reliable source from an unreliable one and get in the habit of finding multiple sources that back up a claim, you’ll have better quality research.

In addition, when you learn how to keep an open mind about what you’ll find, you’ll avoid falling into the trap of confirmation bias, and by staying organized and narrowing your focus as you go (rather than before you start), you’ll be able to gather quality information more efficiently.

What is the importance of research?

The importance of research is that it informs most decisions and strategies in a business. Whether it’s deciding which products to offer or creating a marketing strategy, research should be used in every part of a company.

Because of this, employers want employees who have strong research skills. They know that you’ll be able to put them to work bettering yourself and the organization as a whole.

Should you put research skills on your resume?

Yes, you should include research skills on your resume as they are an important professional skill. Where you include your research skills on your resume will depend on whether you have a lot of experience in research from a previous job or as part of getting your degree, or if you’ve just cultivated them on your own.

If your research skills are based on experience, you could put them down under the tasks you were expected to perform at the job in question. If not, then you should likely list it in your skills section.

University of the People – The Best Research Skills for Success

Association of Internet Research Specialists — What are Research Skills and Why Are They Important?

MasterClass — How to Improve Your Research Skills: 6 Research Tips

How useful was this post?

Click on a star to rate it!

Average rating / 5. Vote count:

No votes so far! Be the first to rate this post.

Sky Ariella is a professional freelance writer, originally from New York. She has been featured on websites and online magazines covering topics in career, travel, and lifestyle. She received her BA in psychology from Hunter College.

Recent Job Searches

• Registered Nurse Jobs Resume Location
• Truck Driver Jobs Resume Location
• Call Center Representative Jobs Resume Location
• Customer Service Representative Jobs Resume
• Delivery Driver Jobs Resume Location
• Warehouse Worker Jobs Resume Location
• Account Executive Jobs Resume Location
• Sales Associate Jobs Resume Location
• Licensed Practical Nurse Jobs Resume Location
• Company Driver Jobs Resume

Related posts

Creative Thinking Skills (Definitions, Examples, And How To Improve)

A Complete Guide To Situational Analysis (With Examples)

What Are Hybrid Skills? (With Examples)

How To Delegate And Get More Done (With Examples)

• Career Advice >
• Hard Skills >
• Research Skills

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Research Skills: What they are and Benefits

Research skills play a vital role in the success of any research project, enabling individuals to navigate the vast sea of information, analyze data critically, and draw meaningful conclusions. Whether conducting academic research, professional investigations, or personal inquiries, strong research skills are essential for obtaining accurate and reliable results.

LEARN ABOUT:   Research Process Steps

By understanding and developing these skills, individuals can embark on their research endeavors with confidence, integrity, and the capability to make meaningful contributions in their chosen fields. This article will explore the importance of research skills and discuss critical competencies necessary for conducting a research project effectively.

Content Index

What are Research Skills?

Important research skills for research project, benefits of research skills.

• Improving your Research Skills

Talk to Experts to Improve Skills

Research skills are the capability a person carries to create new concepts and understand the use of data collection. These skills include techniques, documentation, and interpretation of the collected data. Research is conducted to evaluate hypotheses and share the findings most appropriately. Research skills improve as we gain experience.

To conduct efficient research, specific research skills are essential. These skills are necessary for companies to develop new products and services or enhance existing products. To develop good research skills is important for both the individual as well as the company.

When undertaking a research project, one must possess specific important skills to ensure the project’s success and accuracy. Here are some essential research skills that are crucial for conducting a project effectively:

Time Management Skills:

Time management is an essential research skill; it helps you break down your project into parts and enables you to manage it easier. One can create a dead-line oriented plan for the research project and assign time for each task. Time management skills include setting goals for the project, planning and organizing functions as per their priority, and efficiently delegating these tasks.

Communication Skills:

These skills help you understand and receive important information and also allow you to share your findings with others in an effective manner. Active listening and speaking are critical skills for solid communication. A researcher must have good communication skills.

Problem-Solving:  

The ability to handle complex situations and business challenges and come up with solutions for them is termed problem-solving. To problem-solve, you should be able to fully understand the extent of the problem and then break it down into smaller parts. Once segregated into smaller chunks, you can start thinking about each element and analyze it to find a solution.

Information gathering and attention to detail:

Relevant information is the key to good research design . Searching for credible resources and collecting information from there will help you strengthen your research proposal and drive you to solutions faster. Once you have access to information, paying close attention to all the details and drawing conclusions based on the findings is essential.

Research Design and Methodology :

Understanding research design and methodology is essential for planning and conducting a project. Depending on the research question and objectives, researchers must select appropriate research methods, such as surveys, experiments, interviews, or case studies. Proficiency in designing research protocols, data collection instruments, and sampling strategies is crucial for obtaining reliable and valid results.

Data Collection and Analysis :

Researchers should be skilled in collecting and analyzing data accurately. It involves designing data collection instruments, collecting data through various methods, such as surveys or observations, and organizing and analyzing the collected data using appropriate statistical or qualitative analysis techniques. Proficiency in using software tools like SPSS, Excel, or qualitative analysis software can be beneficial.

By developing and strengthening these research skills, researchers can enhance the quality and impact of their research process, contributing to good research skills in their respective fields.

Research skills are invaluable assets that can benefit individuals in various aspects of their lives. Here are some key benefits of developing and honing research skills:

Boosts Curiosity :

Curiosity is a strong desire to know things and a powerful learning driver. Curious researchers will naturally ask questions that demand answers and will stop in the search for answers. Interested people are better listeners and are open to listening to other people’s ideas and perspectives, not just their own.

Cultivates Self-awareness :

As well as being aware of other people’s subjective opinions, one must develop the importance of research skills and be mindful of the benefits of awareness research; we are exposed to many things while researching. Once we start doing research, the benefit from it reflects on the beliefs and attitudes and encourages them to open their minds to other perspectives and ways of looking at things.

Effective Communication:

Research skills contribute to practical communication skills by enhancing one’s ability to articulate ideas, opinions, and findings clearly and coherently. Through research, individuals learn to organize their thoughts, present evidence-based arguments, and effectively convey complex information to different audiences. These skills are crucial in academic research settings, professional environments, and personal interactions.

Personal and Professional Growth :

Developing research skills fosters personal and professional growth by instilling a sense of curiosity, intellectual independence, and a lifelong learning mindset. Research encourages individuals to seek knowledge, challenge assumptions, and embrace intellectual growth. These skills also enhance adaptability as individuals become adept at navigating and assimilating new information, staying updated with the latest developments, and adjusting their perspectives and strategies accordingly.

Academic Success:

Research skills are essential for academic research success. They enable students to conduct thorough literature reviews, gather evidence to support their arguments, and critically evaluate existing research. By honing their research skills, students can produce well-structured, evidence-based essays, projects, and dissertations demonstrating high academic research rigor and analytical thinking.

Professional Advancement:

Research skills are highly valued in the professional world. They are crucial for conducting market research, analyzing trends, identifying opportunities, and making data-driven decisions. Employers appreciate individuals who can effectively gather and analyze information, solve complex problems, and provide evidence-based recommendations. Research skills also enable professionals to stay updated with advancements in their field, positioning themselves as knowledgeable and competent experts.

Developing and nurturing research skills can significantly benefit individuals in numerous aspects of their lives, enabling them to thrive in an increasingly information-driven world.

Improving Your Research Skills

There are many things you can do to improve your research skills and utilize them in your research or day job. Here are some examples:

• Develop Information Literacy: Strengthening your information literacy skills is crucial for conducting thorough research. It involves identifying reliable sources, evaluating the credibility of information, and navigating different research databases.
• Enhance Critical Thinking: Critical thinking is an essential skill for effective research. It involves analyzing information, questioning assumptions, and evaluating arguments. Practice critical analysis by analyzing thoughtfully, identifying biases, and considering alternative perspectives.
• Master Research Methodologies: Familiarize yourself with different research methodologies relevant to your field. Whether it’s qualitative, quantitative, or mixed methods research, realizing the strengths and limitations of each approach is crucial.
• Practice Effective Time Management: Research requires dedicated time and effort. Develop good time management skills to ensure that you allocate sufficient time for each stage of the research process, including planning, data collection, analysis, and writing.
• Embrace Collaboration: Collaborating with peers and colleagues can provide a fresh perspective and enrich your research experience. Engage in discussions, share ideas, and seek feedback from others. Collaborative projects allow for exchanging knowledge and skills.
• Continuously Update Your Knowledge: Stay informed about your field’s latest developments and advancements. Regularly read scholarly articles, attend conferences, and follow reputable sources of information to stay up to date with current research trends.

There is plenty of information available on the internet about every topic; hence, learning skills to know which information is relevant and credible is very important. Today most search engines have the feature of advanced search, and you can customize the search as per your preference. Once you learn this skill, it will help you find information. 

Experts possess a wealth of knowledge, experience, and insights that can significantly enhance your understanding and abilities in conducting research. Experts have often encountered numerous challenges and hurdles throughout their research journey and have developed effective problem-solving techniques. Engaging with experts is a highly effective approach to improving research skills.

Moreover, experts can provide valuable feedback and constructive criticism on your research work. They can offer fresh perspectives, identify areas for improvement, and help you refine your research questions, methodology, and analysis.

At QuestionPro, we can help you with the necessary tools to carry out your projects, and we have created the following free resources to help you in your professional growth:

• Survey Templates

Research skills are invaluable assets that empower individuals to navigate the ever-expanding realm of information, make informed decisions, and contribute to advancing knowledge. With advanced research tools and technologies like QuestionPro Survey Software, researchers have potent resources to conduct comprehensive surveys, gather data, and analyze results efficiently.

Where data-driven decision-making is crucial, research skills supported by advanced tools like QuestionPro are essential for researchers to stay ahead and make impactful contributions to their fields. By embracing these research skills and leveraging the capabilities of powerful survey software, researchers can unlock new possibilities, gain deeper insights, and pave the way for meaningful discoveries.

Authors : Gargi Ghamandi & Sandeep Kokane

FREE TRIAL         LEARN MORE

MORE LIKE THIS

Data Information vs Insight: Essential differences

May 14, 2024

Pricing Analytics Software: Optimize Your Pricing Strategy

May 13, 2024

Relationship Marketing: What It Is, Examples & Top 7 Benefits

May 8, 2024

The Best Email Survey Tool to Boost Your Feedback Game

May 7, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Empowering students to develop research skills

February 8, 2021

This post is republished from   Into Practice ,  a biweekly communication of Harvard’s  Office of the Vice Provost for Advances in Learning

Terence D. Capellini, Richard B Wolf Associate Professor of Human Evolutionary Biology, empowers students to grow as researchers in his Building the Human Body course through a comprehensive, course-long collaborative project that works to understand the changes in the genome that make the human skeleton unique. For instance, of the many types of projects, some focus on the genetic basis of why human beings walk on two legs. This integrative “Evo-Devo” project demands high levels of understanding of biology and genetics that students gain in the first half of class, which is then applied hands-on in the second half of class. Students work in teams of 2-3 to collect their own morphology data by measuring skeletons at the Harvard Museum of Natural History and leverage statistics to understand patterns in their data. They then collect and analyze DNA sequences from humans and other animals to identify the DNA changes that may encode morphology. Throughout this course, students go from sometimes having “limited experience in genetics and/or morphology” to conducting their own independent research. This project culminates in a team presentation and a final research paper.

The benefits: Students develop the methodological skills required to collect and analyze morphological data. Using the UCSC Genome browser  and other tools, students sharpen their analytical skills to visualize genomics data and pinpoint meaningful genetic changes. Conducting this work in teams means students develop collaborative skills that model academic biology labs outside class, and some student projects have contributed to published papers in the field. “Every year, I have one student, if not two, join my lab to work on projects developed from class to try to get them published.”

“The beauty of this class is that the students are asking a question that’s never been asked before and they’re actually collecting data to get at an answer.”

The challenges:  Capellini observes that the most common challenge faced by students in the course is when “they have a really terrific question they want to explore, but the necessary background information is simply lacking. It is simply amazing how little we do know about human development, despite its hundreds of years of study.” Sometimes, for instance, students want to learn about the evolution, development, and genetics of a certain body part, but it is still somewhat a mystery to the field. In these cases, the teaching team (including co-instructor Dr. Neil Roach) tries to find datasets that are maximally relevant to the questions the students want to explore. Capellini also notes that the work in his class is demanding and hard, just by the nature of the work, but students “always step up and perform” and the teaching team does their best to “make it fun” and ensure they nurture students’ curiosities and questions.

Takeaways and best practices

• Incorporate previous students’ work into the course. Capellini intentionally discusses findings from previous student groups in lectures. “They’re developing real findings and we share that when we explain the project for the next groups.” Capellini also invites students to share their own progress and findings as part of class discussion, which helps them participate as independent researchers and receive feedback from their peers.
• Assign groups intentionally.  Maintaining flexibility allows the teaching team to be more responsive to students’ various needs and interests. Capellini will often place graduate students by themselves to enhance their workload and give them training directly relevant to their future thesis work. Undergraduates are able to self-select into groups or can be assigned based on shared interests. “If two people are enthusiastic about examining the knee, for instance, we’ll match them together.”
• Consider using multiple types of assessments.  Capellini notes that exams and quizzes are administered in the first half of the course and scaffolded so that students can practice the skills they need to successfully apply course material in the final project. “Lots of the initial examples are hypothetical,” he explains, even grounded in fiction and pop culture references, “but [students] have to eventually apply the skills they learned in addressing the hypothetical example to their own real example and the data they generate” for the Evo-Devo project. This is coupled with a paper and a presentation treated like a conference talk.

Bottom line:  Capellini’s top advice for professors looking to help their own students grow as researchers is to ensure research projects are designed with intentionality and fully integrated into the syllabus. “You can’t simply tack it on at the end,” he underscores. “If you want this research project to be a substantive learning opportunity, it has to happen from Day 1.” That includes carving out time in class for students to work on it and make the connections they need to conduct research. “Listen to your students and learn about them personally” so you can tap into what they’re excited about. Have some fun in the course, and they’ll be motivated to do the work.

What are Research Skills? How to Improve Your Skills in Research

Learn strategies and techniques to improve your research skills. Avoid common mistakes and implement proven methods for efficient research. This article offers practical tips to enhance your ability to find and evaluate high-quality information.

Are you struggling to find relevant and reliable information for your research? Do you want to avoid getting lost in a sea of sources and needing help knowing where to start? Improving your research skills is essential for academic success and professional growth.

In today's information age, effectively conducting research has become more important than ever. Whether you are a student, a professional, or simply someone who wants to stay informed, knowing how to find and evaluate information is crucial.

Fortunately, some strategies and techniques can help you improve your research skills and become a more efficient and effective researcher. By avoiding common mistakes and implementing proven methods, you can enhance your ability to find high-quality information and make the most of your research endeavors. This article will explore some practical tips and tricks to help you improve your research skills and achieve better results.

What is Research?

Research is a critical part of learning, problem-solving, and decision-making. It is an essential process used in every field for both the individual and collective’s mutual benefit and success. Research involves systematically gathering data from primary or secondary sources, analyzing it, interpreting it, and communicating its findings to researchers and other interested parties.

Research can be divided into two main categories: quantitative research, which uses numerical data to describe phenomena, and qualitative research, which seeks to understand people's beliefs, opinions, values, or behaviors. Quantitative research often involves applying model-based approaches that can predict outcomes based on observations. It is one of the most powerful methods of discovering information about the world, as it allows for testing hypotheses in a systematic manner. Qualitative research is more exploratory in nature by focusing on understanding the motivations behind what people do or think rather than developing models or producing statistics in order to conclude behavior and relationships between variables. This type of research usually relies more on observation and engagement with people instead of using statistical models.

What are Research Skills?

Research skills are the abilities and talents required to focus on an objective, gather the relevant data linked to it, analyze it using appropriate methods, and accurately communicate the results. Taking part in research indicates that you have acquired knowledge of your subject matter, have digested that knowledge, and processed, evaluated, and analyzed it until you can resolve a problem or answer a query. It is highly beneficial for employers to hire people with strong research skills since they can provide valuable insights and add value to the company’s performance. Therefore, researching effectively has become crucial to securing a job in most industries.

Why Do Research Skills Matter?

Research skills are essential if one intends to succeed in today's competitive world. With technology ever-evolving and a need to stay ahead of the competition, employees who possess research skills can prove invaluable to their employers. These skills include researching, analyzing, and interpreting data and making informed decisions based on that information.

Employers value workers who can quickly develop a thorough understanding of any changes or trends in their field of work through accurate research. Knowing how to assess customer needs, recognize competition, write reports, improve productivity, and advise on investments can also benefit any business. With the help of research skills, companies can uncover ways to adapt their services or products that better serve their customers’ needs while helping them save money at the same time. This makes overall operations more efficient as well as helps a company remain ahead of its competitors.

Essential Research Skills :

Here is a list of essential research skills:

Data Collection

Data collection is an important part of comprehending a certain topic and ensuring reliable information is collected while striving to answer complex questions. Every situation differs, but data collection typically includes surveys, interviews, observations, and existing document reviews. The data collected can be quantitative or qualitative, depending on the nature of the problem at hand. As students advance through university and other educational institutions, they will need to read extensively into a particular field and may even need to undertake comprehensive literature reviews to answer fundamental questions.

The skills acquired through data collection during university are invaluable for future roles and jobs. Gaining experience in understanding complex topics, reading widely on a given subject matter, collecting relevant data, and analyzing findings - all these activities are integral when dealing with any type of project within the corporate sector. Therefore, embarking on various research projects enhances a person's education level and brings about significant professional experience.

Goal-Setting

Setting goals is an important skill for any successful research project. It allows you to stay focused and motivated throughout the process. Goals are also essential in helping with direction: they provide a path to organize our thoughts, narrow our focus, and prioritize the tasks we need to undertake to achieve our desired result. The concept of goal-setting is inherent in most research processes, as everything needs to have something to strive for — whether that’s gaining knowledge about a particular topic or testing a theory.

When it comes to creating and setting goals during the research process, you must have clear and specific objectives in mind from the outset. Writing down your thoughts helps define these objectives, which can inform the data collection process; moreover, thinking about short-term and long-term goals can help you create manageable steps toward achieving them. Learning how to break up larger projects into smaller “mini-goals effectively” can make all the difference when tackling complex investigations — allowing researchers to monitor their progress more easily and culminate results further down the line.

Critical Thinking

Critical thinking is an integral part of the modern workplace. To succeed, one must be able to look at a situation objectively and make decisions based on evidence. The information examined needs to come from various sources, such as data collection, personal observation, or analysis. The goal should then be to take all this information and form a logical judgment that informs an action plan or idea.

Someone who displays strong critical thinking skills will not just accept proposed ideas at face value but instead can understand how these ideas can be applied and challenged. Accepting something without consideration means making the wrong decision due to a lack of thought. Critical thinkers understand how brainstorming works, assessing all elements before forming any decision. From negotiating with colleagues or customers in adversarial scenarios to analyzing complex documents such as legal contracts in order to review business agreements - critical dedicated apply their knowledge effectively and are able to back up their evaluation with evidence collected from multiple sources.

Observation Skills

Observation skills are necessary for conducting any form of research, whether it be in the workplace or as part of an investigative process. It is important to be able to pick up on the details that might otherwise pass unnoticed, such as inconsistencies in data or irregularities in how something is presented, and to pay careful attention to regulations and procedures that govern the company or environment. This can help researchers to ensure their processes are accurate and reliable.

As well as analyzing what we see around us directly, many research methodologies often involve calculated statistical analyses and calculations. For this reason, it’s important to develop strong observation skills so that the legitimacy of information can be confirmed and checked before conclusions are formed. Improving this skill requires dedication and practice, which could include keeping a journal reflecting on experiences, posing yourself questions about what you have observed, and seeking out opportunities in unfamiliar settings to test your observations.

Detail Orientation

Detail orientation is an important research skill for any scientific endeavor. It allows one to assess a situation or problem in minute detail and make appropriate judgments based on the information gathered. A detail-oriented thinker can easily spot errors, inconsistencies, and vital pieces of evidence, which can help lead to accurate conclusions from the research. Additionally, this skill allows someone to evaluate the quality and accuracy of data recorded during an experiment or project more efficiently to ensure validity.

Spotting small mistakes that may otherwise have been overlooked is a crucial part of conducting detailed research that must be perfected. Individuals aiming for superior outcomes should strive to develop their skill at detecting details by practicing critical analysis techniques, such as breaking down large bodies of information into smaller tasks to identify finer points quickly. Moreover, encouragement should also be made for elaborate comparison and analysis between different pieces of information when solving a complex problem, as it can help provide better insights into problems accurately.

Investigative Skills

Investigative skills are an essential component when it comes to gathering and analyzing data. In a professional setting, it is important to determine the accuracy and validity of different sources of information before making any decisions or articulating ideas. Generally, effective investigation requires collecting different sets of reliable data, such as surveys and interviews with stakeholders, employees, customers, etc. For example, if a company internally assesses possible challenges within its business operations environment, it would need to conduct more profound research involving talking to relevant stakeholders who could provide critical perspectives about the situation.

Data-gathering techniques such as comparison shopping and regulatory reviews have become more commonplace in the industry as people strive for greater transparency and more accurate results. Knowing how to identify reliable sources of information can give individuals a competitive advantage and allow them to make sound decisions based on accurate data. Investing time in learning different investigative skills can help recruiters spot applicants dedicated to acquiring knowledge in this field. Developing these investigative skills is also valuable for those looking for executive positions or starting their own business. By familiarizing themselves with their application process, people can become adept at collecting high-quality data they may use in their research endeavors.

Time Management

Time management is a key skill for any researcher. It's essential to be able to allocate time between different activities so you can effectively plan and structure your research projects. Without good time management, you may find yourself hastily completing tasks or feeling stressed out as you rush to complete an analysis. Ultimately, managing your time allows you to stay productive and ensure that each project is completed with the highest results.

Good time management requires various skills such as planning ahead, prioritizing tasks, breaking down large projects into smaller steps, and even delegating some activities when possible. It also means setting realistic goals for yourself in terms of the amount of research that can be achieved in certain timestamps and learning how to adjust these goals when needed. Becoming mindful of how you spend the same hours each day will propel your productivity and see positive results from your efforts. Time management becomes especially relevant regarding data collection and analysis – it is crucial to understand precisely what kind of resources are needed for each task before diving into the research itself. Knowing how much time should be dedicated to each step is essential for meeting deadlines while still retaining accuracy in the final outcomes of one’s study.

Tips on How to Improve Your Research Skills

Below are some tips that can help in improving your skills in research:

Initiate your project with a structured outline

When embarking on any research project, creating an outline and scope document must first ensure that you remain on the right track. An outline sets expectations for your project by forming a detailed strategy for researching the topic and gathering the necessary data to conclude. It will help you stay organized and break down large projects into more manageable parts. This can help prevent procrastination as each part of the project has its own timeline, making it easier to prioritize tasks accordingly.

Using an outline and scope document also allows for better structure when conducting research or interviews, as it guides which sources are most relevant, what questions need to be answered, and how information should be collected or presented. This ensures that all information received through research or interviews stays within the confines of the chosen topic of investigation. Additionally, it ensures that no important details are overlooked while minimizing the chance that extraneous information gets included in your results. Taking this time upfront prevents potential problems during analysis or reporting of findings later.

Acquire expertise in advanced data collection methods

When it comes to collecting data for research purposes, a range of advanced data collection techniques can be used to maximize your efficiency and accuracy. One such technique is customizing your online search results with advanced search settings. By adding quotation marks and wildcard characters to the terms you are searching for, you are more likely to find the information you need from reliable sources. This can be especially useful if, for instance, you are looking for exact quotes or phrases. Different search engines require different advanced techniques and tactics, so learning these can help you get more specific results from your research endeavors.

Aside from using online searches, another standard methodology when conducting research is accessing primary information through libraries or other public sources. A specific classification system will likely be in place that can help researchers locate the materials needed quickly and easily. Knowing and understanding this system allows one to access information much more efficiently while also giving them ample opportunity to increase their knowledge of various topics by browsing related content in the same category groups. Thus, by learning about advanced data collection techniques for both online and offline sources, researchers can make substantial progress in their studies more efficiently.

Validate and examine the reliability of your data sources

Collecting reliable information for research can be a challenge, especially when relying on online sources. It is essential to remember that not all sources are created equal, and some sites may contain false or inaccurate data. It is, therefore important to verify and analyze the data before using it as part of your research.

One way to start verifying and analyzing your sources is to cross-reference material from one source with another. This may help you determine if particular facts or claims are accurate and, therefore, more valid than others. Additionally, trace where the data is coming from by looking at the author or organization behind it so that you can assess their expertise in a particular field and authority on the topic at hand. Once these steps have been completed, you can confidently use this trusted information for your project.

Structure your research materials

Organizing your research materials is an integral part of any research process. When you’re conducting a project or study and trying to find the most relevant information, you can become overwhelmed with all the data available. It’s important to separate valid from invalid materials and to categorize research materials by subject for easy access later on. Bookmarking websites on a computer or using a digital asset management tool are two effective methods for organizing research information.

When researching, it’s critical to remember that some sources have limited value and may be outside the scope of your topic. Recognizing reliable material versus trustworthy resources can be complex in this sea of information. However, sorting data into appropriate categories can help narrow down what is necessary for producing valid conclusions. This method of classifying information helps ensure that vital documents aren't overlooked during the organization process as they are placed in folders shortcutted for quick access within one centralized source whenever needed. Separating valuable sources also makes it easier to reference later on when writing reports or giving presentations - material won't get lost among irrelevant data, and conclusions will be backed by sound evidence.

Enhance your research and communication capabilities

Developing research and communication skills is essential for succeeding academically and professionally in the modern world. The key to improving these skills lies in rigorous practice, which can begin with small projects such as resolving common issues or completing a research task that can be made into a personal project. One way to do this is to volunteer for research projects at work and gain experience under the guidance of experienced researchers. This will improve your research skills and help you develop communication skills when working with others on the project. Another option is to turn a personal project into a research task. For example, if you plan on taking a holiday soon, you could create an objective method to select the best destination by conducting online research on destinations and making informed decisions based on thorough analysis. Practicing in this way enables you to complete any research task confidently and communicate efficiently with ease.

How to Articulate Research Skills on Your Resume

Research projects require commitment and perseverance, making it an important skill to include on a resume. Even if you have had limited research experience throughout your education or previous job, including this in your resume assesses these qualities to potential employers. It's important to consider the extent of your research experience when deciding how to add this part of your background to your resume. If you have been involved with multiple in-depth research projects, it might be best to highlight this by including it as its own section. On the other hand, if the amount of research you have completed is more limited, then try including it in the skills section instead.

When adding research experience and accomplishments into either section of your resume, be sure to emphasize any specific roles or contributions you made during the process instead of just describing the project itself. Furthermore, remember to quantify any successes where possible - this showcases both communication and technical proficiency strengths, which can help make your resume stand out even more. By properly articulating research skills within a resume, employers will likely be more interested in what job seekers have accomplished in their careers.

How to Apply Research Skills Effectively in Your Workplace

Research skills are an invaluable set of abilities to bring to your workplace. To make sure you use them properly, a good place to start is by taking time to plan the project you have been assigned. Whether it’s writing a report or analyzing data, mapping out what tasks you need to do and how long they should take helps to understand the project timeline better. This also makes setting aside dedicated time for research easier too.

To ensure that the decisions made are sound and informed, reading up on the subject area related to the project remains one of the premier ways of doing this. This will help to ensure that any problems arising can be solved quickly and effectively, as well as provide answers before any decisions are actually put into practice. By arming yourself with knowledge gathered through reading about a particular topic, it can give you more confidence when formulating plans or strategies in which direction to take your work in.

Final Thoughts

Research skills are increasingly important in the modern world, and gaining proficiency in this area can significantly benefit a person's career. Research skills are essential for success in many different roles and fields, including those within business and industry, education, science, and medicine. Developing a deep understanding of research allows us to identify problems better and critically evaluate potential solutions. It also bolsters our problem-solving abilities as we work to find creative solutions that meet our efforts' objectives.

By improving your research capabilities, you can impress employers during an application process or when joining a team at work. Research skills are considered soft skills by potential employers since they signal that you have attention to detail while simultaneously demonstrating your ability to learn new things quickly. Employers regard these skills highly, making them one of the key graduate career skills recruiters seek. Furthermore, being able to add ‘research skills’ to your CV will be looked upon favorably by employers and help drive up your employability significantly. Demonstrating that you possess these sought-after traits makes it easier for recruiters to give you the opportunity you've been looking for, so it's worth investing the time into developing these life-long learning tools today.

How do I resolve any dispute

Effective Job Search Strategies That Work To Land Your Dream Role

How to Choose Between Two Job Offers? Tips to Help You Decide the Right Opportunity

Introduction to research skills: Home

• Learning from lectures
• Managing your time
• Effective reading
• Evaluating Information
• Critical thinking
• Presentation skills
• Studying online
• Writing home
• Maths and Statistics Support
• Problem solving
• Maths skills by discipline
• Introduction to research skills
• Primary research
• Research methods
• Managing data
• Research ethics
• Citing and referencing
• Searching the literature
• What is academic integrity?
• Referencing software
• Integrity Officer/Panel
• Intellectual property and copyright
• Digital skills home

Research skills allow you to find information and use it effectively. It includes creating a strategy to gather facts and reach conclusions so that you can answer a question.

Starting your research

think about your topic – don’t be too vague or too specific (try mind mapping or keyword searching).

read broadly around your subject (don’t just use Google and Wikipedia). Think about a research question that is clearly structured and builds on literature already produced.

find information using the subject databases. View the Database Orientation Program to learn about databases and using search strategies to refine your search and limit results. View our library tutorial on planning your literature search and look at our library subject guides for resources on your specific topic.

Another good starting point for finding information is our library catalogue Library Search  which allows you to search across the library's electronic resources as well as major subject databases and indexes.

carry out a literature review . You may want to include journals, books, websites, grey literature or data and statistics for example. See the list of sources below for more information. Keep a record and organise your references and sources. If you are intending to carry out a systematic review then take a look at the systematic review page on our Research Support library guide.

evaluate your resources – use the CRAAP test (Currency, Relevancy, Authority, Accuracy, Purpose - watch the video, top right). 

reach considered conclusions and make recommendations where necessary.

Your research journey.

Why do I need research skills?

they enable you to locate appropriate information and evaluate it for quality and relevance

they allow you to make good use of information to resolve a problem

they give you the ability to synthesize and communicate your ideas in written and spoken formats

they foster critical thinking

they are highly transferable and can be adapted to many settings including the workplace

You can access more in depth information on areas such as primary research, literature reviews, research methods, and managing data, from the drop down headings under Research Skills on the Academic Skills home page. The related resources in the right-hand column of this page also contain useful supporting information.

• Conference proceedings
• Data & statistics
• Grey literature
• Official publications

Books are good for exploring new subject areas. They help define a topic and provide an in-depth account of a subject.

Scholarly books contain authoritative information including comprehensive accounts of research or scholarship and experts' views on themes and topics. Their bibliographies can lead readers to related books, articles and other sources. 

Details on the electronic books held by the University of Southampton can be found using the library catalogue .

Journals are quicker to publish than books and are often a good source of current information. They are useful when you require information to support an argument or original research written by subject experts.  The bibliographies at the end of journal articles should point you to other relevant research.

Academic journals go through a "peer-review" process. A peer-reviewed journal is one whose articles are checked by experts, so you can be more confident that the information they contain is reliable.

The Library's discovery service Library Search  is a good place to start when searching for journal articles and enables access to anything that is available electronically.

Newspapers enable you to follow current and historical events from multiple perspectives. They are an excellent record of political, social, cultural, and economic events and history.

Newspapers are popular rather than scholarly publications and their content needs to be treated with caution. For example, an account of a particular topic can be biased in favour of that newspaper’s political affiliation or point of view. Always double-check the data/statistics or any other piece of information that a newspaper has used to support an argument before you quote it in your own work.

The library subscribes to various resources which provide full-text access to both current and historical newspapers. Find out more about these on the Library's Newspaper Resources page.

Websites provide information about every topic imaginable, and many will be relevant to your studies.

Use websites with caution as anyone can publish on the Internet and therefore the quality of the information provided is variable. When you’re researching and come across a website you think might be useful, consider whether or not it provides information that is reliable and authoritative enough to use in your work.

Proceedings are collections of papers presented by researchers at academic conferences or symposia. They may be printed volumes or in electronic format.

You can use the information in conference proceedings with a high degree of confidence as the quality is ensured by having external experts read & review the papers before they are accepted in the proceedings.

Find the data and statistics you need, from economics to health, environment to oceanography - and everywhere between - http://library.soton.ac.uk/data .

Grey literature is the term given to non-traditional publications (material not published by mainstream publishers). For example - leaflets, reports, conference proceedings, government documents, preprints, theses, clinical trials, blogs, tweets, etc.. 

The majority of Grey literature is generally not peer-reviewed so it is very important to critically appraise any grey literature before using it.

Most aspects of life are touched by national governments, or by inter-governmental bodies such as the European Union or the United Nations.  Official publications are the documentary evidence of that interest. 

Our main printed collections and online services are for British and EU official publications, but we can give advice on accessing official publications from other places and organisations. Find out more from our web pages  http://library.soton.ac.uk/officialpublications .

Patents protect inventions - the owner can stop other people making, using or selling the item without their permission. This applies for a limited period and a separate application is needed for each country.

Patents can be useful since they contain full technical details on how an invention works. If you use an active patent outside of research - permission or a license is probably needed.

Related resources:

Checking for CRAAP - UMW New Media Archive

How to Develop a STRONG Research Question - Scribbr

Guide to dissertation and project writing - by University of Southampton (Enabling Services)

Guide to writing your dissertation - by the Royal Literary Fund  

Guidance on the Conduct of Narrative Synthesis in Systematic Reviews  - by ESRC Methods Programme

Guidelines for preparing a Research Proposal - by University of Southampton

Choosing good keywords - by the Open University

Developing a Research or Guided Question  - a self-guided tutorial produced by Arizona State University

Evaluating information - a 7 minute tutorial from the University of Southampton which covers thinking critically, and understanding how to find quality and reliable information.

Hints on conducting a literature review  - by the University of Toronto

Planning your literature search  - a short tutorial by the University of Southampton

Using Overleaf for scientific writing and publishing  -  a popular  LaTeX/Rich Text based online collaborative tool for students and researchers alike. It is designed to make the process of writing, editing, and producing scientific papers quicker and easier for authors. 

Systematic reviews  - by the University of Southampton. 

Create your own research proposal - by the University of Southampton

• Last Updated: Mar 13, 2024 3:14 PM
• URL: https://library.soton.ac.uk/sash/introduction-to-research-skills

Research Basics: an open academic research skills course

• Lesson 1: Using Library Tools
• Lesson 2: Smart searching
• Lesson 3: Managing information overload
• Assessment - Module 1
• Lesson 1: The ABCs of scholarly sources
• Lesson 2: Additional ways of identifying scholarly sources
• Lesson 3: Verifying online sources
• Assessment - Module 2
• Lesson 1: Creating citations
• Lesson 2: Citing and paraphrasing
• Lesson 3: Works cited, bibliographies, and notes
• Assessment - Module 3
• - For Librarians and Teachers -
• Acknowledgements
• Other free resources from JSTOR

JSTOR is a digital library for scholars, researchers, and students.

Learn more about JSTOR

Get Help with JSTOR

JSTOR Website & Technical Support

 Email:  [email protected]  Text:  (734)-887-7001  Call Toll Free in the U.S.:  (888)-388-3574  Call Local and International:  (734)-887-7001

Hours of operation:  Mon - Fri, 8:30 a.m. - 5:00 p.m. EDT (GMT -4:00)

Welcome to the ever-expanding universe of scholarly research!

There's a lot of digital content out there, and we want to help you get a handle on it. Where do you start? What do you do? How do you use it? Don’t worry, this course has you covered.

This introductory program was created by  JSTOR  to help you get familiar with basic research concepts needed for success in school. The course contains three modules, each made up of three short lessons and three sets of practice quizzes. The topics covered are subjects that will help you prepare for college-level research. Each module ends with an assessment to test your knowledge.

The JSTOR librarians who helped create the course hope you learn from the experience and feel ready to research when you’ve finished this program.  Select Module 1: Effective Searching to begin the course. Good luck!

• Next: Module 1: Effective searching >>
• Last Updated: Apr 24, 2024 6:38 AM
• URL: https://guides.jstor.org/researchbasics

JSTOR is part of ITHAKA , a not-for-profit organization helping the academic community use digital technologies to preserve the scholarly record and to advance research and teaching in sustainable ways.

©2000-2024 ITHAKA. All Rights Reserved. JSTOR®, the JSTOR logo, JPASS®, Artstor® and ITHAKA® are registered trademarks of ITHAKA.

JSTOR.org Terms and Conditions   Privacy Policy Cookie Policy Cookie settings Accessibility

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Fostering students’ motivation towards learning research skills: the role of autonomy, competence and relatedness support

Louise maddens.

1 Centre for Instructional Psychology and Technology, Faculty of Psychology and Educational Sciences, KU Leuven and KU Leuven Campus Kulak Kortrijk, Etienne Sabbelaan 51 – bus 7800, 8500 Kortrijk, Belgium

2 Itec, imec Research Group at KU Leuven, imec, Leuven, Belgium

3 Vives University of Applied Sciences, Kortrijk, Belgium

Fien Depaepe

Annelies raes.

In order to design learning environments that foster students’ research skills, one can draw on instructional design models for complex learning, such as the 4C/ID model (in: van Merriënboer and Kirschner, Ten steps to complex learning, Routledge, London, 2018). However, few attempts have been undertaken to foster students’ motivation towards learning complex skills in environments based on the 4C/ID model. This study explores the effects of providing autonomy, competence and relatedness support (in Deci and Ryan, Psychol Inquiry 11(4): 227–268, https://doi.org/10.1207/S15327965PLI1104_01, 2000) in a 4C/ID based online learning environment on upper secondary school behavioral sciences students’ cognitive and motivational outcomes. Students’ cognitive outcomes are measured by means of a research skills test consisting of short multiple choice and short answer items (in order to assess research skills in a broad way), and a research skills task in which students are asked to integrate their skills in writing a research proposal (in order to assess research skills in an integrative manner). Students’ motivational outcomes are measured by means of students’ autonomous and controlled motivation, and students’ amotivation. A pretest-intervention-posttest design was set up in order to compare 233 upper secondary school behavioral sciences students’ outcomes among (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. Both learning environments proved equally effective in improving students’ scores on the research skills test. Students in the need supportive condition scored higher on the research skills task compared to their peers in the baseline condition. Students’ autonomous and controlled motivation were not affected by the intervention. Although, unexpectedly, students’ amotivation increased in both conditions, students’ amotivation was lower in the need supportive condition compared to students in the baseline condition. Theoretical relationships were established between students’ need satisfaction, students’ motivation (autonomous, controlled, and amotivation), and students’ cognitive outcomes. These findings are discussed taking into account the COVID-19 affected setting in which the study took place.

Introduction

Several scholars have argued that the process of learning research skills is often obstructed by motivational problems (Lehti & Lehtinen, 2005 ; Murtonen, 2005 ). Some even describe these issues as students having an aversion towards research (Pietersen, 2002 ). Examples of motivational problems are that students experience research courses as boring, inaccessible, or irrelevant to their daily lives (Braguglia & Jackson, 2012 ). In a research synthesis on teaching and learning research methods, Earley ( 2014 ) argues that students fail to see the relevance of research methods courses, are anxious or nervous about the course, are uninterested and unmotivated to learn the material, and have poor attitudes towards learning research skills. It should be mentioned that the studies mentioned above focused on the field of higher university education. In upper secondary education, to date, students’ motivation towards learning research skills has rarely been studied. As difficulties while learning research seem to relate to problems involving students’ previous experiences regarding learning research skills (Murtonen, 2005 ), we argue that fostering students’ motivation from secondary education onwards is a promising area of research.

The current study combines insights from instructional design theory and self-determination theory (SDT, Deci & Ryan, 2000 ), in order to investigate the cognitive and motivational effects of providing psychological need support (support for the need for autonomy, competence and relatedness) in a 4C/ID based (van Merriënboer & Kirschner, 2018 ) online learning environment fostering upper secondary schools students’ research skills. In the following section, we elaborate on the definition of research skills in the understudied domain of behavioral sciences; on 4C/ID (van Merriënboer & Kirschner, 2018 ) as an instructional design model for complex learning; and on self-determination theory and its related need theory (Deci & Ryan, 2000 ). In addition, the research questions addressed in the current study are outlined.

Conceptual framework

Research skills.

As described by Fischer et al., ( 2014 , p. 29), we define research skills 1 as a broad set of skills used “to understand how scientific knowledge is generated in different scientific disciplines, to evaluate the validity of science-related claims, to assess the relevance of new scientific concepts, methods, and findings, and to generate new knowledge using these concepts and methods”. Furthermore, eight scientific activities learners engage in while performing research are distinguished, namely: (1) problem identification, (2) questioning, (3) hypothesis generation, (4) construction and redesign of artefacts, (5) evidence generation, (6) evidence evaluation, (7) drawing conclusions, and (8) communicating and scrutinizing (Fischer et al., 2014 ). Fischer et al. ( 2014 ) argue that both the nature of, and the weights attributed to each of these activities, differ between domains. Intervention studies aiming to foster research skills are almost exclusively situated in natural sciences domains (Engelmann et al., 2016 ), leaving behavioral sciences domains largely understudied. The current study focuses on research skills in the understudied domain of behavioral sciences. We refer to the domain of behavioral sciences as the study of questions related to how people behave, and why they do so. Human behavior is understood in its broadest sense, and is the study of object in fields of psychology, educational sciences, cultural and social sciences.

The design of the learning environments used in this study is based on an existing instructional design model, namely the 4C/ID model (van Merriënboer & Kirschner, 2018 ). The 4C/ID model has been proven repeatedly effective in fostering complex skills (Costa et al., 2021 ), and thus drew our attention for the case of research skills, as research skills can be considered complex skills (it requires learners to integrate knowledge, skills and attitudes while performing complex learning tasks). Since the 4C/ID model focusses on supporting students’ cognitive outcomes, it might not be considered as relevant from a motivational point of view. However, since we argue that a deliberately designed learning environment from a cognitive point of view is an important prerequisite to provide qualitative motivational support, we briefly sketch the 4C/ID model and its characteristics. The 4C/ID model has a comprehensive character, integrating insights from different theories and models (Merrill, 2002 ), and highlights the relevance of four crucial components: learning tasks, supportive information, part task-practice, and just-in-time information. Central characteristics of these four components are that (a) high variability in authentic learning tasks is needed in order to deal with the complexity of the task; (b) supportive information is provided to the students in order to help them build mental models and strategies for solving the task under study (Cook & McDonald, 2008 ); (c) part-task practice is provided for recurrent skills that need to be automated; and (d) just-in-time (procedural) information is provided for recurrent skills.

Taking into account students’ cognitive struggles regarding research skills, and the existing research on the role of support in fostering research skills (see for example de Jong & van Joolingen, 1998 ), the 4C/ID model was found suitable to design a learning environment for research skills. This is partly because of its inclusion of (almost) all of the support found effective in the literature on research skills, such as providing direct access to domain information at the appropriate moment, providing learners with assignments, including model progression, the importance of students’ involvement in authentic activities, and so on (Chi, 2009 ; de Jong, 2006 ; de Jong & van Joolingen, 1998 ; Engelmann et al., 2016 ). While mainly implemented in vocational oriented programs, the 4C/ID model has been proposed as a good model to design learning environments aiming to foster research skills as well (Bastiaens et al., 2017 ; Maddens et al., 2020b ). Indeed, acquiring research skills requires complex learning processes (such as coordinating different constituent skills). Overall, the 4C/ID model can be considered to be highly suitable for designing learning environments aiming to foster research skills. Given its holistic design approach, it helps “to deal with complexity without losing sight of the interrelationships between the elements taught” (van Merriënboer & Kirschner, 2018 , p. 5).

Although the 4C/ID model has been used widely to construct learning environments enhancing students’ cognitive outcomes (see for example Fischer, 2018 ), research focusing on students’ motivational outcomes related to the 4C/ID model is scarce (van Merriënboer & Kirschner, 2018 ). Van Merriënboer and Kirschner ( 2018 ) suggest self-determination theory (SDT; Deci & Ryan, 2000 ) and its related need theory as a sound theoretical framework to investigate motivation in relation to 4C/ID.

Self-determination theory

Self-determination theory (SDT; Deci & Ryan, 2000 ) provides a broad framework for the study of motivation and distinguishes three types of motivation: amotivation (a lacking ability to self-regulate with respect to a behaviour), extrinsic motivation (extrinsically motivated behaviours, be they self-determined versus controlled), and intrinsic motivation (the ‘highest form’ of self-determined behaviour) (Deci & Ryan, 2000 ). According to Deci and Ryan ( 2000 , p. 237), intrinsic motivation can be considered “a standard against which the qualities of an extrinsically motivated behavior can be compared to determine its degree of self-determination”. Moreover, the authors (Deci & Ryan, 2000 , p. 237) argue that “extrinsic motivation does not typically become intrinsic motivation”. As the current study focuses on research skills in an academic context in which students did not voluntary chose to learn research skills, and thus learning research skills can be considered instrumental (directed to attaining a goal), the current study focuses on students’ amotivation, and students’ extrinsic motivation, realistically striving for the most self-determined types of extrinsic motivation.

Four types of extrinsic motivation are distinguished by SDT (external regulation, introjection, identification, and integration). These types can be categorized in two overarching types of motivation (autonomous and controlled motivation). Autonomous motivation contains the integrated and identified regulation towards a task (be it because the task is considered interesting, or because the task is considered personally relevant respectively). Controlled motivation refers to the external and introjected regulation towards the task (as a consequence of external or internal pressure respectively) (Vansteenkiste et al., 2009 ). More autonomous types of motivation have been found to be related to more positive cognitive and motivational outcomes (Deci & Ryan, 2000 ).

SDT further maintains that one should consider three innate psychological needs related to students’ motivation. These needs are the need for autonomy, the need for competence, and the need for relatedness. The need for autonomy can be described as the need to experience activities as being “concordant with one’s integrated sense of self” (Deci & Ryan, 2000 , p. 231). The need for competence refers to the need to feel effective when dealing with the environment (Deci & Ryan, 2000 ). The need for relatedness contains the need to have close relationships with others, including peers and teachers (Deci & Ryan, 2000 ). The satisfaction of these needs is hypothesized to be related to more internalization, and thus to more autonomous types of motivation (Deci & Ryan, 2000 ). This relationship has been studied frequently (for a recent overview, see Vansteenkiste et al., 2020 ). Indeed, research established the positive relationships between perceived autonomy (see for example Deci et al., 1996 ), perceived competence (see for example Vallerand & Reid, 1984 ), and perceived relatedness (see for example Ryan & Grolnick, 1986 for a self-report based study) with students’ more positive motivational outcomes. Apart from students’ need satisfaction, several scholars also aim to investigate need frustration as a different notion, as “it involves an active threat of the psychological needs (rather than a mere absence of need satisfaction)” (Vansteenkiste et al., 2020 , p. 9). In what follows, possible operationalizations are defined for the three needs.

Possible operationalizations of autonomy need support found in the literature are: teachers accepting irritation or negative feelings related to aspects of a task perceived as “uninteresting” (Reeve, 2006 ; Reeve & Jang, 2006 ; Reeve et al., 2002 ); providing a meaningful rationale in order to explain the value/usefulness of a certain task and stressing why involving in the task is important or why a rule exists (Deci & Ryan, 2000 ); using autonomy-supportive, inviting language (Deci et al., 1996 ); and allowing learners to regulate their own learning and to work at their own pace (Martin et al., 2018 ). Related to competence support, possible operationalizations are: providing a clear task rationale and providing structure (Reeve, 2006 ; Vansteenkiste et al., 2012 ); providing informational positive feedback after a learning activity (Deci et al., 1996 ; Martin et al., 2018 ; Vansteenkiste et al., 2012 ); providing an indication of progress and dividing content into manageable blocks (Martin et al., 2018 ; Schunk, 2003 ); and evaluating performance by means of previously introduced criteria (Ringeisen & Bürgermeister, 2015 ). Possible operationalizations concerning relatedness support are: teacher’s relational supports (Ringeisen & Bürgermeister, 2015 ); encouraging interaction between course participants and providing opportunities for learners to connect with each other (Butz & Stupnisky, 2017 ; van Merriënboer & Kirschner, 2018 ); using a warm and friendly approach or welcoming learners personally into a course (Martin et al., 2018 ); and offering a platform for learners to share ideas and to connect (Butz & Stupnisky, 2017 ; Martin et al., 2018 ).

In the current research, SDT is selected as a theoretical framework to investigate students’ motivation towards learning research skills, as, in contrast to other more purely goal-directed theories, it includes the concept of innate psychological needs or the Basic Psychological Need Theory (Deci & Ryan, 2000 ; Ryan, 1995 ; Vansteenkiste et al., 2020 ), and it describes the relation between these perceived needs and students’ autonomous motivation: higher levels of perceived needs relate to more autonomous forms of motivation. The inclusion of this need theory is considered an advantage in the case of research skills because research revealed problems of students with respect to both their feelings of competence in relation to research skills (Murtonen, 2005 ), as their feelings of autonomy in relation to research skills (Martin et al., 2018 ), as was indicated in the introduction. As such, fostering students’ psychological needs while learning research skills seems a promising way of fostering students’ motivation towards learning research skills.

4C/ID and SDT

One study (Bastiaens et al., 2017 ) was found to implement need support in 4C/ID based learning environments, comparing a traditional module, a 4C/ID based module and an autonomy supportive 4C/ID based module in a vocational undergraduate education context. Autonomy support was operationalized by means of providing choice to the learners. No main effect of the conditions was found on students’ motivation. Surprisingly, providing autonomy support did also not lead to an increase in students’ autonomy satisfaction. Similarly, no effects were found on students’ relatedness and competence satisfaction. Remarkably, students did qualitatively report positive experiences towards the need support, but this did not reflect in their quantitatively reported need experiences. In a previous study performed in the current research trajectory, Maddens et al. ( under review ) investigated the motivational effects of providing autonomy support in a 4C/ID based online learning environment fostering students’ research skills, compared to a learning environment not providing such support. Autonomy support was operationalized as stressing task meaningfulness to the students. Based on insights from self-determination theory, it was hypothesized that students in the autonomy condition would show more positive motivational outcomes compared to students in the baseline condition. However, results showed that students’ motivational outcomes appeared to be unaffected by the autonomy support. One possible explanation for this unexpected finding was that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support (Deci & Ryan, 2000 ; Niemiec & Ryan, 2009 ), and thus, that the intervention was insufficiently powerful for effects to occur. Autonomy support has often been manipulated in experimental research (Deci et al., 1994 ; Reeve et al., 2002 ; Sheldon & Filak, 2008 ). However, the three needs are rarely simultaneously manipulated (Sheldon & Filak, 2008 ).

Integrated need support

Although not making use of 4C/ID based learning environments, some scholars have focused on the impact of integrated (autonomy, competence and relatedness) need support on learners’ motivation. For example, Raes and Schellens ( 2015 ) found differential effects of a need supportive inquiry environment on upper secondary school students’ motivation: positive effects on autonomous motivation were only found in students in a general track, and not in students in a science track. This indicates that motivational effects of need-supportive environments might differ between tracks and disciplines. However, Raes and Schellens ( 2015 ) did not experimentally manipulate need support, as the learning environment was assumed to be need-supportive and was not compared to a non-need supportive learning environment. Pioneers in manipulating competence, relatedness and autonomy support in one study are Sheldon and Filak ( 2008 ), predicting need satisfaction and motivation based on a game-learning experience with introductory psychology students. Relatedness support (mainly operationalized by emphasizing interest in participants’ experiences in a caring way) had a significant effect on intrinsic motivation. Competence support (mainly operationalized by means of explicating positive expectations) had a marginal significant effect on intrinsic motivation. No main effects on intrinsic motivation were found regarding autonomy support (mainly operationalized by means of emphasizing choice, self-direction and participants’ perspective upon the task). However, as is often the case in motivational research based on SDT, the task at hand was quite straight forward (a timed task in which students try to form as many words as possible from a 4 × 4 letter grid), and thus, the applicability of the findings for providing need support in 4C/ID based learning environments for complex learning might be limited.

In the preceding section, several operationalizations of need support were discussed. Deci and Ryan ( 2000 ) argue that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support. However, such integrated need support has rarely been empirically studied (Sheldon & Filak, 2008 ). In addition, research investigating how need support can be implemented in learning environments based on the 4C/ID model is particularly scarce (van Merriënboer & Kirschner, 2018 ). This study aims to combine insights from instructional design theory for complex learning (van Merriënboer & Kirschner, 2018 ) and self-determination theory (Deci & Ryan, 2000 ) in order to investigate the motivational effects of providing need support in a 4C/ID based learning environment for students’ research skills. A pretest-intervention-posttest design is set up in order to compare 233 upper secondary school behavioral sciences students’ cognitive and motivational outcomes among two conditions: (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. The following research questions are answered based on a combination of quantitative and qualitative data (see ‘method’): (1) Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task? ; ( 2) What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes (i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)? ; (3) What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)? ; (4) How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment? .

The first three questions are answered by means of quantitative data. Since the learning environment is constructed in line with existing instructional design principles for complex learning, we hypothesize that both learning environments will succeed in improving students’ research skills (RQ1). Relying on insights from self-determination theory (Deci & Ryan, 2000 ), we hypothesize that providing need support will enhance students’ autonomous motivation (RQ2). In addition, we hypothesize students’ need satisfaction to be positively related to students’ autonomous motivation (RQ3). These hypotheses on the relationship between students’ needs and students’ motivation rely on Vallerands’ ( 1997 ) finding that changes in motivation can be largely explained by students’ perceived competence, autonomy and relatedness (as psychological mediators). More specifically, Vallerand ( 1997 ) argues that environmental factors (in this case the characteristics of a learning environment) influence students’ perceptions of competence, autonomy, and relatedness, which, in turn, influence students’ motivation and other affective outcomes. In addition, based on the self-determination literature (Deci & Ryan, 2000 ), we expect students’ motivation to be positively related to students’ cognitive outcomes. In order to answer the fourth research question, qualitative data (students’ qualitative feedback on the learning environments) is analysed and categorized based on the need satisfaction and need frustration concepts (RQ4) in order to thoroughly capture the meaning of the quantitative results collected in light of RQ1–3. No hypotheses are formulated in this respect.

Methodology

Participants.

The study took place in authentic classroom settings in upper secondary behavioral sciences classes. In total, 233 students from 12 classes from eight schools in Flanders participated in the study. All participants are 11th or 12th grade students in a behavioral sciences track 2 in general upper secondary education in Flanders (Belgium). Classes were randomly assigned to one out of two experimental conditions. Of all 233 students, 105 students (with a mean age of 16.32, SD 0.90) worked in the baseline condition (of which 62% 11th grade students, 36% 12th grade students, and 2% not determined; and of which 31% male, 68% female, and 1% ‘other’), and 128 students (with a mean age of 16.02, SD 0.59) worked in the need supportive condition (of which 80% 11th grade students, and 20% 12th grade students; and of which 19% male, and 81% female). As the current study did not randomly assign students within classes to one out of the two conditions, this study should be considered quasi-experimental. Full randomization was considered but was not feasible as students worked in the learning environments in class, and would potentially notice the experimental differences when observing their peers working in the learning environment. As such, we argued that this would potentially cause bias in the study. By taking into account students’ pretest scores on the relevant variables (cognitive and motivational outcomes) as covariates, we aimed to adjust for inter-conditional differences. No such differences were found for students’ autonomous motivation t (226) =  − 0.115, p  < 0.909, d  = 0.015, and students’ amotivation t (226) =  − 0.658, p  < 0.511, d  =  − 0.088. However, differences were observed for students’ controlled motivation t (226) =  − 2.385, p  < 0.018, d  =  − 0.318, and students’ scores on the LRST pretest t (225) = − 5.200, p  < 0.001, d  =  − 0.695.

Study design and procedure

In a pretest session of maximum two lesson hours, the Leuven Research Skills Test (LRST, Maddens et al., 2020a ), the Academic Self-Regulation Scale (ASRS, Vansteenkiste et al., 2009 ), and four items related to students’ amotivation (Aydin et al., 2014 ) were administered in class via an online questionnaire, under supervision of the teacher. In the subsequent eight weeks, participants worked in the online learning environment, one hour a week. Out of the 233 participating students, 105 students studied in a baseline online learning environment. The baseline online learning environment 3 is systematically designed using existing instructional design principles for complex learning based on the 4C/ID model (van Merriënboer & Kirschner, 2018 ). All four components of the 4C/ID model were taken into account in the design process: regarding the first component, the learning tasks included real-life, authentic cases. More specifically, tasks were selected from the domains of psychology, educational sciences and sociology. As such, there was a large variety in the cases used in the learning tasks. This large variety in learning tasks is expected to facilitate transfer of learners’ research skills in a wide range of contexts. Furthermore, the tasks were ill-structured and required learners to make judgments, in order to provoke deep learning processes. Regarding the second component, supportive information was provided for complex tasks in the learning environment, such as formulating a research question, where students can consult general information on what constitutes a good research question, can consult examples or demonstrations of this general information, and can receive cognitive feedback on their answers (for example by means of example answers). Examples of the implementation of the third component (procedural information) are the provision of information on how to recognize a dependent and an independent variable by means of on-demand (just-in-time) presentation by means of pop-ups; information on how to use Boolean operators; and information on how to read a graph. To avoid split attention, this kind of information was integrated with the task environment itself (van Merriënboer & Kirschner, 2018 ). Finally, the fourth component, part-task-practice (by means of short tests) was implemented for routine aspects of research skills that should be automated, for example the formulation of a search query.

The remaining participating students ( n  = 128) completed an adapted version of the baseline online learning environment, in which autonomy, relatedness and competence support are provided. In total, need support consisted of 12 implementations (four implementations for each need), based on existing research on need support. An overview of these adaptations can be found in Tables ​ Tables1 1 and ​ and2. 2 . Although, ideally, students would work in class, under supervision of their teacher, this was not possible for all classes, due to the COVID-19 restrictions. 4 As a consequence, some students completed the learning environment partly at home. All students were supervised by their teachers (be it virtually or in class), and the researcher kept track of students’ overall activities in order to be able to contact students who did not complete the main activities. During the last two sessions of the intervention, participants submitted a two-pages long research proposal (“two-pager”). One week after the intervention, the LRST (Maddens et al., 2020a ), the ASRS (Vansteenkiste et al., 2009 ), four items related to students’ amotivation (Aydin et al., 2014 ), the value/usefulness scale (Ryan, 1982 ) and the Basic Psychological Need Satisfaction and Frustration Scale (BPNSNF, Chen et al., 2015 ) were administered in a posttest session of maximum two hours. Although most classes succeeded in organizing this posttest session in class, for some classes this posttest was administered at home. However, all classes were supervised by the teacher (be it virtually or in class). These contextual differences at the test moments will be reflected upon in the discussion section.

Adaptations online learning environment

Overview instruments

a When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively

Instruments

In this section, we elaborate on the tests used during the pretest and the posttest. Example items for each scale are presented in Appendix 1.

Motivational outcomes

In the current study, two groups of motivational outcomes are assessed: (1) students’ need satisfaction and frustration, and students’ experiences of value/usefulness; and (2) students’ level of autonomous motivation, controlled motivation, and amotivation. When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively.

The BPNSNF-training scale (The Basic Psychological Need Satisfaction and Frustration Scale, Chen et al., 2015 ; translated version Aelterman et al., 2016 5 ) measured students’ need satisfaction and need frustration while working in the learning environment, and consists of 24 items (four items per scale): (autonomy satisfaction, α  = 0.67; ω = 0.67; autonomy frustration, α  = 0.76; ω = 0.76; relatedness satisfaction, α  = 0.79; ω = 0.79; relatedness frustration, α  = 0.60; ω = 0.61; competence satisfaction, α  = 0.72; ω = 0.73; competence frustration, α  = 0.68; ω = 0.67). The items are Likert-type items ranging from one (not at all true) to five (entirely true). Although the current study focusses mainly on students’ need satisfaction, the scales regarding students’ need frustration are included in order to be able to also detect students’ potential ill-being and in order to detect potential critical issues regarding students’ needs. In addition to the BPNSNF, by means of seven Likert-type items ranging from one (not at all true) to seven (entirely true), the (for the purpose of this research translated) value/usefulness scale of the Intrinsic Motivation Inventory (IMI, Ryan, 1982 ) measured to what extent students valued the activities of the online learning environment ( α  = 0.92; ω = 0.92). Since in the research skills literature problems have been observed related to students’ perceived value/usefulness of research skills (Earley, 2014 ; Murtonen, 2005 ), and this concept is not sufficiently stressed in the BPNSNF-scale, we found it useful to include this value/usefulness scale to the study. The difference in the range of the answer possibilities (one to five vs one to seven) exists because we wanted to keep the range as initially prescribed by the authors of each instrument. All motivational measures are calculated by adding the scores on every item, and dividing this sum score by the number of items on a scale, leading to continuous outcomes. Although the IMI and the BPNSNF targeted students’ experiences while completing the online learning environment, these measures were administered during the posttest. Thus, students had to think retrospectively about their experiences. In order to prevent cognitive overload while completing the online learning environment, these measures were not administered during the intervention itself.

Students’ autonomous and controlled motivation towards learning research skills was measured by means of the Dutch version of the Academic Self-Regulation Scale (ASRS; Vansteenkiste et al., 2009 ), adapted to ‘ research skills ’. The ASRS consists of Likert-type items ranging from one (do not agree at all) to five (totally agree), and contains eight items per subscale (autonomous and controlled motivation). In the autonomous motivation scale, four items are related to identified regulation, and four items are related to intrinsic motivation. 6 In the controlled motivation scale, four items are related to external regulation, and four items are related to introjected regulation. Both scales (autonomous motivation and controlled motivation) indicated good internal consistency for the study’s data (autonomous motivation: α  = 0.91; 0.92; ω = 0.90; 0.92; controlled motivation: α  = 0.83; 0.86; ω = 0.82; 0.85). The items were adapted to the domain under study (motivation to learn about research skills). Based on students’ motivational issues related to research skills, we found it useful to also include a scale to assess students’ amotivation. This was measured with (for the purpose of the current research translated) four items related to students’ amotivation regarding learning research skills, adapted from Academic Motivation Scale for Learning Biology (Aydin et al., 2014 ) ( α  = 0.80; 0.75; ω = 0.81; 0.75). Also this measure consist of Likert-type items ranging from one (do not agree at all) to five (totally agree).

Cognitive outcomes

Students’ research skills proficiency was measured by means of a research skills test (Maddens et al., 2020a ) and a research skills task.

The research skills test used in this study is the LRST (Maddens et al., 2020a ) consisting of a combination of 37 open ended and close ended items ( α  = 0.79; 0.82; ω = 0.78; ω = 0.80 for this data set), administered via an online questionnaire. Each item of the LRST is related to one of the eight epistemic activities regarding research skills as mentioned in the introduction (Fischer et al., 2014 ), and is scored as 0 or 1. The total score on the LRST is calculated by adding the mean subscale scores (related to the eight epistemic activities), and dividing them by eight (the number of scales). In a previous study (Maddens et al., 2020a ), the LRST was checked and found suitable in light of interrater reliability ( κ  = 0.89). As the same researchers assessed the same test with a similar cohort in the current study, the interrater reliability was not calculated for this study.

In the research skills task (“two pager task”), students were asked to write a research proposal of maximum two pages long. The concrete instructions for this research proposal are given in Appendix 1. In this research proposal, students were asked to formulate a research question and its relevance; to explain how they would tackle this research question (method and participants); to explain their hypotheses or expectations; and to explain how they would communicate their results. The two-pager task was analyzed using a pairwise comparison technique, in which four evaluators (i.e. the four authors of this paper) made comparative judgements by comparing two two-pagers at a time, and indicating which two-pager they think is best. All four evaluators are researchers in educational sciences and are familiar with the research project and with assessing students’ texts. This shared understanding and expertise is a prerequisite for obtaining reliable results (Lesterhuis et al., 2018 ). The comparison technique is performed by means of the Comproved tool ( https://comproved.com ). As described by Lesterhuis et al. ( 2018 , p. 18), “the comparative judgement method involves assessing a text on its overall quality. However, instead of requiring an assessor to assign an absolute score to a single text, comparative judgement simplifies the process to a decision about which of two texts is better”. In total, 1635 comparisons were made (each evaluator made 545 comparisons), and this led to a (interrater)reliability score of 0.79. In a next step, these comparative judgements were used to rank the 218 products (15 students did not submit a two-pager) on their quality; and the products were graded based on their ranking. This method was used to grade the two-pagers because it facilitates the holistic evaluation of the tasks, based on the judgement of multiple experts (interrater reliability).

Qualitative feedback

Students’ experiences with the online learning environment were investigated in the online learning environment itself. After completing the learning environment, students were asked how they experienced the tasks, the theory, the opportunity to post answers in the forum and to ask questions via the chat, what they liked or disliked in the online learning environment, and what they disliked in the online learning environment (Fig.  1 ).

Study overview

The first research question (” Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task?” ) is answered by means of a paired samples t -test in order to look for overall improvements in order to detect potential general trends, followed by a full factorial MANCOVA, as this allows us to investigate the effectiveness for both conditions taking into account students’ pretest scores. Hence, the condition is included as an experimental factor, and students’ scores on the LRST and the two-pager task are included as continuous outcome variables. Students’ pretest scores on the LRST are included as a covariate. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariate.

The second research question (“ What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes, i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?”) ;) is answered by means of a full factorial MANCOVA. The condition (need satisfaction condition versus baseline condition) is included as an experimental factor, and students’ responses on the value/usefulness, autonomous and controlled motivation, amotivation, and need satisfaction scales are included as continuous outcome variables. ASRS pretest scores (autonomous and controlled motivation) are included as covariates in order to test the differences between group means, adjusted for students’ a priori motivation. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariates, and assumptions to be met to perform a MANCOVA are checked. 7

The third research question ( “ What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?” ), is initially answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students’ value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables. The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores, and (5) scores on the two-pager task as dependent variables. As a follow-up analysis (see ‘ results ’) two additional regression analyses are performed to look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST (6) and two-pager (7)). As the goal of this analysis is to investigate the relationships between variables as described in SDT research, this analysis focuses on the full sample, rather than distinguishing between the two conditions. An ‘Enter’ method (Field, 2013 ) is used in order to enter the independent variables simultaneously (in line with Sheldon et al., 2008 ).

The fourth research question (“ How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment?” ) is analyzed by means of the knowledge management tool Citavi. Based on the theoretical framework, students’ experiences are labeled by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. For example, students’ quotes referring to the value/usefulness of the learning environment, are labeled as ‘autonomy satisfaction’ or ‘autonomy frustration’. Students’ references towards their feelings of mastery of the learning content are labeled as ‘competence satisfaction’ or ‘competence frustration’. Students’ quotes regarding their relationships with peers and teachers are labeled as ‘relatedness satisfaction’ or ‘relatedness frustration’ (Fig.  2 ).

Overview variables

Does the deliberately designed (4C/ID based) learning environments improve students’ research skills, as measured by a research skills test and a research skills task?

Paired samples t -test. A paired samples t -test reveals that, in general, students ( n  = 210) improved on the LRST-posttest ( M  = 0.57, SD  = 0.16) compared to the pretest ( M  = 0.51, SD  = 0.15) (range 0–1). The difference between the posttest and the pretest is significant t (209) =  − 8.215, p  < 0.001, d 8  =  − 0.567. The correlation between the LRST pretest and posttest is 0.70 ( p  < 0.010).

MANCOVA. A MANCOVA model ( n  = 196) was defined checking for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariate LRST pretest did not show significant interaction effects for the two outcome variables LRST post ( p  = 0.259) and the two-pager task ( p  = 0.702). The correlation between the outcome variables (LRST post and two-pager), is 0.28 ( p  < 0.050).

Of all 233 students, 36 students were excluded from the main analysis because of missing data (for example, because they were absent during a pretest or posttest moment). These students were excluded by means of a listwise deletion method because we found it important to use a complete dataset, since, in a lot of cases, students who did not complete the pretest or posttest, did also not complete the entire learning environment. Including partial data for these students could bias the results. The baseline condition counted 86 students, and the need satisfaction condition counted 111 students. Using Pillai’s Trace [ V  = 0.070, F (2,193) = 7.285, p  ≤ 0.001], there was a significant effect of the condition on the cognitive outcome variables, taking into account students’ LRST pretest scores. Separate univariate ANOVAs on the outcome variables revealed no significant effect of the condition on the LRST posttest measure, F (1,194) = 2.45, p  = 0.120. However, a significant effect of condition was found on the two-pager scores, F (1,194) = 13.69, p  < 0.001 (in the baseline group, the mean score was 6,6/20; in the need condition group, the mean score was 7,6/20). It should be mentioned that both scores are rather low.

What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID based) learning environment fostering students’ research skills, on students’ motivational outcomes (students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?

Paired samples t -tests. The correlations between students’ pretest and posttestscores for the motivational measures are 0.67 ( p  < 0.010) for autonomous motivation; 0.44 ( p  < 0.010) for controlled motivation, and 0.38 for amotivation ( p  < 0.010). Regarding the differences in students’ motivation, three unexpected findings were observed. Overall, students’ ( n  = 215) amotivation was higher on the posttest ( M  = 2.26, SD  = 0.89) compared to the pretest ( M  = 1.77, SD  = 0.79) (based on a score between 1 and 5). The difference between the posttest and the pretest is significant t (214) =  − 7.69, p  < 0.001, d  =  − 0.524. Further analyses learn that the amotivation means in the baseline group increased with 0.65, and the amotivation in the need support group increased with 0.37. In addition, students’ ( n  = 215) autonomous motivation was higher on the pretest ( M  = 2.81, SD  = 0.81) compared to the posttest ( M  = 2.64, SD  = 0.82). The difference between the posttest and the pretest is significant t (214) = 3.72, p  < 0.001, d  = 0.254. Students’ mean scores on autonomous motivation in the baseline condition decreased with 0.19, and students’ autonomous motivation in the need support condition decreased with 0.15. Students’ ( n  = 215) controlled motivation was higher on the posttest ( M  = 2.33, SD  = 0.75) compared to the pretest ( M  = 1.93, SD  = 0.67). The difference between the posttest and the pretest is significant t (214) =  − 07.72, p  < 0.001, d  =  − 0.527. Students’ controlled motivation in the baseline group increased with 0.36, and students’ controlled motivation in the need support group increased with 0.43. However, overall, all mean scores are and stay below neutral score (below 3), indicating robust low autonomous, controlled and amotivation scores (see Table ​ Table3). 3 ). An independent samples T -test on the mean differences between these measures shows that the increases/decreases on autonomous motivation [ t (213) =  − 0.506, p  = 0.613, d  =  − 0.069] and controlled motivation [ t (213) =  − 0.656, p  = 0.513, d  =  − 0.090] did not differ between the two groups. However, the increases in amotivation [ t (213) = 2.196, p  = 0.029, d  = 0.301] does differ significantly between the two conditions. More specifically, the increase was lower in the need supportive condition compared to the baseline condition.

Mean scores and standard deviations motivational variables

a Overall, students’ ( n  = 215) autonomous motivation was significantly higher on the pretest compared to the posttest ( t (214) 3.72, p  ≤ 0.001, d  = 0.254

b Students’ (n = 215) controlled motivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 7.72, p  ≤ 0.001, d  =  − 0.527

c Students’ ( n  = 215) amotivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 07,69, p  ≤ 0.001, d  =  − 0.534)

MANCOVA. Of all 233 students, 18 students were excluded from the analysis because of missing data (for example, because they were absent during a pretest or posttest moment). Compared to the cognitive analyses, the amount of missing data is lower concerning motivational outcomes since, concerning the cognitive outcomes, some students did not complete the two-pager task. However, we found it important to use all relevant data and chose to report this is in a clear way. In total, the baseline condition counted 97 students, and the experimental condition counted 118 students. Similar to the analysis for the cognitive outcomes, a MANCOVA model was defined to check for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariates did not show significant interaction effects for the outcome variables. 9

Using Pillai’s Trace [ V  = 0.113, F (10,201) = 2.558, p  = 0.006], there was a significant effect of condition on the motivational variables, taking into account students’ autonomous and controlled pretest scores, and students’ a priori amotivation. Separate univariate ANOVAs on the outcome variables revealed a significant effect of the condition on the outcome variables amotivation, F (1,210) = 3.98, p  = 0.047; and relatedness satisfaction F (1,210) = 6.41, p  = 0.012. As was hypothesized, students in the need satisfaction group reported less amotivation ( M  = 2.38), compared to students in the baseline group ( M  = 2.18). In contrast to what was hypothesized, students in the need satisfaction group reported less relatedness satisfaction ( M  = 2.43) compared to students in the baseline group ( M  = 2.73), and no significant effects of condition were found on the outcome variables autonomous motivation post, controlled motivation post, value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, and relatedness frustration. Table ​ Table4 4 shows the correlations between the motivational outcome variables.

Correlations motivational outcome variables

AM autonomous motivation, CM controlled motivation, AMOT amotivation, VU value/usefulness, AS autonomy satisfaction, AF autonomy frustration, CS competence satisfaction, CF competence frustration, RS relatedness satisfaction, RF relatedness frustration

**Correlation is significant at the 0.010 level (2-tailed)

*Correlation is significant at the 0.050 level (2-tailed)

What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?

The third research question (investigating the relationships between students’ need satisfaction, students’ motivation and students’ cognitive outcomes), is answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables ( n  = 219). The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores ( n  = 215), and (5) scores on the two-pager task as dependent variables ( n  = 206). Table ​ Table4 4 depicts the correlations for the first three analyses. Table ​ Table5 5 depicts the correlations for the last two analyses.

Correlations motivational and cognitive outcome variables

AM  autonomous motivation, CM  controlled motivation, AMOT  amotivation, LRST  score on LRST, Twopager  score on Twopager

In Table ​ Table3, 3 , we can see that students in both conditions experience average competence and autonomy satisfaction. However, students’ relatedness satisfaction seems low in both conditions. This finding will be further discussed in the discussion section. For autonomous motivation, a significant regression equation was found F (7,211) = 37.453, p  < 0.001. The regression analysis (see Table ​ Table5) 5 ) further reveals that all three satisfaction scores (competence satisfaction, relatedness satisfaction and autonomy satisfaction) contribute positively to students’ autonomous motivation, as does students’ experienced value/usefulness. Also for students’ controlled motivation a significant regression equation was found F (7,211) = 8.236, p  < 0.001, with students’ autonomy frustration and students’ relatedness satisfaction contributing to students’ controlled motivation. The aforementioned relationships are in line with the expectations. However, we noticed that relatedness satisfaction contributed to students’ controlled motivation in the opposite direction of what was expected (the higher students’ relatedness satisfaction, the lower students’ controlled motivation). This finding will be reflected upon in the discussion section. Also for students’ amotivation, a significant regression equation was found F (7,211) = 7.913, p  < 0.001. Students’ autonomy frustration, competence frustration and students’ value/usefulness contributed to students’ amotivation in an expected way. Also for cognitive outcomes related to the research skills test, a significant regression equation was found F (3,211) = 8.351, p  < 0.001. In line with the expectations, the regression analysis revealed that the higher students’ amotivation, the lower students’ scores on the research skills test. No significant regression equation was found for the outcome variable related to the research skills task F (3,202) = 0.954, p  < 0.416. For all regression equations, the R 2 and the exact regression weights are presented in Table ​ Table6 6 .

Linear model of predictors of autonomous motivation, controlled motivation, amotivation, LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

*Significant at .050 level

As a follow-up analysis and in order to better understand the outcomes, we decided to also look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST and two-pager) by means of two additional regression analyses. The motivation behind this decision relates to possible issues regarding the motivational measures used, which might complicate the investigation of indirect relationships (see discussion). The results are provided in Table ​ Table7, 7 , and show that both for the LRST and the two-pager, respectively, a significant [ F (7,207) = 4.252, p  < 0.001] and marginally significant regression weight [ F (7,199) = 2.029, p  = 0.053] was found. More specifically, students’ relatedness satisfaction and students’ perceived value/usefulness contribute to students’ scores on the two-pager and on the research skills test. As one would expect, we see that the higher students’ value/usefulness, the higher students’ scores on both cognitive outcomes. In contrast to one would expect, we found that the higher students’ relatedness satisfaction, the lower students’ scores on the cognitive outcomes. These findings are reflected upon in the discussion section.

Linear model of predictors of LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID based) learning environment?

As was mentioned in the method section, the fourth research question was analysed by labelling students’ qualitative feedback by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. By means of this approach, we could analyse students’ need experiences in a fine grained manner. When students’ quotes were applicable to more than one code, they were labelled with different codes. In what follows, students’ quotes are indicated with the codes “BC” (baseline condition) or “NSC” (need satisfaction condition) in order to indicate which learning environment the student completed. Of all 233 students, 124 students provided qualitative feedback (44 in BC and 80 in NSC). In total, 266 quotes were labeled. Autonomy satisfaction was coded 40 times BC and 41 times in NSC; autonomy frustration was coded 13 times in BC and four times in NSC; competence satisfaction was coded 28 times in BC and 34 times in NSC; competence frustration was coded 31 times in BC and 27 times in NSC; relatedness satisfaction was coded 10 times in BC and 16 times in NSC; and relatedness frustration was coded five times in BC and 17 times in NSC. Several observations could be drawn from the qualitative data.

Related to autonomy satisfaction , in both conditions, several students explicitly mentioned the personal value and usefulness of what they had learned in the learning environment. While in the baseline condition, these references were often vague (“Now I know what people expect from me next year ”; “I think I might use this information in the future ”); some references appeared to be more specific in the need support condition (“I want to study psychology and I think I can use this information!”; “This is a good preparation for higher education and university ”; “I can use this information to write an essay ”; “I think the theory was interesting, because you are sure you will need it once. I don’t always have that feeling during a normal lesson in school”). In addition, students in both conditions mentioned that they found the material interesting, and that they appreciated the online format: “It’s different then just listening to a teacher, I kept interested because of the large variety in exercises and overall, I found it fun” (NSC).

Several comments were coded as ‘ autonomy frustration’ in both conditions. Some students indicated that they found the material “useless” (BC), or that “they did not remember that much” (BC). Others found the material “uninteresting” (BC), “heavy and boring” (NSC) or “not fun” (BC). In addition, some students “did not like to complete the assignments” (NSC), or “prefer a book to learn theory” (NSC).

Related to competence satisfaction , students in both conditions found the material “clear” (BC, NSC). In addition, students’ appreciated the example answers, the difficulty rate (“Some exercises were hard, but that is good. That’s a sign you’re learning something new” (NSC)), and the fact that the theory was segmented in several parts. In addition, students recognized that the material required complex skills: “I learned a lot, you had to think deeper or gain insights in order to solve the exercises” (NSC), “you really had to think to complete the exercises” (NSC). In the need satisfaction group, several quotes were labelled related to the specific need support provided. For example, students indicated that they appreciated the forum option: “If something was not clear, you could check your peer’s answers” (NSC). Students also valued the fact that they could work at their own pace: “I found it very good that we could solve everything at our own pace” (NSC); “good that you could choose your own pace, and if something was not clear to you, you could reread it at your own pace” (NSC). In addition, students appreciated the immediate feedback provided by the researcher “I found it very good that we received personal feedback from xxx (name researcher). That way, I knew whether I understood the theory correctly” (NSC); and the fact that they could indicate their progress “It was good that you could see how far you proceeded in the learning environment” (NSC).

In both the baseline and the need supportive condition, there were also several comments related to competence frustration . For example, students found exercises vague, unclear or too difficult. While students, overall, understood the theory provided, applying the theory to an integrative assignment appears to be very difficult: “I did understand the several parts of the learning environment, but I did not succeed in writing a research proposal myself” (NSC). “I just found it hard to respond to questions. When I had to write my two-pager research proposal, I really struggled. I really felt like I was doing it entirely wrong” (NSC)). In addition, a lot comments related to the fact that the theory was a lot to process in a short time frame, and therefore, students indicated that it was hard to remember all the theory provided. In addition, this led pressure in some students: “Sometimes, I experiences pressure. When you see that your peers are finished, you automatically start working faster.” (BC).

Concerning relatedness satisfaction , in the baseline condition, students appreciated the chat function “you could help each other and it was interesting to hear each other’s opinions about the topics we were working on” (BC). However, most students indicated that they did not make use of the chat or forum options. In the need satisfaction condition, students appreciated the forum and the chat function: “You knew you could always ask questions. This helped to process the learning material” (NSC), “My peers’ answers inspired me” (NSC), “Thanks to the chat function, I felt more connected to my peers” (NSC). In addition, students in the need satisfaction condition appreciated the fact that they could contact the researcher any time.

Several students made comments related to relatedness frustration . In both groups, students missed the ‘live teaching’: “I tried my best, but sometimes I did not like it, because you do not receive the information in ‘real time’, but through videos” (BC). In addition, students missed their peers: “We had to complete the environment individually” (BC). While some students appreciated the opportunity of a forum, other students found this possibility stressful: “I think the forum is very scary. I posted everything I had to, but I found it very scary that everyone can see what you post” (NSC). Others did not like the fact that they needed to work individually: “Sometimes I lost my attention because no one was watching my screen with me” (NSC); “I found it hard because this was new information and we could not discuss it with each other” (NSC); “I felt lonely” (NSC); “It is hard to complete exercises without the help of a teacher. In the future this will happen more often, so I guess I will have to get used to it” (NSC); “When I see the teacher physically, I feel less reluctant to ask questions” (NSC).

The current intervention study aimed at exploring the motivational and cognitive effects of providing need support in an online learning environment fostering upper secondary school students’ research skills. More specifically, we investigated the impact of autonomy, competence and relatedness support in an online learning environment on students’ scores on a research skills test, a research skills task, students’ autonomous motivation, controlled motivation, amotivation, need satisfaction, need frustration, and experienced value/usefulness. Adopting a pretest-intervention-posttest design approach, 233 upper secondary school behavioral sciences students’ motivational outcomes were compared among two conditions: (1) a 4C/ID inspired online learning environment condition (baseline condition), and (2) a condition with an identical online learning environment additively providing support for students’ autonomy, relatedness and competence need satisfaction (need supportive condition). This study aims to contribute to the literature by exploring the integration of need support for all three needs (the need for competence, relatedness and autonomy) in an ecologically valid setting. In what follows, the findings are discussed taking into account the COVID-19 affected circumstances in which the study took place.

As was hypothesized based on existing research (Costa et al., 2021 ), results showed significant learning gains on the LRST cognitive measure in both conditions, pointing out that the learning environments in general succeeded in improving students’ research skills. The current study did not find any significant differences in these learning gains between both conditions. Controlling for a priori differences between the conditions on the LRST pretest measure, students in the need support condition did exceed students in the baseline condition on the two-pager task. However, overall, the scores on the research skills task were quite low, pointing to the fact that students still seem to struggle in writing a research proposal. This task can be considered more complex (van Merriënboer & Kirschner, 2018 ) than the research skills test, as students are required to combine their conceptual and procedural knowledge in one assignment. Indeed, in the qualitative feedback, students indicate that they understand the theory and are able to apply the theory in basic exercises, but that they struggle in integrating their knowledge in a research proposal. Future research could set up more extensive interventions explicitly targeting students’ progress while writing a research proposal, for example using development portfolios (van Merriënboer et al., 2006 ).

The effect of the intervention on the motivational outcome measures was investigated. Since we experimentally manipulated need support, this study hypothesized that students in the need supportive condition would show higher scores for autonomous motivation, value/usefulness and need satisfaction; and lower scores for controlled motivation, amotivation and need frustration compared to students in the baseline condition (Deci & Ryan, 2000 ). However, the analyses showed that students in the conditions did not differ on the value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration and relatedness frustration measures. In contrast to what was hypothesized, students’ in the baseline condition reported higher relatedness satisfaction compared to students in the need supportive condition. No differences were found in students’ autonomous motivation and controlled motivation. However, as was expected, students in the need supportive conditions did report lower levels of amotivation compared to students in the baseline condition. Still, for the current study, one could question the role of the need support in this respect, as the current intervention did not succeed in manipulating students’ need experiences. In what follows, possible explanations for these findings are outlined in light of the existing literature.

Need experiences

A first observation based on the findings as described above is that the intervention did not succeed in manipulating students’ need satisfaction, need frustration and value/usefulness in an expected way. One effect was found of condition on relatedness satisfaction, but in the opposite direction of what was expected. We did not find a conclusive explanation for this unanticipated finding, but we do argue that the COVID-19 related measures at play during the intervention could have impacted this result. This will be reflected upon later in this discussion (limitations). In both conditions, students seem to be averagely satisfied regarding autonomy and competence in the 4C/ID based learning environments. This might be explained by the fact that 4C/ID based learning environments inherently foster students’ perceived competence because of the attention for structure and guidance, and the fact that the use of authentic tasks can be considered autonomy supportive (Bastiaens & Martens, 2007). However, we see that students experience low relatedness satisfaction in both conditions. The fact that the learning environment was organized entirely online might have influenced this result. While one might also partly address this low relatedness satisfaction to the COVID-19 circumstances at play during the study, this hypothetical explanation does not hold entirely since also in a previous non COVID-affected study in this research trajectory (Maddens et al., under review ), students’ relatedness satisfaction was found to be low. This finding, combined with findings from students’ qualitative feedback clearly indicating relatedness frustration, we argue that future research could focus on the question as how to provide need for relatedness support in 4C/ID based learning environments. On a more general level, this raises the question how opportunities for discussions and collaboration can be included in 4C/ID based learning environments. For example, organizing ‘real classroom interactions’ or performing assignments in groups (see also the suggestion of van Merriënboer & Kirschner, 2018 ), might be important in fostering students’ relatedness satisfaction (Salomon, 2002 ) . As argued by Wang et al. ( 2019 ), relatedness support is clearly understudied, for a long time often even ignored, in the SDT literature. Recently, relatedness is beginning to receive more attention, and has been found a strong predictor of autonomous motivation in the classroom (Wang et al., 2019 ).

Possibly, the need support provided in the learning environment was insufficient or inadequate to foster students’ need experiences. However, as the implementations were based on the existing literature (Deci & Ryan, 2000 ), this finding can be considered surprising. In addition, we derive from the qualitative feedback that students seem to value the need support provided in the learning environment. These contradictory observations are in line with previous research (Bastiaens et al., 2017 ), and call for further investigation.

Autonomous motivation, controlled motivation, amotivation

A second observation is that, in both conditions, students seem to hold low autonomous motivation and low controlled motivation towards learning research. On average, also students’ amotivation is low. The fact that students are not amotivated to learn about research can be considered reassuring. However, the fact that students experience low autonomous motivation causes concerns, as we know this might negatively impact their learning behavior and intentions to learn (Deci & Ryan, 2000 ; Wang et al., 2019 ). However, this result is based on mean scores. Future research might look at these results at student level, in order to identify individual motivational profiles (Vansteenkiste et al., 2009 ) and their prevalence in upper secondary behavioral sciences education.

A third observation is that students’ autonomous and controlled motivation were not affected by the intervention. Since the intervention did not succeed in manipulating students’ need experiences, this finding is not surprising. In addition, this is in line with Bastiaens et al.’ ( 2017 ) study, not finding motivational effects of providing need support in 4C/ID based learning environments. However, the current study did confirm that—although still higher than at pretest level, see below—students in the need supportive condition reported lower amotivation compared to students in the baseline condition. As no amotivational differences were observed at pretest level, this might indicate that students’ self-reported motivation (autonomous and controlled motivation) and/or needs do not align with students’ experienced motivation and needs. As was mentioned, this calls for further research.

Theoretical relationships

In line with previous research (Wang et al., 2019 ), multiple regression analyses revealed that students’ need satisfaction (on all three measures) contributed positively to students’ autonomous motivation. In addition, also students’ perceived value/usefulness contributed positively to students’ autonomous motivation. Students’ competence frustration and autonomy frustration contributed positively to students’ amotivation, and students’ value/usefulness contributed negatively to students’ amotivation. Students’ autonomy frustration contributed positively to students’ controlled motivation. While all the aforementioned relationships are in line with the expectations (Deci & Ryan, 2000 ; Wang et al., 2019 ), an unexpected finding is that students’ relatedness satisfaction contributed positively to students’ controlled motivation. This contradicts previous research (Wang et al., 2019 ), reporting that relatedness contributes to controlled motivation negatively. However, previous research (Wang et al., 2019 ) did find controlled motivation to be positively related to pressure . Although we did not find a conclusive explanation for this unanticipated finding, one possible reason thus is that students who contacted their peers in the online learning environment (and thus felt more related to their peers), might have experienced pressure because they felt like their peers worked faster or in a different way. Indeed, in the qualitative feedback, we noticed that some students indicated they ‘rushed’ through the online learning environment because they noticed a peer working faster. This finding calls for further research.

Overall, the results indicate that the observed need variables contributed most to students’ autonomous motivation, compared to (reversed relationships in) students’ amotivation and students’ controlled motivation. As such, when targeting students’ motivation, fostering students’ autonomous motivation based on students’ need experiences seems most promising. This is in line with previous research (Wang et al., 2019 ) reporting high correlations between students’ needs and students’ autonomous motivation, compared to students’ controlled motivation. We also investigated the relationships between students’ motivation and students’ cognitive outcomes. In line with a previously conducted study in this research trajectory (Maddens et al., under review ), but in contrast to what was hypothesized based on the existing literature (Deci & Ryan, 2000 ; Grolnick et al., 1991 ; Reeve, 2006 ) we found that nor students’ autonomous motivation, nor students’ controlled motivation contributed to students’ scores on the research skills test. However, we did find that students’ amotivation contributed negatively to students’ LRST scores. As such, when targeting students’ cognitive outcomes in educational programs, one might pay explicit attention to preventing amotivation. This is in line with previous research conducted in other domains, reporting that amotivation plays an important role in predicting mathematics achievement (Leroy & Bressoux, 2016 ), while this relationship was not found in other motivation types. Related to research skills, the current research suggests that preventing competence frustration and autonomy frustration, and fostering students’ experiences of value/usefulness might be especially promising to reach this goal.

Initially, we did not plan any analyses investigating the direct relationships between students’ needs and students’ cognitive outcomes, partly because previous research (Vallerand & Losier, 1999 ) suggests that the relationships between need satisfaction and (cognitive) outcomes are mediated by the types of motivation. To this end, we investigated the relationships between students’ needs and students’ motivation, separately from the relationships between students’ motivation and students’ cognitive outcomes. However, because of potential issues with the motivational measures (see earlier), which possibly hampers the interpretation of the relationships between students’ needs, students’ motivation, and students’ cognitive outcomes, we decided to also directly assess the regression weights of students’ needs and students’ perceived value/usefulness, on students’ cognitive outcomes. Results revealed that, in line with the expectations, students’ perceived value/usefulness contributed positively to students’ LRST scores and two-pager scores, which potentially stresses the importance of value/usefulness, not only for motivational purposes, but also for cognitive purposes. This is in line with previous research (Assor et al., 2002 ), establishing relationships between fostering relevance and students’ behavioral and cognitive engagement (which potentially leads to better cognitive outcomes). In contrast to the expectations, students’ relatedness satisfaction was found to be negatively related to students’ scores on the LRST and the two-pager. However, again, this surprising finding is best interpreted in light of the COVID-10 pandemic (see earlier).

Limitations

This study faced some reliability issues given the time frame in which the study took place. Due to the COVID-19-restrictions at play at the time of study, the study plan needed to be revised several times in collaboration with teachers in order to be able to complete the interventions. In addition, it is very likely that students’ motivation (and relatedness satisfaction) was influenced by the COVID 19-restrictions. For example, due to the restrictions, in the last phase of the intervention, students could only be present at school halftime, and therefore, some students worked from home while others worked in the classroom. In the qualitative feedback, students reported several COVID-19 related frustrations (it was too cold in class because teachers were obligated to open the windows; students needed to frequently disinfect their computers…). Also the teachers mentioned that students suffered from low well-being during the COVID-19 time frame (see further), and as such, this affected their motivation. Although all efforts were undertaken in order for the study to take place as controlled as possible, results should be interpreted in light of this time frame. The impact of the COVID-19 pandemic on students’ self-reported motivation has been established in recent research (Daniels et al., 2021 ). Overall, one could question to what extent we can expect an intervention at microlevel (manipulating need support in learning environments) to work, when the study takes place in a time frame where students’ need experiences are seriously threatened by the circumstances.

Decreasing motivation

Students’ motivation evolved in a non-desirable way in both conditions. This unexpected finding (decreasing motivation) might be explained by four possible reasons: a first explanation is that asking students to fill out the same questionnaire at posttest and pretest level might lead to frustration and lower reported motivation (Kosovich et al., 2017 ). Indeed, students spent a lot of time working in the online learning environment, so filling out another motivational questionnaire on top of the intervention might have added to the frustration (Kosovich et al., 2017 ). A second explanation is that students’ motivation naturally declines over time (which is a common finding in the motivational literature, Kosovich et al., 2017 ). A third explanation is that students, indeed, felt less motivated towards research skills after having completed the online learning environment. For example, the qualitative data indicated that a lot of students acknowledged the fact that the learning environment was useful, but that personally, they were not interested in learning the material. In addition, students indicated that the learning material was a lot to process in a short time frame, and was new to them, which might have negatively impacted their motivation. The latter (students indicating that the learning material was extensive) might indicate that students experienced high cognitive load (Paas & van Merriënboer, 1994; Sweller et al., 1994 ) while completing the learning environment. A fourth explanation is that, due to the COVID19-restrictions, students lost motivation during the learning process. A post-intervention survey in which we asked teachers about the impact of the COVID-19 restrictions on students’ motivation indicated that some students experienced low well-being during the COVID-19 pandemic, and thus, this might have hampered their motivation to learn. In addition, a teacher mentioned that COVID-19 in general was very demotivating for the students, and that students had troubles concentrating due to the fact they felt isolated. As was mentioned, the impact of COVID-19 on students’ motivation has been well described in the literature (Daniels et al., 2021 ). Although, in the current study, we cannot prove the impact of these measures on students’ motivation specifically towards learning research skills, it is important to take this context into account when interpreting the results.

Students’ learning behavior

Based on students’ qualitative feedback, we have reasons to believe that students did not always work in the learning environment as we would want them to do. Thus, students did not interact with the need support in the intended way (‘instructional disobedient behavior’: Elen, 2020 ). For example, several students reported that they did not always read all the material, did not make use of the forum, or did not notice certain messages from the researcher. However, the current research did not specifically look into students’ learning behavior in the learning environment. In learning environments organized online, future researchers might want to investigate students’ online behavior in order to gain insights in students’ interactions with the learning environment.

This study aims to contribute to theory and practice. Firstly, this study defines the 4C/ID model (van Merriënboer & Kirschner, 2018 ) as a good theoretical framework in order to design learning environments aiming to foster students’ research skills. However, this study also points to students’ struggling in writing a research proposal, which might lead to more specific intervention studies especially focussing on monitoring students’ progress while performing such tasks. Secondly, this study clearly elaborates on the operationalizations of need support used, and as such, might inform instructional designers in order to implement need support in an integrated manner (including competence, relatedness and autonomy support). Future interventions might want to track and monitor students’ learning behavior in order for students to interact with the learning environment as expected (Elen, 2020 ). Thirdly, this study established theoretical relationships between students’ needs, motivation and cognitive outcomes, which might be useful information for researchers aiming to investigate students’ motivation towards learning research skills in the future. Based on the findings, future researchers might especially involve in research fostering students’ autonomous motivation by means of providing need support; and avoiding students’ amotivation in order to enhance students’ cognitive outcomes. Suggestions are made based on the need support and frustration measures relating to these motivational and cognitive outcomes. For example, fostering students’ value/usefulness seems promising for both cognitive and motivational outcomes. Fourthly, although we did not succeed in manipulating students’ need experiences, we did gain insights in students’ experiences with the need support by means of the qualitative data. For example, the irreplaceable role of teachers in motivating students has been exposed. This study can be considered innovative because of its aim to inspect both students’ cognitive and motivational outcomes after completing a 4C/ID based educational program (van Merriënboer & Kirschner, 2018 ). In addition, this study implements integrated need support rather than focusing on a single need (Deci & Ryan, 2000 ; Sheldon & Filak, 2008 ).

Acknowledgements

This study was carried out within imec’s Smart Education research programme, with support from the Flemish government.

Appendix: Overview test instruments

• Instructions 2-pager (Maddens, Depaepe, Raes, & Elen, under review)

Write a research proposal for a fictional study.

In a Word-document of maximum two pages…

• You describe a research question and the importance of this research question
• You explain how you would answer this research question (manner of data collection and target group)
• You explain what your expectations are, and how you will report your results.

To do so, you receive 2 hours.

Post your research proposal here.

Good luck and thank you for your activity in the RISSC-environment!

Declarations

The authors declare that they have no conflict of interest.

All ethical and GDPR-related guidelines were followed as required for conducting human research and were approved by SMEC (Social and Societal Ethics Committee).

1 Fischer et al. ( 2014 ) refer to these research skills as scientific reasoning skills.

2 In Flanders, during the time of study, four different types of education are offered from the second stage of secondary education onwards (EACEA, 2018) (general secondary education, technical secondary education, secondary education in the arts and vocational secondary education). Behavioral sciences is a track in general secondary education.

3 For a complete overview on the design and the evaluation of this learning environment, see Maddens et al ( 2020b ).

4 During the time of study, the COVID-19 restrictions became more strict: students in upper secondary education could only come to school half of the time. Therefore, some students completed the last modules of the learning environment at home.

5 The BPNSNF-training scale is initially constructed to evaluate motivation related to workshops. The phrasing was adjusted slightly in order for the suitability for the current study. For example, we changed the wording ‘during the past workshop…’ to ‘while completing the online learning environment…’.

6 In the current study, we would label the items categorized as ‘intrinsic motivation’ in ASRS (finding something interesting, fun, fascinating or a pleasant activity) as ‘integration’. In SDT (Deci & Ryan, 2000 ; Deci et al., 2017 ), integration is described as being “fully volitional”, or “wholeheartedly engaged”, and it is argued that fully internalized extrinsic motivation does not typically become intrinsic motivation, but rather remains extrinsic even though fully volitional (because it is still instrumental). In the context of the current study, in which students learn about research skills because this is instructed (thus, out of instrumental motivations), we think that the term integration is more applicable than pure intrinsic motivation in self-initiated contexts (which can be observed for example in children’s play or in sports).

7 Levene’s test for homogeneity of variances was significant for the outcome “two-pager”. However, we continued with the analyses since the treatment group sizes are roughly equal, and thus, the assumption of homogeneity of variances does not need to be considered (Field, 2013 ). Levene’s test for homogeneity of variances was non-significant for all the other outcome measures.

8 Cohen’s D is calculated in SPSS by means of the formula: D = M 1 - M 2 Sp

Condition x autonomous motivation pretest Value/usefulness: p  = 0.251; autonomous motivation: p  = 0.269; controlled motivation: p  = 0.457; amotivation: p  = 0.219; autonomy satisfaction: p  = 0.794; autonomy frustration: p  = 0.096; competence satisfaction: p  = 0.682; competence frustration: p  = 0.699; relatedness satisfaction: p  = 0.943; relatedness frustration: p  = 0.870.

Condition x controlled motivation pretest Value/usefulness: p  = 0.882; autonomous motivation: p  = 0.270; controlled motivation: p  = 0.782; amotivation: p  = 0.940; autonomy satisfaction: p  = 0.815; autonomy frustration: p  = 0.737; competence satisfaction: p  = 0.649; competence frustration: p  = 0.505; relatedness satisfaction: p  = 0.625; relatedness frustration: p  = 0.741.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Aelterman N, Vansteenkiste M, Van Keer H, Haerens L. Changing teachers' beliefs regarding autonomy support and structure: The role of experienced psychological need satisfaction in teacher training. Psychology of Sport and Exercise. 2016; 23 :64–72. doi: 10.1016/j.psychsport.2015.10.007. [ CrossRef ] [ Google Scholar ]
• Assor A, Kaplan H, Roth G. Choice is good, but relevance is excellent: Autonomy-enhancing and suppressing teacher behaviours predicting students' engagement in schoolwork. British Journal of Educational Psychology. 2002; 72 (2):261–278. doi: 10.1348/000709902158883. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aydın S, Yerdelen S, Yalmancı SG, Göksu V. Academic motivation scale for learning biology: A scale development study. Education & Science/Egitim Ve Bilim. 2014; 39 (176):425–435. doi: 10.15390/EB.2014.3678. [ CrossRef ] [ Google Scholar ]
• Bastiaens E, van Merriënboer J, van Tilburg J. Research-based learning: Case studies from Maastricht University. Springer; 2017. Three educational models for positioning the Maastricht research-based learning programme; pp. 35–41. [ Google Scholar ]
• Braguglia KH, Jackson KA. Teaching research methodology using a project-based three course sequence critical reflections on practice. American Journal of Business Education (AJBE) 2012; 5 (3):347–352. doi: 10.19030/ajbe.v5i3.7007. [ CrossRef ] [ Google Scholar ]
• Butz NT, Stupnisky RH. Improving student relatedness through an online discussion intervention: The application of self-determination theory in synchronous hybrid programs. Computers & Education. 2017; 114 :117–138. doi: 10.1016/j.compedu.2017.06.006. [ CrossRef ] [ Google Scholar ]
• Chen B, Vansteenkiste M, Beyers W, Boone L, Deci EL, Van der Kaap-Deeder J, Verstuyf J. Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion. 2015; 39 (2):216–236. doi: 10.1007/s11031-014-9450-1. [ CrossRef ] [ Google Scholar ]
• Chi MT. Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science. 2009; 1 (1):73–105. doi: 10.1111/j.17568765.2008.01005.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook DA, McDonald FS. E-learning: Is there anything special about the" e"? Perspectives in Biology and Medicine. 2008; 51 (1):5–21. doi: 10.1353/pbm.2008.0007. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Costa JM, Miranda GL, Melo M. Four-component instructional design (4C/ID) model: A meta-analysis on use and effect. Learning Environments Research. 2021 doi: 10.1007/s10984-021-09373-y. [ CrossRef ] [ Google Scholar ]
• Daniels LM, Goegan LD, Parker PC. The impact of COVID-19 triggered changes to instruction and assessment on university students’ self-reported motivation, engagement and perceptions. Social Psychology of Education. 2021; 24 (1):299–318. doi: 10.1007/s11218-021-09612-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• de Jong T. Scaffolds for scientific discovery learning. In: Elen J, Clark RE, editors. Handling complexity in learning environments: Theory and research. Emerald Group Publishing Limited; 2006. pp. 107–128. [ Google Scholar ]
• de Jong T, van Joolingen WR. Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research. 1998; 68 (2):179–201. doi: 10.3102/00346543068002179. [ CrossRef ] [ Google Scholar ]
• Deci EL, Eghrari H, Patrick BC, Leone DR. Facilitating internalization: The self-determination theory perspective. Journal of Personality. 1994; 62 :119–142. doi: 10.1111/j.1467-6494.1994.tb00797.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Deci EL, Olafsen AH, Ryan RM. Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior. 2017; 4 :19–43. doi: 10.1146/annurev-orgpsych-032516-113108. [ CrossRef ] [ Google Scholar ]
• Deci EL, Ryan RM. The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry. 2000; 11 (4):227–268. doi: 10.1207/S15327965PLI1104_01. [ CrossRef ] [ Google Scholar ]
• Deci EL, Ryan RM, Williams GC. Need satisfaction and the self-regulation of learning. Learning and Individual Differences. 1996; 8 (3):165–183. doi: 10.1016/S1041-6080(96)90013-8. [ CrossRef ] [ Google Scholar ]
• Earley MA. A synthesis of the literature on research methods education. Teaching in Higher Education. 2014; 19 (3):242–253. doi: 10.1080/13562517.2013.860105. [ CrossRef ] [ Google Scholar ]
• Elen J. “Instructional disobedience”: A largely neglected phenomenon deserving more systematic research attention. Educational Technology Research and Development. 2020; 68 (5):2021–2032. doi: 10.1007/s11423-020-09776-3. [ CrossRef ] [ Google Scholar ]
• Engelmann K, Neuhaus BJ, Fischer F. Fostering scientific reasoning in education: Meta-analytic evidence from intervention studies. Educational Research and Evaluation. 2016; 22 (5–6):333–349. doi: 10.1080/13803611.2016.1240089. [ CrossRef ] [ Google Scholar ]
• Field A. Discovering statistics using IBM SPSS statistics. SAGE Publications; 2013. [ Google Scholar ]
• Fischer F, Chinn CA, Engelmann K, Osborne J. Scientific reasoning and argumentation. Routledge; 2018. [ Google Scholar ]
• Fischer F, Kollar I, Ufer S, Sodian B, Hussmann H, Pekrun R, Neuhaus B, Dorner B, Pankofer S, Fischer M, Strijbos J-W, Heene M, Eberle J. Scientific reasoning and argumentation: Advancing an interdisciplinary research agenda in education. Frontline Learning Research. 2014; 4 :28–45. doi: 10.14786/flr.v2i2.96. [ CrossRef ] [ Google Scholar ]
• Grolnick WS, Ryan RM, Deci EL. Inner resources for school achievement: Motivational mediators of children's perceptions of their parents. Journal of Educational Psychology. 1991; 83 (4):508–517. doi: 10.1037/0022-0663.83.4.508. [ CrossRef ] [ Google Scholar ]
• Kosovich JJ, Hulleman CS, Barron KE. Measuring motivation in educational settings: A Case for pragmatic measurement. In: Renninger KA, Hidi SE, editors. The Cambridge handbook on motivation and learning. Cambridge University Press; 2017. pp. 39–60. [ Google Scholar ]
• Lehti S, Lehtinen E. Computer-supported problem-based learning in the research methodology domain. Scandinavian Journal of Educational Research. 2005; 49 (3):297–324. doi: 10.1080/00313830500109618. [ CrossRef ] [ Google Scholar ]
• Leroy N, Bressoux P. Does amotivation matter more than motivation in predicting mathematics learning gains? A longitudinal study of sixth-grade students in France. Contemporary Educational Psychology. 2016; 44 :41–53. doi: 10.1016/j.cedpsych.2016.02.001. [ CrossRef ] [ Google Scholar ]
• Lesterhuis M, van Daal T, van Gasse R, Coertjens L, Donche V, de Maeyer S (2018) When teachers compare argumentative texts: Decisions informed by multiple complex aspects of text quality. L1 Educational Studies in Language and Literature, 18: 1–22. 10.17239/L1ESLL-2018.18.01.02
• Maddens L, Depaepe F, Janssen R, Raes A, Elen J. Evaluating the Leuven research skills test for 11th and 12th grade. Journal of Psychoeducational Assessment. 2020; 38 (4):445–459. doi: 10.1177/0734282918825040. [ CrossRef ] [ Google Scholar ]
• Maddens L, Depaepe F, Raes A, Elen J. The instructional design of a 4C/ID-inspired learning environment for upper secondary school students' research skills. International Journal of Designs for Learning. 2020; 11 (3):126–147. doi: 10.14434/ijdl.v11i3.29012. [ CrossRef ] [ Google Scholar ]
• Maddens, L., Depaepe, F., Raes, A., & Elen, J. (under review). Fostering students’ motivation towards learning research skills in upper secondary school behavioral sciences education: the role of autonomy support.
• Martin N, Kelly N, Terry P. A framework for self-determination in massive open online courses: Design for autonomy, competence, and relatedness. Australasian Journal of Educational Technology. 2018 doi: 10.14742/ajet.3722. [ CrossRef ] [ Google Scholar ]
• Merrill MD. First principles of instruction. Educational Technology Research and Development. 2002; 50 (3):43–59. doi: 10.1007/BF02505024. [ CrossRef ] [ Google Scholar ]
• Murtonen, M. S. S. (2005). Learning of quantitative research methods: University students' views, motivation and difficulties in learning. Doctoral Dissertation.
• Niemiec CP, Ryan RM. Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education. 2009; 7 (2):133–144. doi: 10.1177/2F1477878509104318. [ CrossRef ] [ Google Scholar ]
• Pietersen C. Research as a learning experience: A phenomenological explication. The Qualitative Report. 2002; 7 (2):1–14. doi: 10.46743/2160-3715/2002.1980. [ CrossRef ] [ Google Scholar ]
• Raes A, Schellens T. Unraveling the motivational effects and challenges of web-based collaborative inquiry learning across different groups of learners. Educational Technology Research and Development. 2015; 63 (3):405–430. doi: 10.1007/s11423-015-9381-x. [ CrossRef ] [ Google Scholar ]
• Reeve J. Extrinsic rewards and inner motivation. In: Evertson CM, Weinstein CS, editors. Handbook of classroom management: Research, practice, and contemporary issues. Lawrence Erlbaum Associates Publishers; 2006. pp. 645–664. [ Google Scholar ]
• Reeve J, Jang H. What teachers say and do to support students' autonomy during a learning activity. Journal of Educational Psychology. 2006; 98 (1):209–218. doi: 10.1037/0022-0663.98.1.209. [ CrossRef ] [ Google Scholar ]
• Reeve J, Jang H, Hardre P, Omura M. Providing a rationale in an autonomy-supportive way as a strategy to motivate others during an uninteresting activity. Motivation and Emotion. 2002; 26 (3):183–207. doi: 10.1023/A:1021711629417. [ CrossRef ] [ Google Scholar ]
• Ringeisen, T., & Bürgermeister, A. (2015). Fostering students’ self-efficacy in presentation skills: The effect of autonomy, relatedness and competence support. In Stress and anxiety: Application to schools, well-being, coping and internet use , 77–87.
• Ryan RM. Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology. 1982; 43 :450–461. doi: 10.1037/0022-3514.43.3.450. [ CrossRef ] [ Google Scholar ]
• Ryan RM. Psychological needs and the facilitation of integrative processes. Journal of Personality. 1995; 63 :397–427. doi: 10.1111/j.1467-6494.1995.tb00501.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ryan RM, Grolnick WS. Origins and pawns in the classroom: Self-report and projective assessments of individual differences in children’s perceptions. Journal of Personality and Social Psychology. 1986; 50 :550–558. doi: 10.1037/0022-3514.50.3.550. [ CrossRef ] [ Google Scholar ]
• Salomon G. Technology and pedagogy: Why don't we see the promised revolution? Educational Technology. 2002; 42 (2):71–75. [ Google Scholar ]
• Schunk DH. Self-efficacy for reading and writing: Influence of modeling, goal setting, and self-evaluation. Reading & Writing Quarterly. 2003; 19 (2):159–172. doi: 10.1080/10573560308219. [ CrossRef ] [ Google Scholar ]
• Sheldon KM, Filak V. Manipulating autonomy, competence, and relatedness support in a game-learning context: New evidence that all three needs matter. British Journal of Social Psychology. 2008; 47 (2):267–283. doi: 10.1348/014466607X238797. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steingut RR, Patall EA, Trimble SS. The effect of rationale provision on motivation and performance outcomes: A meta-analysis. Motivation Science. 2017; 3 (1):19–50. doi: 10.1037/mot0000039. [ CrossRef ] [ Google Scholar ]
• Sweller J. Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction. 1994; 4 (4):295–312. doi: 10.1016/0959-4752(94)90003-5. [ CrossRef ] [ Google Scholar ]
• Vallerand RJ. Advances in experimental social psychology. Academic Press; 1997. Toward a hierarchical model of intrinsic and extrinsic motivation; pp. 271–360. [ Google Scholar ]
• Vallerand RJ, Losier GF. An integrative analysis of intrinsic and extrinsic motivation in sport. Journal of Applied Sport Psychology. 1999; 11 (1):142–169. doi: 10.1080/10413209908402956. [ CrossRef ] [ Google Scholar ]
• Vallerand RJ, Reid G. On the causal effects of perceived competence on intrinsic motivation: A test of cognitive evaluation theory. Journal of Sport Psychology. 1984; 6 :94–102. doi: 10.1123/jsp.6.1.94. [ CrossRef ] [ Google Scholar ]
• Van Merriënboer JJG, Kirschner PA. Ten steps to complex learning. Routledge; 2018. [ Google Scholar ]
• van Merriënboer J, Sluijsmans D, Corbalan G, Kalyuga S, Paas F, Tattersall C. Performance assessment and learning task selection in environments for complex learning. In: Elen J, Clark RE, editors. Handling complexity in learning environments: Theory and Research. Elsevier Science Ltd; 2006. [ Google Scholar ]
• Vansteenkiste M, Ryan RM, Soenens B. Basic psychological need theory: Advancements, critical themes, and future directions. Motivation and Emotion. 2020; 44 :1–31. doi: 10.1007/s11031-019-09818-1. [ CrossRef ] [ Google Scholar ]
• Vansteenkiste M, Sierens E, Goossens L, Soenens B, Dochy F, Mouratidis A, Beyers W. Identifying configurations of perceived teacher autonomy support and structure: Associations with self-regulated learning, motivation and problem behavior. Learning and Instruction. 2012; 22 (6):431–439. doi: 10.1016/j.learninstruc.2012.04.002. [ CrossRef ] [ Google Scholar ]
• Vansteenkiste M, Sierens E, Soenens B, Luyckx K, Lens W. Motivational profiles from a self-determination perspective: The quality of motivation matters. Journal of Educational Psychology. 2009; 101 (3):671–688. doi: 10.1037/a0015083. [ CrossRef ] [ Google Scholar ]
• Wang CJ, Liu WC, Kee YH, Chian LK. Competence, autonomy, and relatedness in the classroom: Understanding students’ motivational processes using the self-determination theory. Heliyon. 2019; 5 (7):e01983. doi: 10.1016/j.heliyon.2019.e01983. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Utility Menu

• ARC Scheduler
• Student Employment
• Effective Learning Practices

Learning at college requires processing and retaining a high volume of information across various disciplines and subjects at the same time, which can be a daunting task, especially if the information is brand new. In response, college students try out varied approaches to their learning – often drawing from their high school experiences and modeling what they see their peers doing. While it’s great to try different styles and approaches to learning and studying for your courses, it's smart to incorporate into your daily habits some learning practices that are backed up by current research. 

Below are some effective learning practices suggested by research in the cognitive and learning sciences:

Take ownership of your educational experience.

As an engaged learner, it is important to take an active, self-directed role in your academic experience. Taking agency might feel new to you. In high school, you might have felt like you had little control over your learning experience, so transitioning to an environment where you are implicitly expected to be in the driver’s seat can be disorienting. 

A shift in your mindset regarding your agency, however, can make a big difference in your ability to learn effectively and get the results you want out of your courses.  

Here are four concrete actions you can take to assert ownership over your education :

• Attend office hours . Come prepared with questions for your instructor about lectures, readings, or other aspects of the course. 
• Schedule meetings with administrators and faculty  to discuss your academic trajectory and educational goals. You might meet with your academic adviser, course heads, or the Director of Undergraduate Studies (DUS) in your concentration.
• Identify areas for growth and development  based on your academic goals. Then, explore opportunities to shape and further refine your skills in those areas.
• Advocate  for support, tools, equipment, or considerations that address your learning needs.

Seek out opportunities for active learning.

Many courses include opportunities for active and engaged learning within their structure. Take advantage of those opportunities in order to enhance your understanding of the material. If such opportunities are not built into the course structure, you can develop your own active learning strategies, including joining study groups and using other active studying techniques. Anytime you grapple actively with your course material, rather than taking it in passively, you’re engaging in active learning. By doing so, you are increasing your retention of key course concepts.

One particularly effective way to help yourself stay focused and engaged in the learning process is to cultivate learning communities, such as accountability groups and study groups. Working in the company of other engaged learners can help remind you why you love learning or why you chose a particular course, concentration, research project, or field of study. Those reminders can re-energize and refocus your efforts. 

Practice study strategies that promote deep learning.

In an attempt to keep up with the demands of college, many students learn concepts just in time for assessment benchmarks (tests, exams, and quizzes). The problem with this methodology is that, for many disciplines (and especially in STEM), the concepts build on one another. Students survive the course only to be met at the final with concepts from the first quiz that they have forgotten long ago. This is why deep learning is important. Deep learning occurs when students use study strategies that ensure course ideas and concepts are embedded into long-term, rather than just short-term, memory. Building your study plans and review sessions in a way that helps create a conceptual framing of the material will serve you now and in the long run. 

Here are some study strategies that promote deep learning: 

Concept Mapping : A concept map is a visualization of knowledge that is organized by the relationships between the topics. At its core, it is made of concepts that are connected together by lines (or arrows) that are labeled with the relationship between the concepts. 

Collaboration : You don’t have to go it alone. In fact, research on learning suggests that it’s best not to. Using study groups, ARC accountability hours, office hours, question centers, and other opportunities to engage with your peers helps you not only test your understanding but also learn different approaches to tackling the material.

Self-test : Quiz yourself about the material you need to know with your notes put away. Refamiliarize yourself with the answers to questions you get wrong, wait a few hours, and then try asking yourself again. Use practice tests provided by your courses or use free apps to create quizzes for yourself.

Create a connection : As you try to understand how all the concepts and ideas from your course fit together, try to associate new information with something you already know. Making connections can help you create a more holistic picture of the material you’re learning. 

Teach someone (even yourself!) : Try teaching someone the concept you’re trying to remember. You can even try to talk to yourself about it! Vocalizing helps activate different sensory processes, which can enhance memory and help you embed concepts more deeply.

Interleave : We often think we’ll do best if we study one subject for long periods of time, but research contradicts this. Try to work with smaller units of time (a half-hour to an hour) and switch up your subjects. Return to concepts you studied earlier at intervals to ensure you learned them sufficiently.

Be intentional about getting started and avoiding procrastination.

When students struggle to complete tasks and projects, their procrastination is not because of laziness, but rather because of the anxiety and negative emotions that accompany starting the task. Understanding what conditions promote or derail your intention to begin a task can help you avoid procrastinating.

Consider the following tips for getting started: 

Eat the Frog : The frog is that one thing you have on your to-do list that you have absolutely no motivation to do and that you’re most likely to procrastinate on. Eating the frog means to just do it, as the first thing you do, and get it over with. If you don’t, odds are that you’ll procrastinate all day. With that one task done, you will experience a sense of accomplishment at the beginning of your day and gain some momentum that will help you move through the rest of your tasks.

Pomodoro Technique : Sometimes, we can procrastinate because we’re overwhelmed by the sheer amount of time we expect it will take to complete a task. But, while it might feel hard to sit down for several hours to work on something, most of us feel we can easily work for a half hour on almost any task. Enter the Pomodoro Technique! When faced with any large task or series of tasks, break the work down into short, timed intervals (25 minutes or so) that are spaced out by short breaks (5 minutes). Working in short intervals trains your brain to focus for manageable periods of time and helps you stay on top of deadlines. With time, the Pomodoro Technique can even help improve your attention span and concentration. Pomodoro is a cyclical system. You work in short sprints, which makes sure you’re consistently productive. You also get to take regular breaks that bolster your motivation and get you ready for your next pomodoro.

Distraction Pads : Sometimes we stop a task that took us a lot of time to get started on because we get distracted by something else. To avoid this, have a notepad beside you while working, and every time you get distracted with a thought, write it down, then push it aside for later. Distracting thoughts can be anything from remembering that you still have another assignment to complete to daydreaming about your next meal. Later on in the day, when you have some free time, you can review your distraction pad to see if any of those thoughts are important and need to be addressed.

Online Apps : It can be hard to rely on our own force of will to get ourselves to start a task, so consider using an external support. There are many self-control apps available for free online (search for "self-control apps"). Check out a few and decide on one that seems most likely to help you eliminate the distractions that can get in the way of starting and completing your work. 

Engage in metacognition.

An effective skill for learning is metacognition. Metacognition is the process of “thinking about thinking” or reflecting on personal habits, knowledge, and approaches to learning. Engaging in metacognition enables students to become aware of what they need to do to initiate and persist in tasks, to evaluate their own learning strategies, and to invest the adequate mental effort to succeed. When students work at being aware of their own thinking and learning, they are more likely to recognize patterns and to intentionally transfer knowledge and skills to solve increasingly complex problems. They also develop a greater sense of self-efficacy.

Mentally checking in with yourself while you study is a great metacognitive technique for assessing your level of understanding. Asking lots of “why,” “how,” and “what” questions about the material you’re reviewing helps you to be reflective about your learning and to strategize about how to tackle tricky material. If you know something, you should be able to explain to yourself how you know it. If you don’t know something, you should start by identifying exactly what you don’t know and determining how you can find the answer.

Metacognition is important in helping us overcome illusions of competence (our brain’s natural inclination to think that we know more than we actually know). All too often students don’t discover what they really know until they take a test. Metacognition helps you be a better judge of how well you understand your course material, which then enables you to refine your approach to studying and better prepare for tests.

Accordion style

• Assessing Your Understanding
• Building Your Academic Support System
• Common Class Norms
• First-Year Students
• How to Prepare for Class
• Interacting with Instructors
• Know and Honor Your Priorities
• Memory and Attention
• Minimizing Zoom Fatigue
• Note-taking
• Office Hours
• Perfectionism
• Scheduling Time
• Senior Theses
• Study Groups
• Tackling STEM Courses
• Test Anxiety

Tips for Online Students , Tips for Students

The Best Research Skills For Success

Updated: December 8, 2023

Published: January 5, 2020

Every student is required to conduct research in their academic careers at one point or another. A good research paper not only requires a great deal of time, but it also requires complex skills. Research skills include the ability to organize, evaluate, locate, and extract relevant information.

Let’s learn how to develop great research skills for academic success.

What is Research?

We’ve all surely heard the term “research” endlessly. But do you really know what it means?

Research is a type of study that focuses on a specific problem and aims to solve it using scientific methods. Research is a highly systematic process that involves both describing, explaining, and predicting something.

Photo by  Startup Stock Photos  from  Pexels

What are research skills.

Research skills are what helps us answer our most burning questions, and they are what assist us in our solving process from A to Z, including searching, finding, collecting, breaking down, and evaluating the relevant information to the phenomenon at hand.

Research is the basis of everything we know — and without it, we’re not sure where we would be today! For starters, without the internet and without cars, that’s for sure.

Why are Research Skills Important?

Research skills come in handy in pretty much everything we do, and especially so when it comes to the workforce. Employers will want to hire you and compensate you better if you demonstrate a knowledge of research skills that can benefit their company.

From knowing how to write reports, how to notice competition, develop new products, identify customer needs, constantly learn new technologies, and improve the company’s productivity, there’s no doubt that research skills are of utter importance. Research also can save a company a great deal of money by first assessing whether making an investment is really worthwhile for them.

How to Get Research Skills

Now that you’re fully convinced about the importance of research skills, you’re surely going to want to know how to get them. And you’ll be delighted to hear that it’s really not so complicated! There are plenty of simple methods out there to gain research skills such as the internet as the most obvious tool.

Gaining new research skills however is not limited to just the internet. There are tons of books, such as Lab Girl by Hope Jahren, journals, articles, studies, interviews and much, much more out there that can teach you how to best conduct your research.

Utilizing Research Skills

Now that you’ve got all the tools you need to get started, let’s utilize these research skills to the fullest. These skills can be used in more ways than you know. Your research skills can be shown off either in interviews that you’re conducting or even in front of the company you’re hoping to get hired at .

It’s also useful to add your list of research skills to your resume, especially if it’s a research-based job that requires skills such as collecting data or writing research-based reports. Many jobs require critical thinking as well as planning ahead.

Career Paths that Require Research Skills

If you’re wondering which jobs actually require these research skills, they are actually needed in a variety of industries. Some examples of the types of work that require a great deal of research skills include any position related to marketing, science , history, report writing, and even the food industry.

Photo by  Abby Chung  from  Pexels

How students can improve research skills.

Perhaps you know what you have to do, but sometimes, knowing how to do it can be more of a challenge. So how can you as a student improve your research skills ?

1. Define your research according to the assignment

By defining your research and understanding how it relates to the specific field of study, it can give more context to the situation.

2. Break down the assignment

The most difficult part of the research process is actually just getting started. By breaking down your research into realistic and achievable parts, it can help you achieve your goals and stay systematic.

3. Evaluate your sources

While there are endless sources out there, it’s important to always evaluate your sources and make sure that they are reliable, based on a variety of factors such as their accuracy and if they are biased, especially if used for research purposes.

4. Avoid plagiarism

Plagiarism is a major issue when it comes to research, and is often misunderstood by students. IAs a student, it’s important that you understand what plagiarism really means, and if you are unclear, be sure to ask your teachers.

5. Consult and collaborate with a librarian

A librarian is always a good person to have around, especially when it comes to research. Most students don’t seek help from their school librarian, however, this person tends to be someone with a vast amount of knowledge when it comes to research skills and where to look for reliable sources.

6. Use library databases

There are tons of online library resources that don’t require approaching anyone. These databases are generally loaded with useful information that has something for every student’s specific needs.

7. Practice effective reading

It’s highly beneficial to practice effective reading, and there are no shortage of ways to do it. One effective way to improve your research skills it to ask yourself questions using a variety of perspectives, putting yourself in the mind of someone else and trying to see things from their point of view.

There are many critical reading strategies that can be useful, such as making summaries from annotations, and highlighting important passages.

Thesis definition

A thesis is a specific theory or statement that is to be either proved or maintained. Generally, the intentions of a thesis are stated, and then throughout, the conclusions are proven to the reader through research. A thesis is crucial for research because it is the basis of what we are trying to prove, and what guides us through our writing.

What Skills Do You Need To Be A Researcher?

One of the most important skills needed for research is independence, meaning that you are capable of managing your own work and time without someone looking over you.

Critical thinking, problem solving, taking initiative, and overall knowing how to work professionally in front of your peers are all crucial for effectively conducting research .

1. Fact check your sources

Knowing how to evaluate information in your sources and determine whether or not it’s accurate, valid or appropriate for the specific purpose is a first on the list of research skills.

2. Ask the right questions

Having the ability to ask the right questions will get you better search results and more specific answers to narrow down your research and make it more concise.

3. Dig deeper: Analyzing

Don’t just go for the first source you find that seems reliable. Always dig further to broaden your knowledge and make sure your research is as thorough as possible.

4. Give credit

Respect the rights of others and avoid plagiarizing by always properly citing your research sources.

5. Utilize tools

There are endless tools out there, such as useful websites, books, online videos, and even on-campus professionals such as librarians that can help. Use all the many social media networks out there to both gain and share more information for your research.

6. Summarizing

Summarizing plays a huge role in research, and once the data is collected, relevant information needs to be arranged accordingly. Otherwise it can be incredibly overwhelming.

7. Categorizing

Not only does information need to be summarized, but also arranged into categories that can help us organize our thoughts and break down our materials and sources of information.

Photo by  Noelle Otto  from  Pexels

What are different types of research, 1. qualitative.

This type of research is exploratory research and its aim is to obtain a better understanding of reasons for things. Qualitative research helps form an idea without any specific fixed pattern. Some examples include face-to-face interviews or group discussions.

2. Quantitative

Quantitative research is based on numbers and statistics. This type of research uses data to prove facts, and is generally taken from a large group of people.

3. Analytical

Analytical research has to always be done from a neutral point of view, and the researcher is intended to break down all perspectives. This type of research involves collecting information from a wide variety of sources.

4. Persuasive

Persuasive research describes an issue from two different perspectives, going through both the pros and cons of both, and then aims to prove their preference towards one side by exploring a variety of logical facts.

5. Cause & Effect

In this type of research, the cause and effects are first presented, and then a conclusion is made. Cause and effect research is for those who are new in the field of research and is mostly conducted by high school or college students.

6. Experimental Research

Experimental research involves very specific steps that must be followed, starting by conducting an experiment. It is then followed by sharing an experience and providing data about it. This research is concluded with data in a highly detailed manner.

7. Survey Research

Survey research includes conducting a survey by asking participants specific questions, and then analyzing those findings. From that, researchers can then draw a conclusion.

8. Problem-Solution Research

Both students and scholars alike carry out this type of research, and it involves solving problems by analyzing the situation and finding the perfect solution to it.

What it Takes to Become a Researcher

• Critical thinking

Research is most valuable when something new is put on the table. Critical thinking is needed to bring something unique to our knowledge and conduct research successfully.

• Analytical thinking

Analytical thinking is one of the most important research skills and requires a great deal of practice. Such a skill can assist researchers in taking apart and understanding a large amount of important information in a short amount of time.

• Explanation skills

When it comes to research skills, it’s not just about finding information, but also about how you explain it. It’s more than just writing it out, but rather, knowing how to clearly and concisely explain your new ideas.

• Patience is key

Just like with anything in life, patience will always take you far. It might be difficult to come by, but by not rushing things and investing the time needed to conduct research properly, your work is bound for success.

• Time management

Time is the most important asset that we have, and it can never be returned back to us. By learning time management skills , we can utilize our time in the best way possible and make sure to always be productive in our research.

What You Need to Sharpen Your Research Skills

Research is one of the most important tasks that students are given in college, and in many cases, it’s almost half of the academic grade that one is given.

As we’ve seen, there are plenty of things that you’ll need to sharpen your research skills — which mainly include knowing how to choose reliable and relevant sources, and knowing how to take them and make it your own. It’s important to always ask the right questions and dig deeper to make sure that you understood the full picture.

Related Articles

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

‘The scientist is not in the business of following instructions.’

Glimpse of next-generation internet

Science is making anti-aging progress. But do we want to live forever?

Lessons in learning.

Sean Finamore ’22 (left) and Xaviera Zime ’22 study during a lecture in the Science Center.

Photos by Kris Snibbe/Harvard Staff Photographer

Peter Reuell

Harvard Staff Writer

Study shows students in ‘active learning’ classrooms learn more than they think

For decades, there has been evidence that classroom techniques designed to get students to participate in the learning process produces better educational outcomes at virtually all levels.

And a new Harvard study suggests it may be important to let students know it.

The study , published Sept. 4 in the Proceedings of the National Academy of Sciences, shows that, though students felt as if they learned more through traditional lectures, they actually learned more when taking part in classrooms that employed so-called active-learning strategies.

Lead author Louis Deslauriers , the director of science teaching and learning and senior physics preceptor, knew that students would learn more from active learning. He published a key study in Science in 2011 that showed just that. But many students and faculty remained hesitant to switch to it.

“Often, students seemed genuinely to prefer smooth-as-silk traditional lectures,” Deslauriers said. “We wanted to take them at their word. Perhaps they actually felt like they learned more from lectures than they did from active learning.”

In addition to Deslauriers, the study is authored by director of sciences education and physics lecturer Logan McCarty , senior preceptor in applied physics Kelly Miller, preceptor in physics Greg Kestin , and Kristina Callaghan, now a physics lecturer at the University of California, Merced.

The question of whether students’ perceptions of their learning matches with how well they’re actually learning is particularly important, Deslauriers said, because while students eventually see the value of active learning, initially it can feel frustrating.

“Deep learning is hard work. The effort involved in active learning can be misinterpreted as a sign of poor learning,” he said. “On the other hand, a superstar lecturer can explain things in such a way as to make students feel like they are learning more than they actually are.”

To understand that dichotomy, Deslauriers and his co-authors designed an experiment that would expose students in an introductory physics class to both traditional lectures and active learning.

For the first 11 weeks of the 15-week class, students were taught using standard methods by an experienced instructor. In the 12th week, half the class was randomly assigned to a classroom that used active learning, while the other half attended highly polished lectures. In a subsequent class, the two groups were reversed. Notably, both groups used identical class content and only active engagement with the material was toggled on and off.

Following each class, students were surveyed on how much they agreed or disagreed with statements such as “I feel like I learned a lot from this lecture” and “I wish all my physics courses were taught this way.” Students were also tested on how much they learned in the class with 12 multiple-choice questions.

When the results were tallied, the authors found that students felt as if they learned more from the lectures, but in fact scored higher on tests following the active learning sessions. “Actual learning and feeling of learning were strongly anticorrelated,” Deslauriers said, “as shown through the robust statistical analysis by co-author Kelly Miller, who is an expert in educational statistics and active learning.”

Those results, the study authors are quick to point out, shouldn’t be interpreted as suggesting students dislike active learning. In fact, many studies have shown students quickly warm to the idea, once they begin to see the results. “In all the courses at Harvard that we’ve transformed to active learning,” Deslauriers said, “the overall course evaluations went up.”

Co-author Kestin, who in addition to being a physicist is a video producer with PBS’ NOVA, said, “It can be tempting to engage the class simply by folding lectures into a compelling ‘story,’ especially when that’s what students seem to like. I show my students the data from this study on the first day of class to help them appreciate the importance of their own involvement in active learning.”

McCarty, who oversees curricular efforts across the sciences, hopes this study will encourage more of his colleagues to embrace active learning.

“We want to make sure that other instructors are thinking hard about the way they’re teaching,” he said. “In our classes, we start each topic by asking students to gather in small groups to solve some problems. While they work, we walk around the room to observe them and answer questions. Then we come together and give a short lecture targeted specifically at the misconceptions and struggles we saw during the problem-solving activity. So far we’ve transformed over a dozen classes to use this kind of active-learning approach. It’s extremely efficient — we can cover just as much material as we would using lectures.”

A pioneer in work on active learning, Balkanski Professor of Physics and Applied Physics Eric Mazur hailed the study as debunking long-held beliefs about how students learn.

“This work unambiguously debunks the illusion of learning from lectures,” he said. “It also explains why instructors and students cling to the belief that listening to lectures constitutes learning. I recommend every lecturer reads this article.”

Dean of Science Christopher Stubbs , Samuel C. Moncher Professor of Physics and of Astronomy, was an early convert. “When I first switched to teaching using active learning, some students resisted that change. This research confirms that faculty should persist and encourage active learning. Active engagement in every classroom, led by our incredible science faculty, should be the hallmark of residential undergraduate education at Harvard.”

Ultimately, Deslauriers said, the study shows that it’s important to ensure that neither instructors nor students are fooled into thinking that lectures are the best learning option. “Students might give fabulous evaluations to an amazing lecturer based on this feeling of learning, even though their actual learning isn’t optimal,” he said. “This could help to explain why study after study shows that student evaluations seem to be completely uncorrelated with actual learning.”

This research was supported with funding from the Harvard FAS Division of Science.

Share this article

You might like.

George Whitesides became a giant of chemistry by keeping it simple

Physicists demo first metro-area quantum computer network in Boston

Nobel laureate details new book, which surveys research, touches on larger philosophical questions

Epic science inside a cubic millimeter of brain

Researchers publish largest-ever dataset of neural connections

Finding right mix on campus speech policies

Legal, political scholars discuss balancing personal safety, constitutional rights, academic freedom amid roiling protests, cultural shifts

Good genes are nice, but joy is better

Harvard study, almost 80 years old, has proved that embracing community helps us live longer, and be happier

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Learn More Effectively

10 Learning Techniques to Try

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Knowing the most effective strategies for how to learn can help you maximize your efforts when trying to acquire new ideas, concepts, and skills. If you are like many people, your time is limited, so it is important to get the most educational value out of the time you have. Speed of learning is not the only important factor, however.

It is also important to be able to accurately remember the information that you learn, recall it at a later time, and use it effectively in a wide variety of situations. How can you teach yourself to learn? As you approach a new subject, incorporate some of the following tactics:

• Find ways to boost your memory
• Always keep learning new things
• Use a variety of learning techniques
• Try teaching it to someone else
• Connect new information to things you already know
• Look for opportunities to have hands-on experiences
• Remember that mistakes are part of the process
• Study a little bit every day
• Test yourself
• Focus on one thing at a time

Knowing how to learn well doesn't happen overnight, but putting a few of these learning techniques into daily practice can help you get more out of your study time .

Improve Your Memory

There are a number of different strategies that can boost memory . Basic tips such as improving your focus, avoiding cram sessions, and structuring your study time are good places to start, but there are even more lessons from psychology that can dramatically improve your learning efficiency.

If you're wondering how to learn better by improving your memory, these strategies can help:

• Getting regular physical exercise , which is linked to improvements in memory and brain health
• Spending time socializing with other people
• Getting enough sleep
• Eliminating distractions so you can focus on what you are learning
• Organizing the information you are studying to make it easier to remember
• Using elaborative rehearsal when studying; when you learn something new, spend a few moments describing it to yourself in your own words
• Using visual aids like photographs, graphs, and charts
• Reading the information you are studying out loud

For example, you might use general learning techniques like setting aside quiet time to study, rehearsing, and reading information aloud. You might combine this with strategies that can foster better memory, such as exercising and socializing.

If you're pressed for time, consider combining study strategies. Listen to a podcast while taking a walk or join a group where you can practice your new skills with others.

Keep Learning New Things

Prasit photo / Getty Images

One surefire way to become a more effective learner is to simply keep learning. Research has found that the brain is capable of producing new brain cells , a process known as neurogenesis. However, many of these cells will eventually die unless a person engages in some type of effortful learning.

By learning new things, these cells are kept alive and incorporated into brain circuits.

If you want to learn a new language, for instance, it is important to keep practicing the language to maintain the gains you have achieved. This "use-it-or-lose-it" phenomenon involves a brain process known as "pruning."

In pruning, certain pathways in the brain are maintained while others are eliminated. If you want the new information you just learned to stay put, keep practicing and rehearsing it.

Learn in Multiple Ways

Another good "how to learn" strategy is to focus on learning in more than one way. For example, instead of just listening to a podcast, which involves auditory learning, find a way to rehearse the information both verbally and visually.

This might involve describing what you learned to a friend, taking notes , or drawing a mind map. By learning in more than one way, you’re further cementing the knowledge in your mind.

For example, if you are trying to pick up a new language, try varying learning techniques such as listening to language examples, reading written language, practicing with a friend, and writing down your own notes.

One helpful tip is to try writing your notes on paper rather than typing on a laptop, tablet, or computer. Research has found that longhand notes can help cement information in memory more effectively than digital note-taking.

Varying your learning techniques and giving yourself the opportunity to learn in different ways and in different contexts can help make you a more efficient learner.

Teach What You Are Learning

Educators have long noted that one of the best ways to learn something is to teach it to someone else. Remember your seventh-grade presentation on Costa Rica? By teaching to the rest of the class, your teacher hoped you would gain even more from the assignment.

You can apply the same principle today by sharing newly learned skills and knowledge with others. Start by translating the information into your own words. This process alone helps solidify new knowledge in your brain. Next, find some way to share what you’ve learned.

Some ideas include writing a blog post, creating a podcast, or participating in a group discussion.

Build on Previous Learning

Tara Moore\ / Getty Images

Another great way to become a more effective learner is to use relational learning. This involves relating new information to things that you already know.

For example, if you are learning a new language, you might associate the new vocabulary and grammar you are learning with what you already know about your native language or other languages you may already speak.

Gain Practical Experience

LWA / Dann Tardif / Getty Images

For many students, learning typically involves reading textbooks, attending lectures, or doing research in the library or online. While seeing information and then writing it down is important, actually putting new knowledge and skills into practice can be one of the best ways to improve learning.

If it is a sport or athletic skill, perform the activity on a regular basis. If you are learning a new language, practice speaking with another person and surround yourself with language-immersion experiences. Watch foreign-language films and strike up conversations with native speakers to practice your budding skills.

If you are trying to acquire a new skill or ability, focus on gaining practical experience.

Don't Be Afraid to Make Mistakes

Research suggests that making mistakes when learning can improve learning outcomes. According to one study, trial-and-error learning where the mistakes were close to the actual answer was actually a helpful part of the learning process.

Another study found that mistakes followed by corrective feedback can be beneficial to learning. So if you make a mistake when learning something new, spend some time correcting the mistake and examining how you arrived at the incorrect answer.

This strategy can help foster critical thinking skills and make you more adaptable in learning situations that require being able to change your mind.

Research suggests that making mistakes when learning can actually help improve outcomes, especially if you correct your mistake and take the time to understand why it happened.

Use Distributed Practice

David Schaffer / Getty Images

Another strategy for how to learn better is known as distributed practice. Instead of trying to cram all of your learning into a few long study sessions, try a brief, focused session, and then take a break.

So if you were learning a new language, you might devote a period of time to an intensive session of studying. After a break, you would then come back and rehearse your previous learning while also extending it to new learning.

This process of returning for brief sessions over a long period of time is one of the best ways to learn efficiently and effectively.  

What is the best way to learn?

Research suggests that this type of distributed learning is one of the most effective learning techniques. Focus on spending a little time studying each topic every day.

While it may seem that spending more time studying is one of the best ways to maximize learning, research has demonstrated that taking tests actually helps you better remember what you've learned—even if the topic wasn't covered on the test.

This phenomenon, known as the testing effect, suggests that spending time retrieving information from memory improves the long-term memory of that information. This retrieval practice makes it more likely that you will be able to remember that information again in the future.

Stop Multitasking

For many years, it was thought that people who multitask had an edge over those who did not. However, research now suggests that multitasking can actually make learning less effective.

Multitasking can involve trying to do more than one thing at the same time. But it can also involve quickly switching back and forth between tasks or trying to rapidly perform tasks one after the other. 

According to research, doing this not only makes people less productive when they work but also impairs attention and reduces comprehension. Multitasking when you are studying makes it harder to focus on the information and reduces how much you understand it.

Research has also found that media multitasking, or dividing attention between different media sources, can also have a detrimental impact on learning and academic performance.

To avoid the pitfalls of multitasking, focus your attention on the task at hand and continue working for a predetermined amount of time.

If you want to know how to learn, it is important to explore learning techniques that have been shown to be effective. Strategies such as boosting your memory and learning in multiple ways can be helpful. Regularly learning new things, using distributed practice, and testing yourself often can also be helpful ways to become a more efficient learner.

This process can take time, and it always takes practice and determination to establish new habits . Start by focusing on just a few of these tips to see if you can get more out of your next study session.

Perhaps most importantly, work on developing the mindset that you are capable of improving your knowledge and skills. Research suggests that believing in your own capacity for growth is one of the best ways to take advantage of the learning opportunities you pursue.

Chaire A, Becke A, Düzel E. Effects of physical exercise on working memory and attention-related neural oscillations . Front Neurosci . 2020;14:239. doi:10.3389/fnins.2020.00239

Mazza S, Gerbier E, Gustin M-P, et al. Relearn faster and retain longer: Along with practice, sleep makes perfect . Psychol Sci. 2016;27(10):1321-1330. doi:10.1177/0956797616659930

University of North Carolina at Chapel Hill. Memorization strategies .

Forrin ND, Macleod CM.  This time it's personal: the memory benefit of hearing oneself .  Memory.  2018;26(4):574-579. doi:10.1080/09658211.2017.1383434

Cunnington R. Neuroplasticity: How the brain changes with learning . IBE - UNESCO.

Mueller PA, Oppenheimer DM. The pen Is mightier than the keyboard: Advantages of longhand over laptop note taking . Psychol Sci . 2014. 2014;25(6):1159-1168. doi:10.1177/0956797614524581

Cyr AA, Anderson ND. Learning from your mistakes: does it matter if you’re out in left foot, I mean field? Memory . 2018;26(9):1281-1290. doi:10.1080/09658211.2018.1464189

Metcalfe J. Learning from errors . Ann Rev Psychol . 2017;68(1):465-489. doi:10.1146/annurev-psych-010416-044022

Kang SHK. Spaced repetition promotes efficient and effective learning: Policy implications for instruction . Policy Insights Behav Brain Sci . 2016;3(1):12-19. doi:10.1177/2372732215624708

Pastotter B, Bauml KHT. Retrieval practice enhances new learning: the forward effect of testing . Front Psychol . 2014;5:286. doi:10.3389/fpsyg.2014.00286

Jeong S-H, Hwang Y.  Media multitasking effects on cognitive vs. attitudinal outcomes: A meta-analysis .  Hum Commun Res . 2016;42(4):599-618. doi:10.1111/hcre.12089

May K, Elder A. Efficient, helpful, or distracting? A literature review of media multitasking in relation to academic performance . Int J Educ Technol High Educ.  2018;15(1):13. doi:10.1186/s41239-018-0096-z

Sarrasin JB, Nenciovici L, Foisy LMB, Allaire-Duquette G, Riopel M, Masson S. Effects of teaching the concept of neuroplasticity to induce a growth mindset on motivation, achievement, and brain activity: A meta-analysis . Trends Neurosci Educ . 2018;12:22-31. doi:10.1016/j.tine.2018.07.003

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Learning Skills Research Foundation

• About us
• Contact us

Identifying and sharing new developments in learning, promoting their application by connecting ideas with practice

Learning skills research foundation is an independent charitable organisation devoted to improving the quality of learning. it raises funds for scientific research, public lectures and policy development in the revolutionary field of educational neuroscience and for other academic research programmes that will lead to the discovery of new learning techniques. we focus on ‘how people learn’ as opposed to ‘what people learn’. we are closely engaged with the centre for educational neuroscience (cen) and others working in the fields of neuroscience and education with the principal aim of providing a bridge between the latest academic discoveries about how the brain functions and the teaching community. we work to promote vital dialogue that will enhance understanding of the underlying processes of how brains encode learning of new information and concepts. this will allow us to shape education for all in the future based on universal, evidence-based principles rather than constantly changing theories and policies of the day. our aim is to come up with practical solutions and we have established learnus® as a think tank to pave the way., the late dame helen alexander dbe, chancellor of the university of southampton, privacy policy, learning skills research foundation will not rent, sell, share or disseminate personal information about you with other people or non-affiliated companies and organizations., equity, diversity and inclusion policy, learning skills research foundation is committed to treating all people equally.....

• Top Courses
• Online Degrees
• Find your New Career
• Join for Free

What Is a Data Scientist? Salary, Skills, and How to Become One

A data scientist uses data to understand and explain the phenomena around them, and help organizations make better decisions.

Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new technological advances. Data scientists have become more common and in demand, as big data continues to be increasingly important to the way organizations make decisions. Here’s a closer look at what they are and do—and how to become one.

Ready to become a data scientist?

Enroll in a course risk-free with a 7-day trial of Coursera Plus . The subscription gives you access to hundreds of courses—including the IBM Data Science Professional Certificate . Start exploring and building skills to see if it's the right career fit for you.

What does a data scientist do?

Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting.

A data scientist might do the following tasks on a day-to-day basis:

Find patterns and trends in datasets to uncover insights

Create algorithms and data models to forecast outcomes

Use machine learning techniques to improve the quality of data or product offerings

Communicate recommendations to other teams and senior staff

Deploy data tools such as Python , R , SAS, or SQL in data analysis

Stay on top of innovations in the data science field

Data analyst vs data scientist: What’s the difference?

The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organizations to make better decisions about operations. But data scientists tend to have more responsibility and are generally considered more senior than data analysts. 

Data scientists are often expected to form their own questions about the data, while data analysts might support teams that already have set goals in mind. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data.

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

Dip your toe into data analytics

Many data scientists can begin their careers as data analysts or statisticians. You might want to start by exploring the popular Google Data Analytics Professional Certificate to learn how to prepare, clean, process, and analyze data. Enroll today with a 7-day trial of Coursera Plus to try it out.

Data scientist salary and job growth

A data scientist earns an average salary of $108,659 in the United States, according to Lightcast™ [1]. 

Demand is high for data professionals—data scientists occupations are expected to grow by 36 percent in the next 10 years (much faster than average), according to the US Bureau of Labor Statistics (BLS) [ 2 ].

The high demand has been linked to the rise of big data and its increasing importance to businesses and other organizations. 

How to become a data scientist

Becoming a data scientist generally requires some formal training. Here are some steps to consider.

1. Earn a data science degree.

Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field.

Already have a bachelor's degree?

Consider getting a master’s in data science. At a master’s degree program, you can dive deeper into your understanding of statistics, machine learning, algorithms, modeling, and forecasting, and potentially conduct your own research on a topic you care about. Several data science master’s degrees are available online .

2. Sharpen relevant skills.

If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. Here are some of the skills you’ll want to have under your belt.

Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:

Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:

Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.

Big data: Some employers may want to see that you have some familiarity in grappling with big data. Some of the software frameworks used to process big data include Hadoop and Apache Spark.

Communication: The most brilliant data scientists won’t be able to affect any change if they aren’t able to communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill for data scientists.

Watch this video for a preview of IBM's data science course:

3. Get an entry-level data analytics job.

Though there are many paths to becoming a data scientist, starting in a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst , business intelligence analyst , statistician, or data engineer . From there, you can work your way up to becoming a scientist as you expand your knowledge and skills.

4. Prepare for data science interviews.

With a few years of experience working with data analytics, you might feel ready to move into data science. Once you’ve scored an interview, prepare answers to likely interview questions. 

Data scientist positions can be highly technical, so you may encounter technical and behavioral questions. Anticipate both, and practice by speaking your answer aloud. Preparing examples from your past work or academic experiences can help you appear confident and knowledgeable to interviewers.

Here are a few questions you might encounter:

What are the pros and cons of a linear model?

What is a random forest?

How would you use SQL to find all duplicates in a data set?

Describe your experience with machine learning.

Give an example of a time you encountered a problem you didn’t know how to solve. What did you do?

Read more: SQL Interview Questions: A Guide for Data Analysts

As with the other courses I took on Coursera, this program strengthened my portfolio and helped me in my career. — Mo R ., on taking the IBM Data Science Professional Certificate

Learn data science with IBM

With IBM's Data Science Professional Certificate , build the skills and knowledge you need to become a data scientist. This comprehensive course can lay down a strong foundation for your career. You might also be interested in starting out as a data analyst and starting your journey with the Google Data Analytics Professional Certificate . Explore for free with a 7-day trial of Coursera Plus .

Article sources

Lightcast™ Analyst. "Occupation Summary for Data Scientist." Accessed April 13, 2023.

US Bureau of Labor Statistics. " Data Scientists , https://www.bls.gov/ooh/math/data-scientists.htm." Accessed April 13, 2023.

Keep reading

Coursera staff.

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Along with Stanford news and stories, show me:

• Student information
• Faculty/Staff information

We want to provide announcements, events, leadership messages and resources that are relevant to you. Your selection is stored in a browser cookie which you can remove at any time using “Clear all personalization” below.

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

How Does Writing Fit Into the ‘Science of Reading’?

• Share article

In one sense, the national conversation about what it will take to make sure all children become strong readers has been wildly successful: States are passing legislation supporting evidence-based teaching approaches , and school districts are rushing to supply training. Publishers are under pressure to drop older materials . And for the first time in years, an instructional issue—reading—is headlining education media coverage.

In the middle of all that, though, the focus on the “science of reading” has elided its twin component in literacy instruction: writing.

Writing is intrinsically important for all students to learn—after all, it is the primary way beyond speech that humans communicate. But more than that, research suggests that teaching students to write in an integrated fashion with reading is not only efficient, it’s effective.

Yet writing is often underplayed in the elementary grades. Too often, it is separated from schools’ reading block. Writing is not assessed as frequently as reading, and principals, worried about reading-exam scores, direct teachers to focus on one often at the expense of the other. Finally, beyond the English/language arts block, kids often aren’t asked to do much writing in early grades.

“Sometimes, in an early-literacy classroom, you’ll hear a teacher say, ‘It’s time to pick up your pencils,’” said Wiley Blevins, an author and literacy consultant who provides training in schools. “But your pencils should be in your hand almost the entire morning.”

Strikingly, many of the critiques that reading researchers have made against the “balanced literacy” approach that has held sway in schools for decades could equally apply to writing instruction: Foundational writing skills—like phonics and language structure—have not generally been taught systematically or explicitly.

And like the “find the main idea” strategies commonly taught in reading comprehension, writing instruction has tended to focus on content-neutral tasks, rather than deepening students’ connections to the content they learn.

Education Week wants to bring more attention to these connections in the stories that make up this special collection . But first, we want to delve deeper into the case for including writing in every step of the elementary curriculum.

Why has writing been missing from the reading conversation?

Much like the body of knowledge on how children learn to read words, it is also settled science that reading and writing draw on shared knowledge, even though they have traditionally been segmented in instruction.

“The body of research is substantial in both number of studies and quality of studies. There’s no question that reading and writing share a lot of real estate, they depend on a lot of the same knowledge and skills,” said Timothy Shanahan, an emeritus professor of education at the University of Illinois Chicago. “Pick your spot: text structure, vocabulary, sound-symbol relationships, ‘world knowledge.’”

The reasons for the bifurcation in reading and writing are legion. One is that the two fields have typically been studied separately. (Researchers studying writing usually didn’t examine whether a writing intervention, for instance, also aided students’ reading abilities—and vice versa.)

Some scholars also finger the dominance of the federally commissioned National Reading Panel report, which in 2000 outlined key instructional components of learning to read. The review didn’t examine the connection of writing to reading.

Looking even further back yields insights, too. Penmanship and spelling were historically the only parts of writing that were taught, and when writing reappeared in the latter half of the 20th century, it tended to focus on “process writing,” emphasizing personal experience and story generation over other genres. Only when the Common Core State Standards appeared in 2010 did the emphasis shift to writing about nonfiction texts and across subjects—the idea that students should be writing about what they’ve learned.

And finally, teaching writing is hard. Few studies document what preparation teachers receive to teach writing, but in surveys, many teachers say they received little training in their college education courses. That’s probably why only a little over half of teachers, in one 2016 survey, said that they enjoyed teaching writing.

Writing should begin in the early grades

These factors all work against what is probably the most important conclusion from the research over the last few decades: Students in the early-elementary grades need lots of varied opportunities to write.

“Students need support in their writing,” said Dana Robertson, an associate professor of reading and literacy education at the school of education at Virginia Tech who also studies how instructional change takes root in schools. “They need to be taught explicitly the skills and strategies of writing and they need to see the connections of reading, writing, and knowledge development.”

While research supports some fundamental tenets of writing instruction—that it should be structured, for instance, and involve drafting and revising—it hasn’t yet pointed to a specific teaching recipe that works best.

One of the challenges, the researchers note, is that while reading curricula have improved over the years, they still don’t typically provide many supports for students—or teachers, for that matter—for writing. Teachers often have to supplement with additions that don’t always mesh well with their core, grade-level content instruction.

“We have a lot of activities in writing we know are good,” Shanahan said. “We don’t really have a yearlong elementary-school-level curriculum in writing. That just doesn’t exist the way it does in reading.”

Nevertheless, practitioners like Blevins work writing into every reading lesson, even in the earliest grades. And all the components that make up a solid reading program can be enhanced through writing activities.

4 Key Things to Know About How Reading and Writing Interlock

Want a quick summary of what research tells us about the instructional connections between reading and writing?

1. Reading and writing are intimately connected.

Research on the connections began in the early 1980s and has grown more robust with time.

Among the newest and most important additions are three research syntheses conducted by Steve Graham, a professor at the University of Arizona, and his research partners. One of them examined whether writing instruction also led to improvements in students’ reading ability; a second examined the inverse question. Both found significant positive effects for reading and writing.

A third meta-analysis gets one step closer to classroom instruction. Graham and partners examined 47 studies of instructional programs that balanced both reading and writing—no program could feature more than 60 percent of one or the other. The results showed generally positive effects on both reading and writing measures.

2. Writing matters even at the earliest grades, when students are learning to read.

Studies show that the prewriting students do in early education carries meaningful signals about their decoding, spelling, and reading comprehension later on. Reading experts say that students should be supported in writing almost as soon as they begin reading, and evidence suggests that both spelling and handwriting are connected to the ability to connect speech to print and to oral language development.

3. Like reading, writing must be taught explicitly.

Writing is a complex task that demands much of students’ cognitive resources. Researchers generally agree that writing must be explicitly taught—rather than left up to students to “figure out” the rules on their own.

There isn’t as much research about how precisely to do this. One 2019 review, in fact, found significant overlap among the dozen writing programs studied, and concluded that all showed signs of boosting learning. Debates abound about the amount of structure students need and in what sequence, such as whether they need to master sentence construction before moving onto paragraphs and lengthier texts.

But in general, students should be guided on how to construct sentences and paragraphs, and they should have access to models and exemplars, the research suggests. They also need to understand the iterative nature of writing, including how to draft and revise.

A number of different writing frameworks incorporating various degrees of structure and modeling are available, though most of them have not been studied empirically.

4. Writing can help students learn content—and make sense of it.

Much of reading comprehension depends on helping students absorb “world knowledge”—think arts, ancient cultures, literature, and science—so that they can make sense of increasingly sophisticated texts and ideas as their reading improves. Writing can enhance students’ content learning, too, and should be emphasized rather than taking a back seat to the more commonly taught stories and personal reflections.

Graham and colleagues conducted another meta-analysis of nearly 60 studies looking at this idea of “writing to learn” in mathematics, science, and social studies. The studies included a mix of higher-order assignments, like analyses and argumentative writing, and lower-level ones, like summarizing and explaining. The study found that across all three disciplines, writing about the content improved student learning.

If students are doing work on phonemic awareness—the ability to recognize sounds—they shouldn’t merely manipulate sounds orally; they can put them on the page using letters. If students are learning how to decode, they can also encode—record written letters and words while they say the sounds out loud.

And students can write as they begin learning about language structure. When Blevins’ students are mainly working with decodable texts with controlled vocabularies, writing can support their knowledge about how texts and narratives work: how sentences are put together and how they can be pulled apart and reconstructed. Teachers can prompt them in these tasks, asking them to rephrase a sentence as a question, split up two sentences, or combine them.

“Young kids are writing these mile-long sentences that become second nature. We set a higher bar, and they are fully capable of doing it. We can demystify a bit some of that complex text if we develop early on how to talk about sentences—how they’re created, how they’re joined,” Blevins said. “There are all these things you can do that are helpful to develop an understanding of how sentences work and to get lots of practice.”

As students progress through the elementary grades, this structured work grows more sophisticated. They need to be taught both sentence and paragraph structure , and they need to learn how different writing purposes and genres—narrative, persuasive, analytical—demand different approaches. Most of all, the research indicates, students need opportunities to write at length often.

Using writing to support students’ exploration of content

Reading is far more than foundational skills, of course. It means introducing students to rich content and the specialized vocabulary in each discipline and then ensuring that they read, discuss, analyze, and write about those ideas. The work to systematically build students’ knowledge begins in the early grades and progresses throughout their K-12 experience.

Here again, available evidence suggests that writing can be a useful tool to help students explore, deepen, and draw connections in this content. With the proper supports, writing can be a method for students to retell and analyze what they’ve learned in discussions of content and literature throughout the school day —in addition to their creative writing.

This “writing to learn” approach need not wait for students to master foundational skills. In the K-2 grades especially, much content is learned through teacher read-alouds and conversation that include more complex vocabulary and ideas than the texts students are capable of reading. But that should not preclude students from writing about this content, experts say.

“We do a read-aloud or a media piece and we write about what we learned. It’s just a part of how you’re responding, or sharing, what you’ve learned across texts; it’s not a separate thing from reading,” Blevins said. “If I am doing read-alouds on a concept—on animal habitats, for example—my decodable texts will be on animals. And students are able to include some of these more sophisticated ideas and language in their writing, because we’ve elevated the conversations around these texts.”

In this set of stories , Education Week examines the connections between elementary-level reading and writing in three areas— encoding , language and text structure , and content-area learning . But there are so many more examples.

Please write us to share yours when you’ve finished.

Want to read more about the research that informed this story? Here’s a bibliography to start you off.

Berninger V. W., Abbott, R. D., Abbott, S. P., Graham S., & Richards T. (2002). Writing and reading: Connections between language by hand and language by eye. J ournal of Learning Disabilities. Special Issue: The Language of Written Language, 35(1), 39–56 Berninger, Virginia, Robert D. Abbott, Janine Jones, Beverly J. Wolf, Laura Gould, Marci Anderson-Younstrom, Shirley Shimada, Kenn Apel. (2006) “Early development of language by hand: composing, reading, listening, and speaking connections; three letter-writing modes; and fast mapping in spelling.” Developmental Neuropsychology, 29(1), pp. 61-92 Cabell, Sonia Q, Laura S. Tortorelli, and Hope K. Gerde (2013). “How Do I Write…? Scaffolding Preschoolers’ Early Writing Skills.” The Reading Teacher, 66(8), pp. 650-659. Gerde, H.K., Bingham, G.E. & Wasik, B.A. (2012). “Writing in Early Childhood Classrooms: Guidance for Best Practices.” Early Childhood Education Journal 40, 351–359 (2012) Gilbert, Jennifer, and Steve Graham. (2010). “Teaching Writing to Elementary Students in Grades 4–6: A National Survey.” The Elementary School Journal 110(44) Graham, Steve, et al. (2017). “Effectiveness of Literacy Programs Balancing Reading and Writing Instruction: A Meta-Analysis.” Reading Research Quarterly, 53(3) pp. 279–304 Graham, Steve, and Michael Hebert. (2011). “Writing to Read: A Meta-Analysis of the Impact of Writing and Writing Instruction on Reading.” Harvard Educational Review (2011) 81(4): 710–744. Graham, Steve. (2020). “The Sciences of Reading and Writing Must Become More Fully Integrated.” Reading Research Quarterly, 55(S1) pp. S35–S44 Graham, Steve, Sharlene A. Kiuhara, and Meade MacKay. (2020).”The Effects of Writing on Learning in Science, Social Studies, and Mathematics: A Meta-Analysis.” Review of Educational Research April 2020, Vol 90, No. 2, pp. 179–226 Shanahan, Timothy. “History of Writing and Reading Connections.” in Shanahan, Timothy. (2016). “Relationships between reading and writing development.” In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (2nd ed., pp. 194–207). New York, NY: Guilford. Slavin, Robert, Lake, C., Inns, A., Baye, A., Dachet, D., & Haslam, J. (2019). “A quantitative synthesis of research on writing approaches in grades 2 to 12.” London: Education Endowment Foundation. Troia, Gary. (2014). Evidence-based practices for writing instruction (Document No. IC-5). Retrieved from University of Florida, Collaboration for Effective Educator, Development, Accountability, and Reform Center website: http://ceedar.education.ufl.edu/tools/innovation-configuration/ Troia, Gary, and Steve Graham. (2016).“Common Core Writing and Language Standards and Aligned State Assessments: A National Survey of Teacher Beliefs and Attitudes.” Reading and Writing 29(9).

A version of this article appeared in the January 25, 2023 edition of Education Week as How Does Writing Fit Into the ‘Science of Reading’?

Sign Up for EdWeek Update

Edweek top school jobs.

Sign Up & Sign In

• Skip to main content
• Keyboard shortcuts for audio player

Your Health

• Treatments & Tests
• Health Inc.
• Public Health

Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

Is this some kind of joke? A school facing shortages starts teaching standup comedy

In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

Shots - Health News

Feeling artsy here's how making art helps your brain.

"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

• handwriting

Setting a new bar for online higher education

The education sector was among the hardest hit  by the COVID-19 pandemic. Schools across the globe were forced to shutter their campuses in the spring of 2020 and rapidly shift to online instruction. For many higher education institutions, this meant delivering standard courses and the “traditional” classroom experience through videoconferencing and various connectivity tools.

The approach worked to support students through a period of acute crisis but stands in contrast to the offerings of online education pioneers. These institutions use AI and advanced analytics to provide personalized learning and on-demand student support, and to accommodate student preferences for varying digital formats.

Colleges and universities can take a cue from the early adopters of online education, those companies and institutions that have been refining their online teaching models for more than a decade, as well as the edtechs that have entered the sector more recently. The latter organizations use educational technology to deliver online education services.

To better understand what these institutions are doing well, we surveyed academic research as well as the reported practices of more than 30 institutions, including both regulated degree-granting universities and nonregulated lifelong education providers. We also conducted ethnographic market research, during which we followed the learning journeys of 29 students in the United States and in Brazil, two of the largest online higher education markets in the world, with more than 3.3 million 1 Integrated Postsecondary Education Data System, 2018, nces.ed.gov. and 2.3 million 2 School Census, Censo Escolar-INEP, 2019, ensobasico.inep.gov.br. online higher education students, respectively.

We found that, to engage most effectively with students, the leading online higher education institutions focus on eight dimensions of the learning experience. We have organized these into three overarching principles: create a seamless journey for students, adopt an engaging approach to teaching, and build a caring network (exhibit). In this article, we talk about these principles in the context of programs that are fully online, but they may be just as effective within hybrid programs in which students complete some courses online and some in person.

Create a seamless journey for students

The performance of the early adopters of online education points to the importance of a seamless journey for students, easily navigable learning platforms accessible from any device, and content that is engaging, and whenever possible, personalized. Some early adopters have even integrated their learning platforms with their institution’s other services and resources, such as libraries and financial-aid offices.

1. Build the education road map

In our conversations with students and experts, we learned that students in online programs—precisely because they are physically disconnected from traditional classroom settings—may need more direction, motivation, and discipline than students in in-person programs. The online higher education  programs that we looked at help students build their own education road map using standardized tests, digital alerts, and time-management tools to regularly reinforce students’ progress and remind them of their goals.

Brazil’s Cogna Educação, for instance, encourages students to assess their baseline knowledge at the start of the course. 3 Digital transformation: A new culture to shape our future , Kroton 2018 Sustainability Report, Kroton Educacional, cogna.com.br. Such up-front diagnostics could be helpful in highlighting knowledge gaps and pointing students to relevant tools and resources, and may be especially helpful to students who have had unequal educational opportunities. A web-based knowledge assessment allows Cogna students to confirm their mastery of certain parts of a course, which, according to our research, can potentially boost their confidence and allow them to move faster through the course material.

At the outset of a course, leaders in online higher education can help students clearly understand the format and content, how they will use what they learn, how much time and effort is required, and how prepared they are for its demands.

The University of Michigan’s online Atlas platform, for instance, gives students detailed information about courses and curricula, including profiles of past students, sample reports and evaluations, and grade distributions, so they can make informed decisions about their studies. 4 Atlas, Center for Academic Innovation, University of Michigan, umich.edu. Another provider, Pluralsight, shares movie-trailer-style overviews of its course content and offers trial options so students can get a sense of what to expect before making financial commitments.

Meanwhile, some of the online doctoral students we interviewed have access to an interactive timeline and graduation calculator for each course, which help students understand each of the milestones and requirements for completing their dissertations. Breaking up the education process into manageable tasks this way can potentially ease anxiety, according to our interviews with education experts.

2. Enable seamless connections

Students may struggle to learn if they aren’t able to connect to learning platforms. Online higher education pioneers provide a single sign-on through which students can interact with professors and classmates and gain access to critical support services. Traditional institutions considering a similar model should remember that because high-speed and reliable internet are not always available, courses and program content should be structured so they can be accessed even in low-bandwidth situations or downloaded for offline use.

The technology is just one element of creating seamless connections. Since remote students may face a range of distractions, online-course content could benefit them by being more engaging than in-person courses. Online higher education pioneers allow students to study at their own pace through a range of channels and media, anytime and anywhere—including during otherwise unproductive periods, such as while in the waiting room at the doctor’s office. Coursera, for example, invites students to log into a personalized home page where they can review the status of their coursework, complete unfinished lessons, and access recommended “next content to learn” units. Brazilian online university Ampli Pitagoras offers content optimized for mobile devices that allows students to listen to lessons, contact tutors for help, or do quizzes from wherever they happen to be.

Adopt an engaging approach to teaching

The pioneers in online higher education we researched pair the “right” course content with the “right” formats to capture students’ attention. They incorporate real-world applications into their lesson plans, use adaptive learning tools to personalize their courses, and offer easily accessible platforms for group learning.

3. Offer a range of learning formats

The online higher education programs we reviewed incorporate group activities and collaboration with classmates—important hallmarks of the higher education experience—into their mix of course formats, offering both live classes and self-guided, on-demand lessons.

The Georgia Institute of Technology, for example, augments live lessons from faculty members in its online graduate program in data analytics with a collaboration platform where students can interact outside of class, according to a student we interviewed. Instructors can provide immediate answers to students’ questions via the platform or endorse students’ responses to questions from their peers. Instructors at Zhejiang University in China use live videoconferencing and chat rooms to communicate with more than 300 participants, assign and collect homework assignments, and set goals. 5 Wu Zhaohui, “How a top Chinese university is responding to coronavirus,” World Economic Forum, March 16, 2020, weforum.org.

The element of personalization is another area in which online programs can consider upping their ante, even in large student groups. Institutions could offer customized ways of learning online, whether via digital textbook, podcast, or video, ensuring that these materials are high quality and that the cost of their production is spread among large student populations.

Some institutions have invested in bespoke tools to facilitate various learning modes. The University of Michigan’s Center for Academic Innovation embeds custom-designed software into its courses to enhance the experience for both students and professors. 6 “Our mission & principles,” University of Michigan Center for Academic Innovation, ai.umich.edu. The school’s ECoach platform helps students in large classes navigate content when one-on-one interaction with instructors is difficult because of the sheer number of students. It also sends students reminders, motivational tips, performance reviews, and exam-preparation materials. 7 University of Michigan, umich.edu. Meanwhile, Minerva University focuses on a real-time online-class model that supports higher student participation and feedback and has built a platform with a “talk time” feature that lets instructors balance class participation and engage “back-row students” who may be inclined to participate less. 8 Samad Twemlow-Carter, “Talk Time,” Minerva University, minervaproject.com.

4. Ensure captivating experiences

Delivering education on digital platforms opens the potential to turn curricula into engaging and interactive journeys, and online education leaders are investing in content whose quality is on a par with high-end entertainment. Strayer University, for example, has recruited Emmy Award–winning film producers and established an in-house production unit to create multimedia lessons. The university’s initial findings show that this investment is paying off in increased student engagement, with 85 percent of learners reporting that they watch lessons from beginning to end, and also shows a 10 percent reduction in the student dropout rate. 9 Increased student engagement and success through captivating content , Strayer Studios outcomes report, Strayer University, studios.strategiced.com.

Other educators are attracting students not only with high-production values but influential personalities. Outlier provides courses in the form of high-quality videos that feature charismatic Ivy League professors and are shot in a format that reduces eye strain. 10 Outlier online course registration for Calculus I, outlier.org. The course content follows a storyline, and each course is presented as a crucial piece in an overall learning journey.

5. Utilize adaptive learning tools

Online higher education pioneers deliver adaptive learning using AI and analytics to detect and address individual students’ needs and offer real-time feedback and support. They can also predict students’ requirements, based on individuals’ past searches and questions, and respond with relevant content. This should be conducted according to the applicable personal data privacy regulations of the country where the institution is operating.

Cogna Educação, for example, developed a system that delivers real-time, personalized tutoring to more than 500,000 online students, paired with exercises customized to address specific knowledge gaps. 11 Digital transformation , 2018. Minerva University used analytics to devise a highly personalized feedback model, which allows instructors to comment and provide feedback on students’ online learning assignments and provide access to test scores during one-on-one feedback sessions. 12 “Maybe we need to rethink our assumptions about ‘online’ learning,” Minerva University, minervaproject.com. According to our research, instructors can also access recorded lessons during one-on-one sessions and provide feedback on student participation during class.

6. Include real-world application of skills

The online higher education pioneers use virtual reality (VR) laboratories, simulations, and games for students to practice skills in real-world scenarios within controlled virtual environments. This type of hands-on instruction, our research shows, has traditionally been a challenge for online institutions.

Arizona State University, for example, has partnered with several companies to develop a biology degree that can be obtained completely online. The program leverages VR technology that gives online students in its biological-sciences program access to a state-of-the-art lab. Students can zoom in to molecules and repeat experiments as many times as needed—all from the comfort of wherever they happen to be. 13 “ASU online biology course is first to offer virtual-reality lab in Google partnership,” Arizona State University, August 23, 2018, news.asu.edu. Meanwhile, students at Universidad Peruana de Ciencias Aplicadas are using 3-D games to find innovative solutions to real-world problems—for instance, designing the post-COVID-19 campus experience. 14 Cleofé Vergara, “Learn by playing with Minecraft Education,” Innovación Educativa, July 13, 2021, innovacioneducativa.upc.edu.pe.

Some institutions have expanded the real-world experience by introducing online internships. Columbia University’s Virtual Internship Program, for example, was developed in partnership with employers across the United States and offers skills workshops and resources, as well as one-on-one career counseling. 15 Virtual Internship Program, Columbia University Center for Career Education, columbia.edu.

Create a caring network

Establishing interpersonal connections may be more difficult in online settings. Leading online education programs provide dedicated channels to help students with academic, personal, technological, administrative, and financial challenges and to provide a means for students to connect with each other for peer-to-peer support. Such programs are also using technologies to recognize signs of student distress and to extend just-in-time support.

7. Provide academic and nonacademic support

Online education pioneers combine automation and analytics with one-on-one personal interactions to give students the support they need.

Southern New Hampshire University (SNHU), for example, uses a system of alerts and communication nudges when its digital platform detects low student engagement. Meanwhile, AI-powered chatbots provide quick responses to common student requests and questions. 16 “SNHU turns student data into student success,” Southern New Hampshire University, May 2019, d2l.com. Strayer University has a virtual assistant named Irving that is accessible from every page of the university’s online campus website and offers 24/7 administrative support to students, from recommending courses to making personalized graduation projections. 17 “Meet Irving, the Strayer chatbot that saves students time,” Strayer University, October 31, 2019, strayer.edu.

Many of these pioneer institutions augment that digital assistance with human support. SNHU, for example, matches students in distress with personal coaches and tutors who can follow the students’ progress and provide regular check-ins. In this way, they can help students navigate the program and help cultivate a sense of belonging. 18 Academic advising, Southern New Hampshire University, 2021, snhu.edu. Similarly, Arizona State University pairs students with “success coaches” who give personalized guidance and counseling. 19 “Accessing your success coach,” Arizona State University, asu.edu.

8. Foster a strong community

The majority of students we interviewed have a strong sense of belonging to their academic community. Building a strong network of peers and professors, however, may be challenging in online settings.

To alleviate this challenge, leading online programs often combine virtual social events with optional in-person gatherings. Minerva University, for example, hosts exclusive online events that promote school rituals and traditions for online students, and encourages online students to visit its various locations for in-person gatherings where they can meet members of its diverse, dispersed student population. 20 “Join your extended family,” Minerva University, minerva.edu. SNHU’s Connect social gateway gives online-activity access to more than 15,000 members, and helps them interact within an exclusive university social network. Students can also join student organizations and affinity clubs virtually. 21 SNHU Connect, Southern New Hampshire University, snhuconnect.com.

Getting started: Designing the online journey

Building a distinctive online student experience requires significant time, effort, and investment. Most institutions whose practices we reviewed in this article took several years to understand student needs and refine their approaches to online education.

For those institutions in the early stages of rethinking their online offerings, the following three steps may be useful. Each will typically involve various functions within the institution, including but not necessarily limited to, academic management, IT, and marketing.

The diagnosis could be performed through a combination of focus groups and quantitative surveys, for example. It’s important that participants represent various student segments, which are likely to have different expectations, including young-adult full-time undergraduate students, working-adult part-time undergraduate students, and graduate students. The eight key dimensions outlined above may be helpful for structuring groups and surveys, in addition to self-evaluation of institution performance and potential benchmarks.

• Set a strategic vision for your online learning experience. The vision should be student-centric and link tightly to the institution’s overarching manifesto. The function leaders could evaluate the costs/benefits of each part of the online experience to ensure that the costs are realistic. The online model may vary depending on each school’s market, target audience, and tuition price point. An institution with high tuition, for example, is more likely to afford and provide one-on-one live coaching and student support, while an institution with lower tuition may need to rely more on automated tools and asynchronous interactions with students.
• Design the transformation journey. Institutions should expect a multiyear journey. Some may opt to outsource the program design and delivery to dedicated program-management companies. But in our experience, an increasing number of institutions are developing these capabilities internally, especially as online learning moves further into the mainstream and becomes a source of long-term strategic advantage.

We have found that leading organizations often begin with quick wins that significantly raise student experiences, such as stronger student support, integrated technology platforms, and structured course road maps. In parallel, they begin the incremental redesign of courses and delivery models, often focusing on key programs with the largest enrollments and tapping into advanced analytics for insights to refine these experiences.

Finally, institutions tackle key enabling factors, such as instructor onboarding and online-teaching training, robust technology infrastructure, and advanced-analytics programs that enable the institutions to understand which features of online education are performing well and generating exceptional learning experiences for their students.

The question is no longer whether the move to online will outlive the COVID-19 lockdowns but when online learning will become the dominant means for delivering higher education. As digital transformation accelerates across all industries, higher education institutions will need to consider how to develop their own online strategies.

Felipe Child is a partner in McKinsey’s Bogotá office, Marcus Frank is a senior practice expert in the São Paulo office, Mariana Lef is an associate in the Buenos Aires office, and Jimmy Sarakatsannis is a partner in the Washington, DC, office.

References to specific products, companies, or organizations are solely for information purposes and do not constitute any endorsement or recommendation.

This article was edited by Justine Jablonska, an editor in the New York office.

Explore a career with us

Related articles.

How to transform higher-education institutions for the long term

Higher education in the post-COVID world

Reimagining higher education in the United States

'My research made me realize how fascinating psychology is'

5/17/2024 A&S Communications

Feifei (Lily) Ren

Psychology Beijing, China

What was your favorite class and why?  

My favorite class was environmental psychology with Professor Gary Evans because that class was inspirational to me on a personal level. It provided me answers that I was not able to understand by myself.

What are the most valuable skills you gained from your Arts & Sciences education?

My education here at A&S taught me professional communication skills and research skills that I will definitely be able to implement in my future careers.         

What have you accomplished as a Cornell student that you are most proud of?

I am very proud of my academic accomplishments. I was able to improve myself academically every semester, and I was part of many research projects and labs, including my honors thesis project. My research experience, especially my honors thesis project, made me realize how fascinating psychological research is, and inspired me to do more.  

If you were to offer advice to an incoming first year student, what would you say?

Try to be patient with your personal life and academic life! Try to take things easy and prioritize your personal wellbeing.

Every year, our faculty nominate graduating Arts & Sciences students to be featured as part of our Extraordinary Journeys series.  Read more about the Class of 202 4.

'It was rewarding to build something that people from the community enjoyed and learned from'

'I dream of investigating virtual reality and making an impact on the world'

'I'm a researcher in Cornell's Play and Learning Lab'

'I felt more empowered as a student and a woman'

Leveraging collective action and environmental literacy to address complex sustainability challenges

• Perspective
• Open access
• Published: 09 August 2022
• Volume 52 , pages 30–44, ( 2023 )

Cite this article

You have full access to this open access article

• Nicole M. Ardoin   ORCID: orcid.org/0000-0002-3290-8211 1 ,
• Alison W. Bowers 2 &
• Mele Wheaton 3  

8222 Accesses

18 Citations

20 Altmetric

Explore all metrics

Developing and enhancing societal capacity to understand, debate elements of, and take actionable steps toward a sustainable future at a scale beyond the individual are critical when addressing sustainability challenges such as climate change, resource scarcity, biodiversity loss, and zoonotic disease. Although mounting evidence exists for how to facilitate individual action to address sustainability challenges, there is less understanding of how to foster collective action in this realm. To support research and practice promoting collective action to address sustainability issues, we define the term “collective environmental literacy” by delineating four key potent aspects: scale, dynamic processes, shared resources, and synergy. Building on existing collective constructs and thought, we highlight areas where researchers, practitioners, and policymakers can support individuals and communities as they come together to identify, develop, and implement solutions to wicked problems. We close by discussing limitations of this work and future directions in studying collective environmental literacy.

Similar content being viewed by others

Bridge over troubled water: managing compatibility and conflict among thought collectives in sustainability science

“Salomone Sostenibile”: An Award to ‘Communicate’ the University’s Leading Role in Sustainable Development

Engaging with Ethnically Diverse Community Groups

Avoid common mistakes on your manuscript.

Introduction

For socio-ecologically intertwined issues—such as climate change, land conversion, biodiversity loss, resource scarcity, and zoonotic diseases—and their associated multi-decadal timeframes, individual action is necessary, yet not sufficient, for systemic, sustained change (Amel et al. 2017 ; Bodin 2017 ; Niemiec et al. 2020 ; Spitzer and Fraser 2020 ). Instead, collective action, or individuals working together toward a common good, is essential for achieving the scope and scale of solutions to current sustainability challenges. To support communities as they engage in policy and action for socio-environmental change, communicators, land managers, policymakers, and other practitioners need an understanding of how communities coalesce and leverage their shared knowledge, skills, connections, and experiences.

Engagement efforts, such as those grounded in behavior-change approaches or community-based social marketing initiatives, that address socio-environmental issues have often emphasized individuals as the pathway to change. Such efforts address a range of domains including, but not limited to, residential energy use, personal transportation choices, and workplace recycling efforts, often doing so in a stepwise fashion, envisioning each setting or suite of behaviors as discrete spheres of action and influence (Heimlich and Ardoin 2008 ; McKenzie-Mohr 2011 ). In this way, specific actions are treated incrementally and linearly, considering first the individual barriers to be removed and then the motivations to be activated (and, sometimes, sustained; Monroe 2003 ; Gifford et al. 2011 ). Once each behavior is successfully instantiated, the next barrier is then addressed. Proceeding methodically from one action to the next, such initiatives often quite successfully alter a series of actions or group of related behaviors (at least initially) by addressing them incrementally, one at a time (Byerly et al. 2018 ). Following this aspirational logic chain, many resources have been channeled into such programs under the assumption that, by raising awareness and knowledge, such information, communication, and educational outreach efforts will shift attitudes and behaviors to an extent that, ultimately, mass-scale change will follow. (See discussion in Wals et al. 2014 .)

Numerous studies have demonstrated, however, that challenges arise with these stepwise approaches, particularly with regard to their ability to address complex issues and persist over time (Heimlich and Ardoin 2008 ; Wals et al. 2014 ). Such approaches place a tremendous—and unrealistic—burden on individuals, ignoring key aspects not only of behavioral science but also of social science more broadly, including the view that humans exist nested within socio-ecological systems and, thus, are most successful at achieving lasting change when it is meaningful, relevant, and undertaken within a supportive context (Swim et al. 2011 ; Feola 2015 ). Individualized approaches often require multiple steps or nudges (Byerly et al. 2018 ), or ongoing reminders to retain their salience (Stern et al. 2008 ). Because of the emphasis on decontextualized action, such approaches can miss, ignore, obfuscate, or minimize the importance of the bigger picture, which includes the sociocultural, biophysical, and political economic contexts (Ardoin 2006 ; Amel et al. 2017 ). Although the tightly trained focus on small, actionable steps and reliance on individual willpower may help in initially achieving success with initial habit formation (Carden and Wood 2018 ), it becomes questionable in terms of bringing about a wave of transformation on larger scales in the longer term. For those decontextualized actions to persist, they require continued prompting, constancy, and support in the social and biophysical context (Schultz 2014 ; Manfredo et al. 2016 ; Wood and Rünger 2016 ).

Less common in practice are theoretically based initiatives that embrace the holistic nature of the human experience, which occurs within complex systems spanning time and space in a multidimensional, weblike fashion (Bronfenbrenner 1979 ; Rogoff 2003 ; Barron 2006 ; DeCaro and Stokes 2008 ; Gould et al. 2019 ; Hovardas 2020 ). These systems-thinking approaches, while varying across disciplines and epistemological perspectives, envision human experiences, including learning and behavior, as occurring within a milieu that include the social, political, cultural, and historical contexts (Rogoff 2003 ; Roth and Lee 2007 ; Swim et al. 2011 ; Gordon 2019 ). In such a view, people’s everyday practices continuously reflect and grow out of past learning and experiences, not only at the individual, but also at the collective level (Lave 1991 ; Gutiérrez and Rogoff 2003 ; Nasir et al. 2020 ; Ardoin and Heimlich 2021 ). The multidimensional context in which we exist—including the broader temporal and spatial ecosystem—both facilitates and constrains our actions.

Scholars across diverse areas of study discuss the need for and power of collective thought and action, using various conceptual frames, models, and terms, such as collective action, behavior, impact, and intelligence; collaborative governance; communities of practice; crowdsourcing; and social movement theory; among many others (Table 1 ). These scholars acknowledge and explore the influence of our multidimensional context on collective thought and action. In this paper, we explore the elements and processes that constitute collective environmental literacy . We draw on the vast, relevant literature and, in so doing, we attempt to invoke the power of the collective: by reviewing and synthesizing ideas from a variety of fields, we strive to leverage existing constructs and perspectives that explore notions of the “collective” (see Table 1 for a summary of constructs and theories reviewed to develop our working definition of collective environmental literacy). A primary goal of this paper is to dialogue with other researchers and practitioners working in this arena who are eager to uncover and further explore related avenues.

First, we present a formal definition of collective environmental literacy. Next, we briefly review the dominant view of environmental literacy at the individual level and, in support of a collective take on environmental literacy, we examine various collective constructs. We then delve more deeply into the definition of collective environmental literacy by outlining four key aspects: scale, dynamic processes, shared resources, and synergy. We conclude by providing suggestions for future directions in studying collective environmental literacy.

Defining collective environmental literacy

Decades of research in political science, economics, anthropology, sociology, psychology, and the learning sciences, among other fields (Chawla and Cushing 2007 ; Ostrom 2009 ; Sawyer 2014 ; Bamberg et al. 2015 ; Chan 2016 ; Jost et al. 2017 ) repeatedly demonstrates the effectiveness, and indeed necessity of, collective action when addressing problems that are inherently social in nature. Yet theoretical frameworks and empirical documentation emphasize that such collective activities rarely arise spontaneously and, when they do, are a result of preconditions that have sown fertile ground (van Zomeren et al. 2008 ; Duncan 2018 ). Persistent and effective collective action then requires scaffolding in the form of institutional, sociocultural, and political economic structure that provides ongoing support. To facilitate discussions of how to effectively support collective action around sustainability issues, we suggest the concept of “collective environmental literacy.” We conceptualize collective environmental literacy as more than collective action; rather, we suggest that the term encapsulates action along with its various supporting structures and resources. Additionally, we employ the word “literacy” as it connotes learning, intention, and the idea that knowledge, skills, attitudes, and behaviors can be enhanced iteratively over time. By using “literacy,” we strive to highlight the efforts, often unseen, that lead to effective collective action in communities. We draw on scholarship in science and health education, areas that have begun over the past two decades to theorize about related areas of collective science literacy (Roth and Lee 2002 , 2004 ; Lee and Roth 2003 ; Feinstein 2018 ) and health literacy (Freedman et al. 2009 ; Papen 2009 ; Chinn 2011 ; Guzys et al. 2015 ). Although these evolving constructs lack consensus definitions, they illuminate affordances and constraints that exist when conceptualizing collective environmental literacy (National Academies of Sciences, Engineering, and Medicine [NASEM] 2016 ).

Some of the key necessary—but not sufficient—conditions that facilitate aligned, collective actions include a common body of decision-making information; shared attitudes, values, and beliefs toward a motivating issue or concern; and efficacy skills that facilitate change-making (Sturmer and Simon 2004 ; van Zomeren et al. 2008 ; Jagers et al. 2020 ). In addition, other contextual factors are essential, such as trust, reciprocity, collective efficacy, and communication among group members and societal-level facilitators, such as social norms, institutions, and technology (Bandura 2000 ; Ostrom 2010 ; McAdam and Boudet 2012 ; Jagers et al. 2020 ). Taken together, we term this body of knowledge, dispositions, skills, and the context in which they flourish collective environmental literacy . More formally, we define collective environmental literacy as: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig.  1 ).

Key elements of collective environmental literacy

Environmental literacy: Historically individual, increasingly collective

Over the past five decades, the term “environmental literacy” has come into increasingly frequent use. Breaking from the traditional association of “literacy” with reading and writing in formal school contexts, environmental literacy emphasizes associations with character and behavior, often in the form of responsible environmental stewardship (Roth 1992 ). Footnote 1 Such perspectives define the concept as including affective (attitudinal), cognitive (knowledge-based), and behavioral domains, emphasizing that environmental literacy is both a process and outcome that develops, builds, and morphs over time (Hollweg et al. 2011 ; Wheaton et al. 2018 ; Clark et al. 2020 ).

The emphasis on defining, measuring, and developing interventions to bring about environmental literacy has primarily remained at the individual scale, as evidenced by frequent descriptions of an environmentally literate person (Roth 1992 ; Hollweg et al. 2011 among others) rather than community or community member. In most understandings, discussions, and manifestations of environmental literacy, the implicit assumption remains that the unit of action, intervention, and therefore analysis occurs at the individual level. Yet instinctively and perhaps by nature, community members often seek information and, as a result, take action collectively, sharing what some scholars call “the hive mind” or “group mind,” relying on each other for distributed knowledge, expertise, motivation, and support (Surowiecki 2005 ; Sunstein 2008 ; Sloman and Fernbach 2017 ; Paul 2021 ).

As with the proverbial elephant (Saxe, n.d.), each person, household, or neighborhood group may understand or “see” a different part of an issue or challenge, bring a novel understanding to the table, and have a certain perspective or skill to contribute. Although some environmental literacy discussions allude to a collective lens (e.g., Hollweg et al. 2011 ; Ardoin et al. 2013 ; Wheaton et al. 2018 ; Bey et al. 2020 ), defining, developing frameworks, and creating measures to assess the efficacy of such collective-scale sustainability-related endeavors has remained elusive. Footnote 2 Looking to related fields and disciplines—such as ecosystem theory, epidemiology and public health, sociology, network theory, and urban planning, among others—can provide insight, theoretical frames, and empirical examples to assist in such conceptualizations (McAdam and Boudet 2012 ; National Research Council 2015 ) (See Table 1 for an overview of some of the many areas of study that informed our conceptualization of collective environmental literacy).

Seeking the essence of the collective: Looking to and learning from others

The social sciences have long focused on “the kinds of activities engaged in by sizable but loosely organized groups of people” (Turner et al. 2020 , para. 1) and addressed various collective constructs, such as collective behavior, action, intelligence, and memory (Table 1 ). Although related constructs in both the social and natural sciences—such as communities of practice (Wenger and Snyder 2000 ), collaborative governance (Ansell and Gash 2008 ; Emerson et al. 2012 ), and the collaboration–coordination continuum (Sadoff and Grey 2005 ; Prager 2015 ), as well as those from social movement theory and related areas (McAdam and Boudet 2012 ; de Moor and Wahlström 2019 )—lack the word “collective” in name, they too leverage the benefits of collectivity. A central tenet connects all of these areas: powerful processes, actions, and outcomes can arise when individuals coalesce around a common purpose or cause. This notion of a dynamic, potent force transcending the individual to enhance the efficacy of outcomes motivates the application of a collective lens to the environmental literacy concept.

Dating to the 1800s, discussions of collective behavior have explored connections to social order, structures, and norms (Park 1927 ; Smelser 2011 /1962; Turner and Killian 1987 ). Initially, the focus emphasized spontaneous, often violent crowd behaviors, such as riots, mobs, and rebellions. More contemporarily, sociologists, political scientists, and others who study social movements and collective behaviors acknowledge that such phenomena may take many forms, including those occurring in natural ecosystems, such as ant colonies, bird flocks, and even the human brain (Gordon 2019 ). In sociology, collective action represents a paradigm shift highlighting coordinated, purposeful pro-social movements, while de-emphasizing aroused emotions and crowd behavior (Miller 2014 ). In political science, Ostrom’s ( 1990 , 2000 , 2010 ) theory of collective action in the context of the management of shared resources extends the concept’s reach to economics and other fields. In education and the learning sciences, social learning and sociocultural theories tap into the idea of learning as a social-cognitive-cultural endeavor (Vygotsky 1980 ; Lave and Wenger 1991 ; Tudge and Winterhoff 1993 ; Rogoff 2003 ; Reed et al. 2010 ).

Collective action, specifically, and collective constructs, generally, have found their way into the research and practice in the fields of conservation, natural resources, and environmental management. Collective action theory has been applied in a range of settings and scenarios, including agriculture (Mills et al. 2011 ), invasive species management (Marshall et al. 2016 ; Sullivan et al. 2017 ; Lubeck et al. 2019 ; Clarke et al. 2021 ), fire management (Canadas et al. 2016 ; Charnley et al. 2020 ), habitat conservation (Raymond 2006 ; Niemiec et al. 2020 ), and water governance (Lopez-Gunn 2003 ; Baldwin et al. 2018 ), among others. Frameworks and methods that emphasize other collective-related ideas—like collaboration, co-production, and group learning—are also ubiquitous in natural resource and environmental management. These constructs include community-based conservation (DeCaro and Stokes 2008 ; Niemiec et al. 2016 ), community natural resource management (Kellert et al. 2000 ; Dale et al. 2020 ), collaboration/coordination (Sadoff and Grey 2005 ; Prager 2015 ), polycentricity (Galaz et al. 2012 ; Heikkila et al. 2018 ), knowledge co-production (Armitage et al. 2011 ; Singh et al. 2021 ), and social learning (Reed et al. 2010 ; Hovardas 2020 ). Many writings on collective efforts in the social sciences broadly, and applied in the area of environment specifically, provide insights into collective action’s necessary preconditions, which prove invaluable to further defining and later operationalizing collective environmental literacy.

Unpacking the definition of collective environmental literacy: Anchoring principles

As described, we propose the following working definition of collective environmental literacy drawing on our analysis of related literatures and informed by scholarly and professional experience in the sustainability and conservation fields: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig.  1 ). This definition centers on four core, intertwined ideas: the scale of the group involved; the dynamic nature of the process; shared resources brought by, available to, and needed by the group; and the synergy that arises from group interaction.

Multi-scalar

When transitioning from the focus on individual to collective actions—and, herein, principles of environmental literacy—the most obvious and primary requisite shift is one of scale. Yet, moving to a collective scale does not mean abandoning action at the individual scale; rather, success at the collective level is intrinsically tied to what occurs at an individual level. Such collective-scale impacts leverage the power of the hive, harnessing people’s willingness, ability, and motivation to take action alongside others, share their ideas and resources to build collective ideas and resources, contribute to making a difference in an impactful way, and participate communally in pro-social activities.

Collective environmental literacy is likely dynamic in its orientation to scale, incorporating place-based notions, such as ecoregional or community-level environmental literacy (with an emphasis on geographic boundaries). On the other hand, it may encapsulate environmental literacy of a group or organization united by a common identity (e.g., organizational membership) or cause (e.g., old-growth forests, coastal protection), rather than solely or even primarily by geography. Although shifting scales can make measuring collective environmental literacy more difficult, dynamic levels may be a benefit when addressing planetary boundary issues such as climate change, biodiversity, and ocean acidification (Galaz et al. 2012 ). Some scholars have called for a polycentric approach to these large-scale issues in response to a perceived failure of global-wide, top-down solutions (Ostrom 2010 , 2012 ; Jordan et al. 2018 ). Conceptualizing and consequently supporting collective environmental literacy at multiple scales can facilitate such desired polycentricity.

Rather than representing a static outcome, environmental literacy is a dynamic process that is fluctuating and complex, reflective of iterative interactions among community members, whose discussions and negotiations reflect the changing context of sustainability issues. Footnote 3 Such open-minded processes allow for, and indeed welcome, adaptation in a way that builds social-ecological resilience (Berkes and Jolly 2002 ; Adger et al. 2005 ; Berkes 2007 ). Additionally, this dynamism allows for collective development and maturation, supporting community growth in collective knowledge, attitudes, skills, and actions via new experiences, interactions, and efforts (Berkman et al. 2010 ). With this mindset, and within a sociocultural perspective, collective environmental literacy evolves through drawing on and contributing to the community’s funds of knowledge (González et al. 2006 ). Movement and actions within and among groups impact collective literacy, as members share knowledge and other resources, shifting individuals and the group in the course of their shared practices (Samerski 2019 ).

In a collective mode, effectiveness is heightened as shared resources are streamlined, waste is minimized, and innovation maximized. Rather than each group member developing individual expertise in every matter of concern, the shared knowledge, skills, and behaviors can be distributed, pursued, and amplified among group members efficiently and effectively, with collective literacy emerging from the process of pooling diverse forms of capital and aggregating resources. This perspective builds on ideas of social capital as a collective good (Ostrom 1990 ; Putnam 2020 ), wherein relationships of trust and reciprocity are both inputs and outcomes (Pretty and Ward 2001 ). The shared resources then catalyze and sustain action as they are reassembled and coalesced at the group level for collective impact.

The pooled resources—likely vast—may include, but are not limited to, physical and human resources, funding, time, energy, and space and place (physical or digital). Shared resources may also include forms of theorized capital, such as intellectual and social (Putnam 2020 ). Also of note is the recognition that these resources extend far beyond information and knowledge. Of particular interest when building collective environmental literacy are resources previously ignored or overlooked by those in power in prior sustainability efforts. For example, collective environmental literacy can draw strength from shared resources unique to the community or even subgroups within the larger community. Discussions of Indigenous knowledge (Gadgil et al. 1993 ) and funds of knowledge (González et al. 2006 ; Cruz et al. 2018 ) suggest critical, shared resources that highlight strengths of an individual community and its members. Another dimension of shared resources relates to the strength of institutional connections, such as the benefits that accrue from leveraging the collective knowledge, expertise, and resources of organizational collaborators working in adjacent areas to further and amplify each other’s impact (Wojcik et al. 2021 ).

Synergistic

Finally, given the inherent complexities related to defining, deploying, implementing, and measuring these dynamic, at-times ephemeral processes, resources, and outcomes at a collective scale, working in such a manner must be clearly advantageous to pressing sustainability issues at hand. Numerous related constructs and approaches from a range of fields emphasize the benefits of diverse collaboration to collective thought and action, including improved solutions, more effective and fair processes, and more socioculturally just outcomes (Klein 1990 ; Jörg 2011 ; Wenger and Snyder 2000 ; Djenontin and Meadow 2018 ). These benefits go beyond efficient aggregation and distribution of resources, invoking an almost magical quality that defines synergy, resulting in robust processes and outcomes that are more than the sum of the parts.

This synergy relies on the diversity of a group across various dimensions, bringing power, strength, and insight to a decision-making process (Bear and Woolley 2011 ; Curşeu and Pluut 2013 ; Freeman and Huang 2015 ; Lu et al. 2017 ; Bendor and Page 2019 ). Individuals are limited not only to singular knowledge-perspectives and skillsets, but also to their own experiences, which influence their self-affirming viewpoints and tendencies to seek out confirmatory information for existing beliefs (Kahan et al. 2011 ). Although the coming together of those from different racial, cultural, social, and economic backgrounds facilitates a collective literacy process that draws on a wider range of resources and equips a gestalt, it also sets up the need to consider issues of power, privilege, voice, and representation (Bäckstrand 2006 ) and the role of social capital, leading to questions related to trust and reciprocity in effective collectives (Pretty and Ward 2001 ; Folke et al. 2005 ).

Leveraging the ‘Hive’: Proceeding with collective environmental literacy

This paper presents one conceptualization of collective environmental literacy, with the understanding that numerous ways exist to envision its definition, formation, deployment, and measurement. Characterized by a collective effort, such literacies at scale offer a way to imagine, measure, and support the synergy that occurs when the emphasis moves from an individual to a larger whole. By expanding the scale and focusing on shared responsibility among actors at the systems level, opportunities arise for inspiring and enabling a broader contribution to a sustainable future. These evolving notions serve to invite ongoing conversation, both in research and practice, about how to enact our collective responsibility toward, as well as vision of, a thriving future.

Emerging from the many discussions of shared and collaborative efforts to address socio-environmental issues, our conceptualization of collective environmental literacy is a first step toward supporting communities as they work to identify, address, and solve sustainability problems. We urge continued discussions on this topic, with the goal of understanding the concept of collective environmental literacy, how to measure it, and the implications of this work for practitioners. The conceptual roots of collective environmental literacy reach into countless fields of study and, as such, a transdisciplinary approach, which includes an eye toward practice, is necessary to fully capture and maximize the tremendous amount of knowledge, wisdom, and experience around this topic. Specifically, next steps to evolve the concept include engaging sustainability researchers and practitioners in discussions of the saliency of the presented definition of collective environmental literacy. These discussions include verifying the completeness of the definition and ensuring a thorough review of relevant research: Are parts of the definition missing or unclear? What are the “blank, blind, bald, and bright spots” in the literature (Reid 2019 p. 158)? Additionally, recognizing and leveraging literacy at a collective scale most certainly is not unique to environmental work, nor is adopting literacy-related language to conceptualize and measure process outcomes, although the former has consistently proven more challenging. Moreover, although we (the authors) appreciate the connotations and structures gained by using a literacy framework, we struggle with whether “environmental literacy” is the most appropriate and useful term for the conceptualizations as described herein; we, thus, welcome lively discussions about the need for new terminology.

Even at this early stage of conceptualization, this work has implications for practitioners. For scientists, communicators, policymakers, land managers, and other professionals desiring to work with communities to address sustainability issues, a primary take-away message concerns the holistic nature of what is needed for effective collective action in the environmental realm. Many previous efforts have focused on conveying information and, while a lack of knowledge and awareness may be a barrier to action in some cases, the need for a more holistic lens is increasingly clear. This move beyond an individually focused, information-deficit model is essential for effective impact (Bolderdijk et al. 2013 ; van der Linden 2014 ; Geiger et al. 2019 ). The concept of collective environmental literacy suggests a role for developing shared resources that can foster effective collective action. When working with communities, a critical early step includes some form of needs assessment—a systematic, in-depth process that allows for meaningfully gauging gaps in shared resources required to tackle sustainability issues (Braus 2011). Following this initial, evaluative step, an understanding of the components of collective environmental literacy, as outlined in this paper, can be used to guide the development of interventions to support communities in their efforts to address those issues.

Growing discussion of collective literacy constructs, and related areas, suggests researchers, practitioners, and policymakers working in pro-social areas recognize and value collective efforts, despite the need for clearer definitions and effective measures. This definitional and measurement work, in both research and practice, is not easy. The ever-changing, dynamic contexts in which collective environmental literacy exists make defining the concept a moving target, compounded by a need to draw upon work in countless, often distinct academic fields of study. Furthermore, the hard-to-see, inner workings of collective constructs make measurement difficult. Yet, the “power of the hive” is intriguing, as the synergism that arises from communities working in an aligned manner toward a unified vision suggests a potency and wave of motivated action essential to coalescing and leveraging individual goodwill, harnessing its power and potential toward effective sustainability solutions.

See Stables and Bishop’s ( 2001 ) idea of defining environmental literacy by viewing the environment as “text.”

The climate change education literature also includes a nascent, but growing, discussion of collective-lens thinking and literacy. See, for example, Waldron et al. ( 2019 ), Mochizuki and Bryan ( 2015 ), and Kopnina ( 2016 ).

This conceptualization is similar to how some scholars describe collective health literacy (Berkman et al., 2010 ; Mårtensson and Hensing, 2012 ).

Adger, W.N. 2003. Social capital, collective action, and adaptation to climate change. Economic Geography 79: 387–404.

Article   Google Scholar  

Adger, W.N., T.P. Hughes, C. Folke, S.R. Carpenter, and J. Rockström. 2005. Social-ecological resilience to coastal disasters. Science 309: 1036–1039. https://doi.org/10.1126/science.1112122 .

Article   CAS   Google Scholar  

Adler, P.S., and S.-W. Kwon. 2002. Social capital: Prospects for a new concept. Academy of Management Review 27: 17–40. https://doi.org/10.5465/amr.2002.5922314 .

Agrawal, A. 1995. Dismantling the divide between Indigenous and scientific knowledge. Development and Change 26: 413–439. https://doi.org/10.1111/j.1467-7660.1995.tb00560.x .

Aguilar, O.M. 2018. Examining the literature to reveal the nature of community EE/ESD programs and research. Environmental Education Research 24: 26–49. https://doi.org/10.1080/13504622.2016.1244658 .

Aguilar, O., A. Price, and M. Krasny. 2015. Perspectives on community environmental education. In M.C. Monroe & M.E. Krasny (Eds.), Across the spectrum: Resources for environmental educators (3rd edn., pp. 235–249). North American Association for Environmental Education.

Aldrich, D.P., and M.A. Meyer. 2015. Social capital and community resilience. American Behavioral Scientist 59: 254–269. https://doi.org/10.1177/0002764214550299 .

Amel, E., C. Manning, B. Scott, and S. Koger. 2017. Beyond the roots of human inaction: Fostering collective effort toward ecosystem conservation. Science 356: 275–279. https://doi.org/10.1126/science.aal1931 .

Ansell, C., and A. Gash. 2008. Collaborative governance in theory and practice. Journal of Public Administration Research and Theory 18: 543–571. https://doi.org/10.1093/jopart/mum032 .

Ardoin, N.M. 2006. Toward an interdisciplinary understanding of place: Lessons for environmental education. Canadian Journal of Environmental Education 11: 112–126.

Google Scholar  

Ardoin, N.M., and J.E. Heimlich. 2021. Environmental learning in everyday life: Foundations of meaning and a context for change. Environmental Education Research 27: 1681–1699. https://doi.org/10.1080/13504622.2021.1992354 .

Ardoin, N.M., C. Clark, and E. Kelsey. 2013. An exploration of future trends in environmental education research. Environmental Education Research 19: 499–520. https://doi.org/10.1080/13504622.2012.709823 .

Armitage, D., F. Berkes, A. Dale, E. Kocho-Schellenberg, and E. Patton. 2011. Co-management and the co-production of knowledge: Learning to adapt in Canada’s Arctic. Global Environmental Change 21: 995–1004. https://doi.org/10.1016/j.gloenvcha.2011.04.006 .

Assis Neto, F.R., and C.A.S. Santos. 2018. Understanding crowdsourcing projects: A systematic review of tendencies, workflow, and quality management. Information Processing & Management 54: 490–506. https://doi.org/10.1016/j.ipm.2018.03.006 .

Bäckstrand, K. 2006. Multi-stakeholder partnerships for sustainable development: Rethinking legitimacy, accountability and effectiveness. European Environment 16: 290–306. https://doi.org/10.1002/eet.425 .

Baldwin, E., P. McCord, J. Dell’Angelo, and T. Evans. 2018. Collective action in a polycentric water governance system. Environmental Policy and Governance 28: 212–222. https://doi.org/10.1002/eet.1810 .

Bamberg, S., J. Rees, and S. Seebauer. 2015. Collective climate action: Determinants of participation intention in community-based pro-environmental initiatives. Journal of Environmental Psychology 43: 155–165. https://doi.org/10.1016/j.jenvp.2015.06.006 .

Bandura, A. 1977. Social learning theory . Englewood Cliffs: Prentice Hall.

Bandura, A. 2000. Exercise of human agency through collective efficacy. Current Directions in Psychological Science 9: 75–78. https://doi.org/10.1111/1467-8721.00064 .

Barron, B. 2006. Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development 49: 193–224. https://doi.org/10.1159/000094368 .

Barry, M.M., M. D’Eath, and J. Sixsmith. 2013. Interventions for improving population health literacy: Insights from a rapid review of the evidence. Journal of Health Communication 18: 1507–1522. https://doi.org/10.1080/10810730.2013.840699 .

Barton, A.C., and E. Tan. 2009. Funds of knowledge and discourses and hybrid space. Journal of Research in Science Teaching 46: 50–73. https://doi.org/10.1002/tea.20269 .

Bear, J.B., and A.W. Woolley. 2011. The role of gender in team collaboration and performance. Interdisciplinary Science Reviews 36: 146–153. https://doi.org/10.1179/030801811X13013181961473 .

Bendor, J., and S.E. Page. 2019. Optimal team composition for tool-based problem solving. Journal of Economics & Management Strategy 28: 734–764. https://doi.org/10.1111/jems.12295 .

Berkes, F. 2007. Understanding uncertainty and reducing vulnerability: Lessons from resilience thinking. Natural Hazards 41: 283–295. https://doi.org/10.1007/s11069-006-9036-7 .

Berkes, F., and D. Jolly. 2002. Adapting to climate change: Social-ecological resilience in a Canadian western Arctic community. Conservation Ecology 5: 45.

Berkes, F., and H. Ross. 2013. Community resilience: Toward an integrated approach. Society & Natural Resources 26: 5–20. https://doi.org/10.1080/08941920.2012.736605 .

Berkes, F., M.K. Berkes, and H. Fast. 2007. Collaborative integrated management in Canada’s north: The role of local and traditional knowledge and community-based monitoring. Coastal Management 35: 143–162.

Berkman, N.D., T.C. Davis, and L. McCormack. 2010. Health literacy: What is it? Journal of Health Communication 15: 9–19. https://doi.org/10.1080/10810730.2010.499985 .

Bey, G., C. McDougall, and S. Schoedinger. 2020. Report on the NOAA office of education environmental literacy program community resilience education theory of change. National Oceanic and Atmospheric Administration . https://doi.org/10.25923/mh0g-5q69 .

Blumer, H. 1971. Social problems as collective behavior. Social Problems 18: 298–306.

Bodin, Ö. 2017. Collaborative environmental governance: Achieving collective action in social-ecological systems. Science . https://doi.org/10.1126/science.aan1114 .

Bolderdijk, J.W., M. Gorsira, K. Keizer, and L. Steg. 2013. Values determine the (in)effectiveness of informational interventions in promoting pro-environmental behavior. PLoS ONE 8: e83911. https://doi.org/10.1371/journal.pone.0083911 .

Brabham, D.C. 2013. Crowdsourcing . Cambridge: MIT Press.

Book   Google Scholar  

Braus, J. (Ed.). 2011. Tools of engagement: A toolkit for engaging people in conservation. NAAEE/Audubon. https://cdn.naaee.org/sites/default/files/eepro/resource/files/toolsofengagement.pdf .

Brieger, S.A. 2019. Social identity and environmental concern: The importance of contextual effects. Environment and Behavior 51: 828–855. https://doi.org/10.1177/0013916518756988 .

Briggs, J. 2005. The use of Indigenous knowledge in development: Problems and challenges. Progress in Development Studies 5: 99–114. https://doi.org/10.1191/1464993405ps105oa .

Briggs, J., and J. Sharp. 2004. Indigenous knowledges and development: A postcolonial caution. Third World Quarterly 25: 661–676. https://doi.org/10.1080/01436590410001678915 .

Bronfenbrenner, U. 1979. The ecology of human development: Experiments by nature and design . Cambridge: Harvard University Press.

Bruce, C., and P. Chesterton. 2002. Constituting collective consciousness: Information literacy in university curricula. International Journal for Academic Development 7: 31–40. https://doi.org/10.1080/13601440210156457 .

Byerly, H., A. Balmford, P.J. Ferraro, C.H. Wagner, E. Palchak, S. Polasky, T.H. Ricketts, A.J. Schwartz, et al. 2018. Nudging pro-environmental behavior: Evidence and opportunities. Frontiers in Ecology and the Environment 16: 159–168. https://doi.org/10.1002/fee.1777 .

Canadas, M.J., A. Novais, and M. Marques. 2016. Wildfires, forest management and landowners’ collective action: A comparative approach at the local level. Land Use Policy 56: 179–188. https://doi.org/10.1016/j.landusepol.2016.04.035 .

Carden, L., and W. Wood. 2018. Habit formation and change. Current Opinion in Behavioral Sciences 20: 117–122. https://doi.org/10.1016/j.cobeha.2017.12.009 .

Chan, M. 2016. Psychological antecedents and motivational models of collective action: Examining the role of perceived effectiveness in political protest participation. Social Movement Studies 15: 305–321. https://doi.org/10.1080/14742837.2015.1096192 .

Charnley, S., E.C. Kelly, and A.P. Fischer. 2020. Fostering collective action to reduce wildfire risk across property boundaries in the American West. Environmental Research Letters 15: 025007. https://doi.org/10.1088/1748-9326/ab639a .

Chawla, L., and D.F. Cushing. 2007. Education for strategic environmental behavior. Environmental Education Research 13: 437–452. https://doi.org/10.1080/13504620701581539 .

Chinn, D. 2011. Critical health literacy: A review and critical analysis. Social Science & Medicine 73: 60–67. https://doi.org/10.1016/j.socscimed.2011.04.004 .

Clark, C.R., J.E. Heimlich, N.M. Ardoin, and J. Braus. 2020. Using a Delphi study to clarify the landscape and core outcomes in environmental education. Environmental Education Research 26: 381–399. https://doi.org/10.1080/13504622.2020.1727859 .

Clarke, M., Z. Ma, S.A. Snyder, and K. Floress. 2021. Factors influencing family forest owners’ interest in community-led collective invasive plant management. Environmental Management 67: 1088–1099. https://doi.org/10.1007/s00267-021-01454-1 .

Cruz, A.R., S.T. Selby, and W.H. Durham. 2018. Place-based education for environmental behavior: A ‘funds of knowledge’ and social capital approach. Environmental Education Research 24: 627–647. https://doi.org/10.1080/13504622.2017.1311842 .

Curşeu, P.L., and H. Pluut. 2013. Student groups as learning entities: The effect of group diversity and teamwork quality on groups’ cognitive complexity. Studies in Higher Education 38: 87–103. https://doi.org/10.1080/03075079.2011.565122 .

Cutter, S.L., L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb. 2008. A place-based model for understanding community resilience to natural disasters. Global Environmental Change 18: 598–606. https://doi.org/10.1016/j.gloenvcha.2008.07.013 .

Dale, A., K. Vella, S. Ryan, K. Broderick, R. Hill, R. Potts, and T. Brewer. 2020. Governing community-based natural resource management in Australia: International implications. Land 9: 234. https://doi.org/10.3390/land9070234 .

de Moor, J., and M. Wahlström. 2019. Narrating political opportunities: Explaining strategic adaptation in the climate movement. Theory and Society 48: 419–451. https://doi.org/10.1007/s11186-019-09347-3 .

DeCaro, D., and M. Stokes. 2008. Social-psychological principles of community-based conservation and conservancy motivation: Attaining goals within an autonomy-supportive environment. Conservation Biology 22: 1443–1451.

Djenontin, I.N.S., and A.M. Meadow. 2018. The art of co-production of knowledge in environmental sciences and management: Lessons from international practice. Environmental Management 61: 885–903. https://doi.org/10.1007/s00267-018-1028-3 .

Duncan, L.E. 2018. The psychology of collective action. In The Oxford handbook of personality and social psychology , ed. K. Deaux and M. Snyder. Oxford: Oxford University Press.

Edwards, M., F. Wood, M. Davies, and A. Edwards. 2015. ‘Distributed health literacy’: Longitudinal qualitative analysis of the roles of health literacy mediators and social networks of people living with a long-term health condition. Health Expectations 18: 1180–1193. https://doi.org/10.1111/hex.12093 .

Emerson, K., T. Nabatchi, and S. Balogh. 2012. An integrative framework for collaborative governance. Journal of Public Administration Research and Theory 22: 1–29.

Engeström, Y. 2001. Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work 14: 133–156. https://doi.org/10.1080/13639080020028747 .

Ensor, J., and B. Harvey. 2015. Social learning and climate change adaptation: Evidence for international development practice. Wires Climate Change 6: 509–522. https://doi.org/10.1002/wcc.348 .

Fanta, V., M. Šálek, and P. Sklenicka. 2019. How long do floods throughout the millennium remain in the collective memory? Nature Communications 10: 1105. https://doi.org/10.1038/s41467-019-09102-3 .

Feinstein, N.W. 2018. Collective science literacy: A key to community science capacity [Conference session]. American Association for the Advancement of Science Annual Meeting, Austin, TX, USA https://d32ogoqmya1dw8.cloudfront.net/files/earthconnections/collective_science_literacy_key.pdf .

Feola, G. 2015. Societal transformation in response to global environmental change: A review of emerging concepts. Ambio 44: 376–390. https://doi.org/10.2139/ssrn.2689741 .

Fernandez-Gimenez, M.E., H.L. Ballard, and V.E. Sturtevant. 2008. Adaptive management and social learning in collaborative and community-based monitoring: A study of five community-based forestry organizations in the western USA. Ecology and Society 13: 15.

Folke, C., T. Hahn, P. Olsson, and J. Norberg. 2005. Adaptive governance of social-ecological systems. Annual Review of Environment and Resources 30: 441–473. https://doi.org/10.1146/annurev.energy.30.050504.144511 .

Freedman, D.A., K.D. Bess, H.A. Tucker, D.L. Boyd, A.M. Tuchman, and K.A. Wallston. 2009. Public health literacy defined. American Journal of Preventive Medicine 36: 446–451. https://doi.org/10.1016/j.amepre.2009.02.001 .

Freeman, R.B., and W. Huang. 2015. Collaborating with people like me: Ethnic coauthorship within the United States. Journal of Labor Economics 33: S289–S318.

Gadgil, M., F. Berkes, and C. Folke. 1993. Indigenous knowledge for biodiversity conservation. Ambio 22: 151–156.

Galaz, V., B. Crona, H. Österblom, P. Olsson, and C. Folke. 2012. Polycentric systems and interacting planetary boundaries—Emerging governance of climate change–ocean acidification–marine biodiversity. Ecological Economics 81: 21–32. https://doi.org/10.1016/j.ecolecon.2011.11.012 .

Geiger, S.M., M. Geiger, and O. Wilhelm. 2019. Environment-specific vs general knowledge and their role in pro-environmental behavior. Frontiers in Psychology 10: 718. https://doi.org/10.3389/fpsyg.2019.00718 .

Gifford, R., C. Kormos, and A. McIntyre. 2011. Behavioral dimensions of climate change: Drivers, responses, barriers, and interventions. Wires Climate Change 2: 801–827. https://doi.org/10.1002/wcc.143 .

González, N., L.C. Moll, and C. Amanti. 2006. Funds of knowledge: Theorizing practices in households, communities, and classrooms . New York: Routledge.

Gordon, D.M. 2019. Measuring collective behavior: An ecological approach. Theory in Biosciences . https://doi.org/10.1007/s12064-019-00302-5 .

Gould, R.K., N.M. Ardoin, J.M. Thomsen, and N. Wyman Roth. 2019. Exploring connections between environmental learning and behavior through four everyday-life case studies. Environmental Education Research 25: 314–340.

Graham, S., A.L. Metcalf, N. Gill, R. Niemiec, C. Moreno, T. Bach, V. Ikutegbe, L. Hallstrom, et al. 2019. Opportunities for better use of collective action theory in research and governance for invasive species management. Conservation Biology 33: 275–287. https://doi.org/10.1111/cobi.13266 .

Granovetter, M. 1978. Threshold models of collective behavior. American Journal of Sociology 83: 1420–1443.

Groulx, M., M.C. Brisbois, C.J. Lemieux, A. Winegardner, and L. Fishback. 2017. A role for nature-based citizen science in promoting individual and collective climate change action? A systematic review of learning outcomes. Science Communication 39: 45–76. https://doi.org/10.1177/1075547016688324 .

Gutiérrez, K.D., and B. Rogoff. 2003. Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher 32: 19–25. https://doi.org/10.3102/0013189X032005019 .

Guzys, D., A. Kenny, V. Dickson-Swift, and G. Threlkeld. 2015. A critical review of population health literacy assessment. BMC Public Health 15: 1–7. https://doi.org/10.1186/s12889-015-1551-6 .

Halbwachs, M. 1992. On collective memory (L. A. Coser, Ed. & Trans.). University of Chicago Press. (Original works published 1941 and 1952).

Heikkila, T., S. Villamayor-Tomas, and D. Garrick. 2018. Bringing polycentric systems into focus for environmental governance. Environmental Policy and Governance 28: 207–211. https://doi.org/10.1002/eet.1809 .

Heimlich, J.E., and N.M. Ardoin. 2008. Understanding behavior to understand behavior change: A literature review. Environmental Education Research 14: 215–237. https://doi.org/10.1080/13504620802148881 .

Hill, R., F.J. Walsh, J. Davies, A. Sparrow, M. Mooney, R.M. Wise, and M. Tengö. 2020. Knowledge co-production for Indigenous adaptation pathways: Transform post-colonial articulation complexes to empower local decision-making. Global Environmental Change 65: 102161. https://doi.org/10.1016/j.gloenvcha.2020.102161 .

Hollweg, K.S., J. Taylor, R.W. Bybee, T.J. Marcinkowski, W.C. McBeth, and P. Zoido. 2011. Developing a framework for assessing environmental literacy: Executive summary . North American Association for Environmental Education. https://cdn.naaee.org/sites/default/files/envliteracyexesummary.pdf .

Hovardas, T. 2020. A social learning approach for stakeholder engagement in large carnivore conservation and management. Frontiers in Ecology and Evolution 8: 436. https://doi.org/10.3389/fevo.2020.525278 .

Jagers, S.C., N. Harring, Å. Löfgren, M. Sjöstedt, F. Alpizar, B. Brülde, D. Langlet, A. Nilsson, et al. 2020. On the preconditions for large-scale collective action. Ambio 49: 1282–1296. https://doi.org/10.1007/s13280-019-01284-w .

Jordan, A., D. Huitema, H. van Asselt, and J. Forster. 2018. Governing climate change: Polycentricity in action? Cambridge: Cambridge University Press.

Jörg, T. 2011. New thinking in complexity for the social sciences and humanities: A generative, transdisciplinary approach . New York: Springer Science & Business Media.

Jost, J.T., J. Becker, D. Osborne, and V. Badaan. 2017. Missing in (collective) action: Ideology, system justification, and the motivational antecedents of two types of protest behavior. Current Directions in Psychological Science 26: 99–108. https://doi.org/10.1177/0963721417690633 .

Jull, J., A. Giles, and I.D. Graham. 2017. Community-based participatory research and integrated knowledge translation: Advancing the co-creation of knowledge. Implementation Science 12: 150. https://doi.org/10.1186/s13012-017-0696-3 .

Kahan, D.M., H. Jenkins-Smith, and D. Braman. 2011. Cultural cognition of scientific consensus. Journal of Risk Research 14: 147–174. https://doi.org/10.1080/13669877.2010.511246 .

Kania, J., and M. Kramer. 2011. Collective impact. Stanford Social Innovation Review 9: 36–41.

Karachiwalla, R., and F. Pinkow. 2021. Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative. Creativity and Innovation Management 30: 563–584. https://doi.org/10.1111/caim.12454 .

Kellert, S.R., J.N. Mehta, S.A. Ebbin, and L.L. Lichtenfeld. 2000. Community natural resource management: Promise, rhetoric, and reality. Society & Natural Resources 13: 705–715.

Klein, J.T. 1990. Interdisciplinarity: History, theory, and practice . Detroit: Wayne State University Press.

Knapp, C.N., R.S. Reid, M.E. Fernández-Giménez, J.A. Klein, and K.A. Galvin. 2019. Placing transdisciplinarity in context: A review of approaches to connect scholars, society and action. Sustainability 11: 4899. https://doi.org/10.3390/su11184899 .

Koliou, M., J.W. van de Lindt, T.P. McAllister, B.R. Ellingwood, M. Dillard, and H. Cutler. 2020. State of the research in community resilience: Progress and challenges. Sustainable and Resilient Infrastructure 5: 131–151. https://doi.org/10.1080/23789689.2017.1418547 .

Kopnina, H. 2016. Of big hegemonies and little tigers: Ecocentrism and environmental justice. The Journal of Environmental Education 47: 139–150. https://doi.org/10.1080/00958964.2015.1048502 .

Krasny, M.E., M. Mukute, O. Aguilar, M.P. Masilela, and L. Olvitt. 2017. Community environmental education. In Urban environmental education review , ed. A. Russ and M.E. Krasny, 124–132. Ithaca: Cornell University Press.

Chapter   Google Scholar  

Lave, J. 1991. Situating learning in communities of practice.

Lave, J., and E. Wenger. 1991. Situated learning: Legitimate peripheral participation . Cambridge: Cambridge University Press.

Lee, S., and W.-M. Roth. 2003. Science and the “good citizen”: Community-based scientific literacy. Science, Technology, & Human Values 28: 403–424. https://doi.org/10.1177/0162243903028003003 .

Lévy, P., and R. Bononno. 1997. Collective intelligence: Mankind’s emerging world in cyberspace . New York: Perseus Books.

Lloyd, A. 2005. No man (or woman) is an island: Information literacy, affordances and communities of practice. The Australian Library Journal 54: 230–237. https://doi.org/10.1080/00049670.2005.10721760 .

Lopez-Gunn, E. 2003. The role of collective action in water governance: A comparative study of groundwater user associations in La Mancha aquifers in Spain. Water International 28: 367–378. https://doi.org/10.1080/02508060308691711 .

Lu, J.G., A.C. Hafenbrack, P.W. Eastwick, D.J. Wang, W.W. Maddux, and A.D. Galinsky. 2017. “Going out” of the box: Close intercultural friendships and romantic relationships spark creativity, workplace innovation, and entrepreneurship. Journal of Applied Psychology 102: 1091–1108. https://doi.org/10.1037/apl0000212 .

Lubeck, A., A. Metcalf, C. Beckman, L. Yung, and J. Angle. 2019. Collective factors drive individual invasive species control behaviors: Evidence from private lands in Montana, USA. Ecology and Society . https://doi.org/10.5751/ES-10897-240232 .

Mackay, C.M.L., M.T. Schmitt, A.E. Lutz, and J. Mendel. 2021. Recent developments in the social identity approach to the psychology of climate change. Current Opinion in Psychology 42: 95–101. https://doi.org/10.1016/j.copsyc.2021.04.009 .

Magis, K. 2010. Community resilience: An indicator of social sustainability. Society & Natural Resources 23: 401–416. https://doi.org/10.1080/08941920903305674 .

Manfredo, M.J., T.L. Teel, and A.M. Dietsch. 2016. Implications of human value shift and persistence for biodiversity conservation. Conservation Biology 30: 287–296. https://doi.org/10.1111/cobi.12619 .

Marshall, G.R., M.J. Coleman, B.M. Sindel, I.J. Reeve, and P.J. Berney. 2016. Collective action in invasive species control, and prospects for community-based governance: The case of serrated tussock ( Nassella trichotoma ) in New South Wales, Australia. Land Use Policy 56: 100–111. https://doi.org/10.1016/j.landusepol.2016.04.028 .

Mårtensson, L., and G. Hensing. 2012. Health literacy: A heterogeneous phenomenon: A literature review. Scandinavian Journal of Caring Sciences 26: 151–160. https://doi.org/10.1111/j.1471-6712.2011.00900.x .

Martin, C., and C. Steinkuehler. 2010. Collective information literacy in massively multiplayer online games. E-Learning and Digital Media 7: 355–365. https://doi.org/10.2304/elea.2010.7.4.355 .

Masson, T., and I. Fritsche. 2021. We need climate change mitigation and climate change mitigation needs the ‘We’: A state-of-the-art review of social identity effects motivating climate change action. Current Opinion in Behavioral Sciences 42: 89–96. https://doi.org/10.1016/j.cobeha.2021.04.006 .

Massung, E., D. Coyle, K.F. Cater, M. Jay, and C. Preist. 2013. Using crowdsourcing to support pro-environmental community activism. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . https://doi.org/10.1145/2470654.2470708 .

McAdam, D. 2017. Social movement theory and the prospects for climate change activism in the United States. Annual Review of Political Science 20: 189–208. https://doi.org/10.1146/annurev-polisci-052615-025801 .

McAdam, D., and H. Boudet. 2012. Putting social movements in their place: Explaining opposition to energy projects in the United States, 2000–2005 . Cambridge University Press.

McKenzie-Mohr, D. 2011. Fostering sustainable behavior: An introduction to community-based social marketing (3rd edn.). New Society Publishers.

McKinley, D.C., A.J. Miller-Rushing, H.L. Ballard, R. Bonney, H. Brown, S.C. Cook-Patton, D.M. Evans, R.A. French, et al. 2017. Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation 208: 15–28.

Miller, D.L. 2014. Introduction to collective behavior and collective action (3rd ed.). Waveland Press.

Mills, J., D. Gibbon, J. Ingram, M. Reed, C. Short, and J. Dwyer. 2011. Organising collective action for effective environmental management and social learning in Wales. The Journal of Agricultural Education and Extension 17: 69–83. https://doi.org/10.1080/1389224X.2011.536356 .

Mistry, J., and A. Berardi. 2016. Bridging Indigenous and scientific knowledge. Science 352: 1274–1275. https://doi.org/10.1126/science.aaf1160 .

Mochizuki, Y., and A. Bryan. 2015. Climate change education in the context of education for sustainable development: Rationale and principles. Journal of Education for Sustainable Development 9: 4–26. https://doi.org/10.1177/0973408215569109 .

Monroe, M.C. 2003. Two avenues for encouraging conservation behaviors. Human Ecology Review 10: 113–125.

Nasir, N.S., M.M. de Royston, B. Barron, P. Bell, R. Pea, R. Stevens, and S. Goldman. 2020. Learning pathways: How learning is culturally organized. In Handbook of the cultural foundations of learning , ed. N.S. Nasir, C.D. Lee, R. Pea, and M.M. de Royston, 195–211. Routledge.

National Academies of Sciences, Engineering, and Medicine. 2016. Science literacy: Concepts, contexts, and consequences . https://doi.org/10.17226/23595

National Research Council. 2015. Collective behavior: From cells to societies: Interdisciplinary research team summaries . National Academies Press. https://doi.org/10.17226/21737

Niemiec, R.M., N.M. Ardoin, C.B. Wharton, and G.P. Asner G.P. 2016. Motivating residents to combat invasive species on private lands: Social norms and community reciprocity. Ecology and Society , 21. https://doi.org/10.5751/ES-08362-210230

Niemiec, R.M., S. McCaffrey, and M.S. Jones. 2020. Clarifying the degree and type of public good collective action problem posed by natural resource management challenges. Ecology and Society 25: 30. https://doi.org/10.5751/ES-11483-250130 .

Norström, A.V., C. Cvitanovic, M.F. Löf, S. West, C. Wyborn, P. Balvanera, A.T. Bednarek, E.M. Bennett, et al. 2020. Principles for knowledge co-production in sustainability research. Nature Sustainability 3: 182–190. https://doi.org/10.1038/s41893-019-0448-2 .

Olick, J.K. 1999. Collective memory: The two cultures. Sociological Theory 17: 333–348. https://doi.org/10.1111/0735-2751.00083 .

Ostrom, E. 1990. Governing the commons: The evolution of institutions for collective action . Cambridge University Press.

Ostrom, E. 2000. Collective action and the evolution of social norms. Journal of Economic Perspectives 14: 137–158. https://doi.org/10.1257/jep.14.3.137 .

Ostrom, E. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325: 419–422. https://doi.org/10.1126/science.1172133 .

Ostrom, E. 2010. Polycentric systems for coping with collective action and global environmental change. Global Environmental Change 20: 550–557. https://doi.org/10.1016/j.gloenvcha.2010.07.004 .

Ostrom, E. 2012. Nested externalities and polycentric institutions: Must we wait for global solutions to climate change before taking actions at other scales? Economic Theory 49: 353–369. https://doi.org/10.1007/s00199-010-0558-6 .

Ostrom, E., and T.K. Ahn. 2009. The meaning of social capital and its link to collective action. In Handbook of social capital: The troika of sociology, political science and economics , ed. G.T. Svendsen and G.L.H. Svendsen, 17–35. Edward Elgar Publishing.

Papen, U. 2009. Literacy, learning and health: A social practices view of health literacy. Literacy and Numeracy Studies . https://doi.org/10.5130/lns.v0i0.1275 .

Park, R.E. 1927. Human nature and collective behavior. American Journal of Sociology 32: 733–741.

Paul, A.M. 2021. The extended mind: The power of thinking outside the brain . Boston: Mariner Books.

Pawilen, G.T. 2021. Integrating Indigenous knowledge in the Philippine elementary science curriculum: Integrating Indigenous knowledge. International Journal of Curriculum and Instruction 13: 1148–1160.

Prager, K. 2015. Agri-environmental collaboratives for landscape management in Europe. Current Opinion in Environmental Sustainability 12: 59–66. https://doi.org/10.1016/j.cosust.2014.10.009 .

Pretty, J., and H. Ward. 2001. Social capital and the environment. World Development 29: 209–227. https://doi.org/10.1016/S0305-750X(00)00098-X .

Putnam, R.D. 2020. Bowling alone: Revised and updated: The collapse and revival of American community . Anniversary. New York: Simon & Schuster.

Raymond, L. 2006. Cooperation without trust: Overcoming collective action barriers to endangered species protection. Policy Studies Journal 34: 37–57. https://doi.org/10.1111/j.1541-0072.2006.00144.x .

Reed, M.S., A.C. Evely, G. Cundill, I. Fazey, J. Glass, A. Laing, J. Newig, B. Parrish, et al. 2010. What is social learning? Ecology and Society 15: 12.

Reicher, S., R. Spears, and S.A. Haslam. 2010. The social identity approach in social psychology. In The SAGE handbook of identities (pp. 45–62). SAGE. https://doi.org/10.4135/9781446200889

Reid, A. 2019. Blank, blind, bald and bright spots in environmental education research. Environmental Education Research 25: 157–171. https://doi.org/10.1080/13504622.2019.1615735 .

Rogoff, B. 2003. The cultural nature of human development (Reprint edition) . Oxford: Oxford University Press.

Roth, C.E. 1992. Environmental literacy: Its roots, evolution and directions in the 1990s . http://eric.ed.gov/?id=ED348235

Roth, W.-M. 2003. Scientific literacy as an emergent feature of collective human praxis. Journal of Curriculum Studies 35: 9–23. https://doi.org/10.1080/00220270210134600 .

Roth, W.-M., and A.C. Barton. 2004. Rethinking scientific literacy . London: Psychology Press.

Roth, W.-M., and S. Lee. 2002. Scientific literacy as collective praxis. Public Understanding of Science 11: 33–56. https://doi.org/10.1088/0963-6625/11/1/302 .

Roth, W.-M., and S. Lee. 2004. Science education as/for participation in the community. Science Education 88: 263–291.

Roth, W.-M., and Y.-J. Lee. 2007. “Vygotsky’s neglected legacy”: Cultural-historical activity theory. Review of Educational Research 77: 186–232.

Sadoff, C.W., and D. Grey. 2005. Cooperation on international rivers: A continuum for securing and sharing benefits. Water International 30: 420–427.

Samerski, S. 2019. Health literacy as a social practice: Social and empirical dimensions of knowledge on health and healthcare. Social Science & Medicine 226: 1–8. https://doi.org/10.1016/j.socscimed.2019.02.024 .

Sawyer, R.K. 2014. The future of learning: Grounding educational innovation in the learning sciences. In The Cambridge handbook of the learning sciences , ed. R.K. Sawyer, 726–746. Cambridge: Cambridge University Press.

Saxe, J.G. n.d.. The blind man and the elephant . All Poetry. Retrieved October 6, 2020, from https://allpoetry.com/The-Blind-Man-And-The-Elephant .

Scheepers, D., and N. Ellemers. 2019. Social identity theory. In Social psychology in action: Evidence-based interventions from theory to practice , ed. K. Sassenberg and M.L.W. Vliek, 129–143. New York: Springer International Publishing.

Schipper, E.L.F., N.K. Dubash, and Y. Mulugetta. 2021. Climate change research and the search for solutions: Rethinking interdisciplinarity. Climatic Change 168: 18. https://doi.org/10.1007/s10584-021-03237-3 .

Schoerning, E. 2018. A no-conflict approach to informal science education increases community science literacy and engagement. Journal of Science Communication, Doi 10: 17030205.

Schultz, P.W. 2014. Strategies for promoting proenvironmental behavior: Lots of tools but few instructions. European Psychologist 19: 107–117. https://doi.org/10.1027/1016-9040/a000163 .

Sharifi, A. 2016. A critical review of selected tools for assessing community resilience. Ecological Indicators 69: 629–647. https://doi.org/10.1016/j.ecolind.2016.05.023 .

Sherrieb, K., F.H. Norris, and S. Galea. 2010. Measuring capacities for community resilience. Social Indicators Research 99: 227–247. https://doi.org/10.1007/s11205-010-9576-9 .

Singh, R.K., A. Singh, K.K. Zander, S. Mathew, and A. Kumar. 2021. Measuring successful processes of knowledge co-production for managing climate change and associated environmental stressors: Adaptation policies and practices to support Indian farmers. Journal of Environmental Management 282: 111679. https://doi.org/10.1016/j.jenvman.2020.111679 .

Sloman, S., and P. Fernbach. 2017. The knowledge illusion: Why we never think alone . New York: Riverhead Books.

Smelser, N.J. 2011. Theory of collective behavior . Quid Pro Books. (Original work published 1962).

Sørensen, K., S. Van den Broucke, J. Fullam, G. Doyle, J. Pelikan, Z. Slonska, H. Brand, and (HLS-EU) Consortium Health Literacy Project European. 2012. Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health 12: 80. https://doi.org/10.1186/1471-2458-12-80 .

Spitzer, W., and J. Fraser. 2020. Advancing community science literacy. Journal of Museum Education 45: 5–15. https://doi.org/10.1080/10598650.2020.1720403 .

Stables, A., and K. Bishop. 2001. Weak and strong conceptions of environmental literacy: Implications for environmental education. Environmental Education Research 7: 89. https://doi.org/10.1080/13504620125643 .

Stern, M.J., R.B. Powell, and N.M. Ardoin. 2008. What difference does it make? Assessing outcomes from participation in a residential environmental education program. The Journal of Environmental Education 39: 31–43. https://doi.org/10.3200/JOEE.39.4.31-43 .

Stets, J.E., and P.J. Burke. 2000. Identity theory and social identity theory. Social Psychology Quarterly 63: 224–237. https://doi.org/10.2307/2695870 .

Sturmer, S., and B. Simon. 2004. Collective action: Towards a dual-pathway model. European Review of Social Psychology 15: 59–99. https://doi.org/10.1080/10463280340000117 .

Sullivan, A., A. York, D. White, S. Hall, and S. Yabiku. 2017. De jure versus de facto institutions: Trust, information, and collective efforts to manage the invasive mile-a-minute weed (Mikania micrantha). International Journal of the Commons 11: 171–199. https://doi.org/10.18352/ijc.676 .

Sunstein, C.R. 2008. Infotopia: How many minds produce knowledge . Oxford: Oxford University Press.

Surowiecki, J. 2005. The wisdom of crowds . New York: Anchor.

Swim, J.K., S. Clayton, and G.S. Howard. 2011. Human behavioral contributions to climate change: Psychological and contextual drivers. American Psychologist 66: 251–264.

Thaker, J., P. Howe, A. Leiserowitz, and E. Maibach. 2019. Perceived collective efficacy and trust in government influence public engagement with climate change-related water conservation policies. Environmental Communication 13: 681–699. https://doi.org/10.1080/17524032.2018.1438302 .

Tudge, J.R.H., and P.A. Winterhoff. 1993. Vygotsky, Piaget, and Bandura: Perspectives on the relations between the social world and cognitive development. Human Development 36: 61–81. https://doi.org/10.1159/000277297 .

Turner, R.H., and L.M. Killian. 1987. Collective behavior , 3rd ed. Englewood Cliffs: Prentice Hall.

Turner, R.H., N.J. Smelser, and L.M. Killian. 2020. Collective behaviour. In Encyclopedia Britannica . Encyclopedia Britannica, Inc. https://www.britannica.com/science/collective-behaviour .

van der Linden, S. 2014. Towards a new model for communicating climate change. In Understanding and governing sustainable tourism mobility , ed. S. Cohen, J. Higham, P. Peeters, and S. Gössling, 263–295. Milton Park: Routledge.

van Zomeren, M., T. Postmes, and R. Spears. 2008. Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin 134: 504–535. https://doi.org/10.1037/0033-2909.134.4.504 .

Vygotsky, L.S. 1980. Mind in society: The development of higher psychological processes . Cambridge: Harvard University Press.

Waldron, F., B. Ruane, R. Oberman, and S. Morris. 2019. Geographical process or global injustice? Contrasting educational perspectives on climate change. Environmental Education Research 25: 895–911. https://doi.org/10.1080/13504622.2016.1255876 .

Wals, A.E.J., M. Brody, J. Dillon, and R.B. Stevenson. 2014. Convergence between science and environmental education. Science 344: 583–584.

Wenger, E.C., and W.M. Snyder. 2000. Communities of practice: The organizational frontier. Harvard Business Review 78: 139–146.

Weschsler, D. 1971. Concept of collective intelligence. American Psychologist 26: 904–907. https://doi.org/10.1037/h0032223 .

Wheaton, M., A. Kannan, and N.M. Ardoin. 2018. Environmental literacy: Setting the stage (Environmental Literacy Brief, Vol. 1). Social Ecology Lab, Stanford University. https://ed.stanford.edu/sites/default/files/news/images/stanfordsocialecologylab-brief-1.pdf .

Wojcik, D.J., N.M. Ardoin, and R.K. Gould. 2021. Using social network analysis to explore and expand our understanding of a robust environmental learning landscape. Environmental Education Research 27: 1263–1283.

Wood, W., and D. Rünger. 2016. Psychology of habit. Annual Review of Psychology 67: 289–314. https://doi.org/10.1146/annurev-psych-122414-033417 .

Woolley, A.W., C.F. Chabris, A. Pentland, N. Hashmi, and T.W. Malone. 2010. Evidence for a collective intelligence factor in the performance of human groups. Science 330: 686–688. https://doi.org/10.1126/science.1193147 .

Download references

Acknowledgements

We are grateful to Maria DiGiano, Anna Lee, and Becca Shareff for their feedback and contributions to early drafts of this paper. We appreciate the research and writing assistance supporting this paper provided by various members of the Stanford Social Ecology Lab, especially: Brennecke Gale, Pari Ghorbani, Regina Kong, Naomi Ray, and Austin Stack.

This work was supported by a grant from the Pisces Foundation.

Author information

Authors and affiliations.

Emmett Interdisciplinary Program in Environment and Resources, Graduate School of Education, and Woods Institute for the Environment, Stanford University, 233 Littlefield Hall, Stanford, CA, 94305, USA

Nicole M. Ardoin

Social Ecology Lab, Graduate School of Education and Woods Institute for the Environment, Stanford University, 233 Littlefield Hall, Stanford, CA, 94305, USA

Alison W. Bowers

Emmett Interdisciplinary Program in Environment and Resources, School of Earth, Energy and Environmental Sciences, Stanford University, 473 Via Ortega, Suite 226, Stanford, CA, 94305, USA

Mele Wheaton

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nicole M. Ardoin .

Ethics declarations

Conflict of interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Ardoin, N.M., Bowers, A.W. & Wheaton, M. Leveraging collective action and environmental literacy to address complex sustainability challenges. Ambio 52 , 30–44 (2023). https://doi.org/10.1007/s13280-022-01764-6

Download citation

Received : 11 July 2021

Revised : 11 January 2022

Accepted : 22 June 2022

Published : 09 August 2022

Issue Date : January 2023

DOI : https://doi.org/10.1007/s13280-022-01764-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Collective action
• Environmental literacy
• Social movements
• Sustainability
• Find a journal
• Publish with us
• Track your research