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Collaborative Problem Solving® (CPS)

At Think:Kids, we recognize that kids with challenging behavior don’t lack the will  to behave well. They lack the  skills  to behave well.

Our Collaborative Problem Solving (CPS) approach is proven to reduce challenging behavior, teach kids the skills they lack, and build relationships with the adults in their lives.

Anyone can learn Collaborative Problem Solving, and we’re here to help.

What is Collaborative Problem Solving?

Kids with challenging behavior are tragically misunderstood and mistreated. Rewards and punishments don’t work and often make things worse. Thankfully, there’s another way. But it requires a big shift in mindset.

Helping kids with challenging behavior requires understanding why they struggle in the first place. But what if everything we thought was true about challenging behavior was actually wrong? Our Collaborative Problem Solving approach recognizes what research has pointed to for years – that kids with challenging behavior are already trying hard. They don’t lack the will to behave well. They lack the skills to behave well.

Learn More About the CPS Approach

Kids do well if they can.

CPS helps adults shift to a more accurate and compassionate mindset and embrace the truth that kids do well if they can – rather than the more common belief that kids would do well if they simply wanted to.

Flowing from this simple but powerful philosophy, CPS focuses on building skills like flexibility, frustration tolerance and problem solving, rather than simply motivating kids to behave better. The process begins with identifying triggers to a child’s challenging behavior and the specific skills they need help developing.  The next step involves partnering with the child to build those skills and develop lasting solutions to problems that work for everyone.

The CPS approach was developed at Massachusetts General Hospital a top-ranked Department of Psychiatry in the United States.  It is proven to reduce challenging behavior, teach kids the skills they lack, and build relationships with the adults in their lives. If you’re looking for a more accurate, compassionate, and effective approach, you’ve come to the right place. Fortunately, anyone can learn CPS. Let’s get started!

Bring CPS to Your Organization

Attend a cps training.

6 out of 10 teachers report reduced stress.

Significant reductions in parents’ stress.

74% average reduction in use of seclusion.

73% reduction in oppositional behaviors during school.

Parents report improvements in parent-child interactions.

71% fewer self-inflicted injuries.

reduction in school office referrals.

Significant improvements in children’s executive functioning skills.

60% of children exhibited improved behavior 

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One Bowdoin Square, 7th Floor Boston , MA   02114

Phone: 617-643-6030 Fax: 617-643-2502

Email: [email protected]

Full website:  www.thinkkids.org

Explore This Treatment Program

Think:Kids aims to dramatically improve society’s understanding and treatment of challenging kids. Think:Kids achieves these goals by disseminating and implementing an innovative, proven approach described in the book, Treating Explosive Kids: The Collaborative Problem Solving Approach. The CPS model conceptualizes challenging behavior as the result of difficulty with crucial thinking skills; thus, unlike traditional models of discipline, the model eschews power, control, and motivational procedures and focuses instead on identifying and teaching challenging kids the skills they lack.

About the Program

At Think:Kids we have a unique view of challenging kids: we don’t think they are attention-seeking, manipulative or unmotivated. Rather we understand that they lack crucial skills for solving problems, handling frustration and being flexible. And we understand that these skills can be taught. Our approach represents a novel, contemporary, practical, compassionate, and (as documented by published research) highly effective approach for understanding and helping these children and their caretakers. Think:Kids staff currently train thousands of parents, educators, and mental health professionals throughout the world each year in applying this model.

For more information, please visit our full website.

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Pediatric Psychiatry

The Division of Child & Adolescent Psychiatry at Mass General for Children provides comprehensive psychiatric services for children and teens.

Pioneering Psychiatry Research

The integration of patient care and clinical research has been a hallmark of the Department of Psychiatry for more than 30 years. Today, the department has the largest clinical research program in the hospital, with studies at the forefront of neuroscience, molecular biology and genetics.

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Parenting, teaching and treating challenging kids: the collaborative problem solving approach.

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• Register/Take course

Think:Kids and the Department of Psychiatry at Massachusetts General Hospital are pleased to offer an online training program featuring Dr. J. Stuart Ablon. This introductory training provides a foundation for professionals and parents interested in learning the evidence-based approach to understanding and helping children and adolescents with behavioral challenges called Collaborative Problem Solving (CPS). This online training serves as the prerequisite for our professional intensive training.

The CPS approach provides a way of understanding and helping kids who struggle with behavioral challenges. Challenging behavior is thought of as willful and goal oriented which has led to approaches that focus on motivating better behavior using reward and punishment programs. If you’ve tried these strategies and they haven’t worked, this online training is for you! At Think:Kids we have some very different ideas about why these kids struggle. Research over the past 30 years demonstrates that for the majority of these kids, their challenges result from a lack of crucial thinking skills when it comes to things like problem solving, frustration tolerance and flexibility. The CPS approach, therefore, focuses on helping adults teach the skills these children lack while resolving the chronic problems that tend to precipitate challenging behavior.

This training is designed to allow you to learn at your own pace. You must complete the modules sequentially, but you can take your time with the content as your schedule allows. Additional resources for each module provide you with the opportunity for further development. Discussion boards for each module allow you to discuss concepts and your own experiences with other participants. Faculty from the Think:Kids program monitor the boards and offer their point of view.

Registrants will have access to course materials from the date of their registration through the course expiration date.

All care Providers: $149 Due to COVID-19, we are offering this course at the reduced rate of $99 for a limited time.

NOTE: If you are paying for your registration via Purchase Order, please send the PO to [email protected] . Our customer service agent will respond with further instructions.

Cancellation Policy

Refunds will be issued for requests received within 10 business days of purchase, but an administrative fee of $35 will be deducted from your refund. No refunds will be made thereafter. Additionally, no refunds will be made for individuals who claim CME or credit, regardless of when they request a refund.

Through the duration of the course, the faculty moderator will respond to any clinical questions that are submitted to the interactive discussion board. The faculty moderator for this course will be:

J. Stuart Ablon, PhD

*** Please note that discussion boards are reviewed on a regular basis, and responses to all questions will be posted within one week of receipt. ***

Target Audience

This program is intended for: Parents, clinicians, educators, allied mental health professionals, and direct care staff.

Learning Objectives

At the end of this program, participants will be able to:

• Shift thinking and approach to foster positive relationships with children
• Reduce challenging behavior
• Foster proactive, rather than reactive interventions
• Teach skills related to self-regulation, communication and problem solving

MaMHCA, and its agent, MMCEP has been designated by the Board of Allied Mental Health and Human Service Professions to approve sponsors of continuing education for licensed mental health counselors in the Commonwealth of Massachusetts for licensure renewal, in accordance with the requirements of 262 CMR 3.00.

This program has been approved for 3.00 CE credit for Licensed Mental Health Counselors MaMHCA.

Authorization number: 17-0490

The Collaborative of NASW, Boston College, and Simmons College Schools of Social Work authorizes social work continuing education credits for courses, workshops, and educational programs that meet the criteria outlined in 258 CMR of the Massachusetts Board of Registration of Social Workers

This program has been approved for 3.00 Social Work Continuing Education hours for relicensure, in accordance with 258 CMR. Collaborative of NASW and the Boston College and Simmons Schools of Social Work Authorization Number D 61675-E

This course allows other providers to claim a Participation Certificate upon successful completion of this course.

Participation Certificates will specify the title, location, type of activity, date of activity, and number of AMA PRA Category 1 Credit™ associated with the activity. Providers should check with their regulatory agencies to determine ways in which AMA PRA Category 1 Credit™ may or may not fulfill continuing education requirements. Providers should also consider saving copies of brochures, agenda, and other supporting documents.

The Massachusetts General Hospital Department of Psychiatry is approved by the American Psychological Association to sponsor continuing education for psychologists. The Massachusetts General Hospital Department of Psychiatry maintains responsibility for this program and its content.

This offering meets the criteria for 3.00 Continuing Education (CE) credits per presentation for psychologists.

Stuart Ablon, PhD

Available credit.

How to ace collaborative problem solving

April 30, 2023 They say two heads are better than one, but is that true when it comes to solving problems in the workplace? To solve any problem—whether personal (eg, deciding where to live), business-related (eg, raising product prices), or societal (eg, reversing the obesity epidemic)—it’s crucial to first define the problem. In a team setting, that translates to establishing a collective understanding of the problem, awareness of context, and alignment of stakeholders. “Both good strategy and good problem solving involve getting clarity about the problem at hand, being able to disaggregate it in some way, and setting priorities,” Rob McLean, McKinsey director emeritus, told McKinsey senior partner Chris Bradley  in an Inside the Strategy Room podcast episode . Check out these insights to uncover how your team can come up with the best solutions for the most complex challenges by adopting a methodical and collaborative approach. 

Want better strategies? Become a bulletproof problem solver

How to master the seven-step problem-solving process

Countering otherness: Fostering integration within teams

Psychological safety and the critical role of leadership development

If we’re all so busy, why isn’t anything getting done?

To weather a crisis, build a network of teams

Unleash your team’s full potential

Modern marketing: Six capabilities for multidisciplinary teams

Beyond collaboration overload

MORE FROM MCKINSEY

Take a step Forward

Collaborative Problem Solving: What It Is and How to Do It

What is collaborative problem solving, how to solve problems as a team, celebrating success as a team.

Problems arise. That's a well-known fact of life and business. When they do, it may seem more straightforward to take individual ownership of the problem and immediately run with trying to solve it. However, the most effective problem-solving solutions often come through collaborative problem solving.

As defined by Webster's Dictionary , the word collaborate is to work jointly with others or together, especially in an intellectual endeavor. Therefore, collaborative problem solving (CPS) is essentially solving problems by working together as a team. While problems can and are solved individually, CPS often brings about the best resolution to a problem while also developing a team atmosphere and encouraging creative thinking.

Because collaborative problem solving involves multiple people and ideas, there are some techniques that can help you stay on track, engage efficiently, and communicate effectively during collaboration.

• Set Expectations. From the very beginning, expectations for openness and respect must be established for CPS to be effective. Everyone participating should feel that their ideas will be heard and valued.
• Provide Variety. Another way of providing variety can be by eliciting individuals outside the organization but affected by the problem. This may mean involving various levels of leadership from the ground floor to the top of the organization. It may be that you involve someone from bookkeeping in a marketing problem-solving session. A perspective from someone not involved in the day-to-day of the problem can often provide valuable insight.
• Communicate Clearly.  If the problem is not well-defined, the solution can't be. By clearly defining the problem, the framework for collaborative problem solving is narrowed and more effective.
• Expand the Possibilities.  Think beyond what is offered. Take a discarded idea and expand upon it. Turn it upside down and inside out. What is good about it? What needs improvement? Sometimes the best ideas are those that have been discarded rather than reworked.
• Encourage Creativity.  Out-of-the-box thinking is one of the great benefits of collaborative problem-solving. This may mean that solutions are proposed that have no way of working, but a small nugget makes its way from that creative thought to evolution into the perfect solution.
• Provide Positive Feedback. There are many reasons participants may hold back in a collaborative problem-solving meeting. Fear of performance evaluation, lack of confidence, lack of clarity, and hierarchy concerns are just a few of the reasons people may not initially participate in a meeting. Positive public feedback early on in the meeting will eliminate some of these concerns and create more participation and more possible solutions.
• Consider Solutions. Once several possible ideas have been identified, discuss the advantages and drawbacks of each one until a consensus is made.
• Assign Tasks.  A problem identified and a solution selected is not a problem solved. Once a solution is determined, assign tasks to work towards a resolution. A team that has been invested in the creation of the solution will be invested in its resolution. The best time to act is now.
• Evaluate the Solution. Reconnect as a team once the solution is implemented and the problem is solved. What went well? What didn't? Why? Collaboration doesn't necessarily end when the problem is solved. The solution to the problem is often the next step towards a new collaboration.

The burden that is lifted when a problem is solved is enough victory for some. However, a team that plays together should celebrate together. It's not only collaboration that brings unity to a team. It's also the combined celebration of a unified victory—the moment you look around and realize the collectiveness of your success.

We can help

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What Is Collaborative Problem Solving and Why Use the Approach?

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Ablon, J.S. (2019). What Is Collaborative Problem Solving and Why Use the Approach?. In: Pollastri, A., Ablon, J., Hone, M. (eds) Collaborative Problem Solving. Current Clinical Psychiatry. Springer, Cham. https://doi.org/10.1007/978-3-030-12630-8_1

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• Review Article
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• Published: 11 January 2023

The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature

• Enwei Xu   ORCID: orcid.org/0000-0001-6424-8169 1 ,
• Wei Wang 1 &
• Qingxia Wang 1  

Humanities and Social Sciences Communications volume  10 , Article number:  16 ( 2023 ) Cite this article

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Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

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Introduction

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2  ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P  < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2  = 7.95, P  < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2  = 86%, z  = 12.78, P  < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), learning scaffold (chi 2  = 9.03, P  < 0.01), and teaching type (chi 2  = 7.20, P  < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2  = 3.15, P  = 0.21 > 0.05, and chi 2  = 0.08, P  = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2  = 3.15, P  = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P  < 0.01), then higher education (ES = 0.78, P  < 0.01), and middle school (ES = 0.73, P  < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2  = 7.20, P  < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P  < 0.01), integrated courses (ES = 0.81, P  < 0.01), and independent courses (ES = 0.27, P  < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2  = 12.18, P  < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P  < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2  = 9.03, P  < 0.01). The resource-supported learning scaffold (ES = 0.69, P  < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P  < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P  < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2  = 8.77, P  < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P  < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P  < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2  = 0.08, P  = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P  < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2  = 13.36, P  < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P  < 0.01), followed by science (ES = 1.25, P  < 0.01) and medical science (ES = 0.87, P  < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P  < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P  < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P  < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2  = 3.15, P  = 0.21 > 0.05) and measuring tools (chi 2  = 0.08, P  = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

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Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

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What Is Collaborative Problem Solving?

Imagine collaborative problem solving as a symphony where each instrument plays an important part in creating harmony.

As you explore the intricacies of this method, you'll uncover the intricate dance of minds working together to unravel complex issues.

The beauty lies in the synergy of diverse perspectives, the orchestration of communication, and the finesse of teamwork.

But how does this symphony truly come together, and what are the secrets behind its success?

Stay tuned to unravel the mysteries and open the potential of collaborative problem solving.

Table of Contents

Key Takeaways

• Collaborative problem solving enhances efficiency and innovation through diverse perspectives.
• Core principles guide the process for effective outcomes and improved team dynamics.
• Effective communication strategies, like active listening, foster a collaborative problem-solving environment.
• Conflict resolution skills and creativity are essential for successful collaborative problem solving.

Benefits of Collaborative Problem Solving

Collaborative problem solving enhances efficiency and fosters innovative solutions through collective expertise and diverse perspectives. Team synergy plays an important role in this process, as individuals bring forth unique insights that contribute to a thorough understanding of the issue at hand. By leveraging the combined problem-solving strategies of team members, collaborative innovation flourishes, leading to more effective outcomes.

One of the key advantages of collaborative problem solving is the ability to tap into a diverse range of perspectives when making decisions. This diversity allows for a more detailed exploration of potential solutions, as individuals with varying backgrounds and experiences offer fresh insights that one person alone may not have considered. Additionally, the collaborative nature of problem solving fosters a sense of ownership among team members, increasing their commitment to implementing the chosen solution effectively.

Key Elements of CPS Process

When engaging in collaborative problem solving, it's important to understand the core CPS principles that guide the process.

Effective communication strategies play an essential role in ensuring that all team members are on the same page and can contribute their insights.

Collaborative decision-making is key to reaching solutions that consider diverse perspectives and foster a sense of ownership among participants.

Core CPS Principles

Effective problem solving in collaborative settings depends on adherence to the core principles that underpin the CPS process. The core principles encompass a set of guidelines that form the foundation for successful problem-solving techniques within a collaborative framework.

These principles emphasize the importance of active listening, open-mindedness, and mutual respect among team members. By embracing these core principles, individuals can enhance their ability to generate innovative solutions, leverage diverse perspectives, and foster a supportive team environment.

Additionally, these principles highlight the significance of maintaining a solution-focused mindset, promoting constructive feedback, and valuing contributions from all team members. Overall, integrating these core principles into collaborative problem-solving endeavors can lead to more effective outcomes and improved team dynamics.

Effective Communication Strategies

Adherence to the core principles of Collaborative Problem Solving lays the groundwork for implementing Effective Communication Strategies essential to the Key Elements of the CPS Process. Active listening, a fundamental component of effective communication, involves fully concentrating, understanding, responding, and remembering what's being said.

By actively listening, you show respect, build trust, and foster a collaborative environment conducive to problem-solving. Additionally, importance training plays a critical role in communication within the CPS framework. Importance training helps individuals express their needs, thoughts, and feelings in a direct and honest manner while respecting the perspectives of others.

This skill enables effective communication by promoting clarity, openness, and constructive dialogue in addressing conflicts and finding solutions collaboratively.

Collaborative Decision-Making

To achieve successful collaborative decision-making within the CPS process, understanding and integrating the key elements is essential. Group decision making plays an important role in the problem-solving process, ensuring that diverse perspectives are considered.

Collective problem resolution is achieved through a team approach, where individuals contribute their unique insights and expertise to reach a consensus. Effective collaborative decision-making requires active participation from all team members, open communication channels, and a shared commitment to the common goal.

Importance of Team Dynamics

Team dynamics play an important role in determining the success of collaborative problem-solving efforts. Team cohesion, which refers to the ability of a group to work together effectively and harmoniously, is vital in achieving shared goals. When group dynamics are positive, team members are more likely to trust each other, communicate openly, and support each other. Collaboration strategies that focus on enhancing team cohesion can lead to improved problem-solving outcomes. For instance, implementing team-building activities, establishing clear roles and responsibilities, and fostering a culture of respect and inclusivity are all ways to strengthen team dynamics.

Effective group dynamics can help teams navigate challenges, adapt to changing circumstances, and capitalize on diverse perspectives. By valuing each member's contributions and leveraging individual strengths, teams can enhance their problem-solving capabilities. When team members feel connected and engaged, they're more motivated to work collaboratively towards finding innovative solutions. Therefore, investing time and effort into nurturing positive team dynamics is essential for achieving successful collaborative problem-solving outcomes.

Role of Communication in CPS

Effective communication plays a crucial role in collaborative problem solving, facilitating the exchange of ideas and information among team members to drive successful outcomes. In the domain of Collaborative Problem Solving (CPS), effective communication strategies are essential for ensuring that the team functions cohesively and efficiently. Here are some key points highlighting the importance of communication in CPS:

• Clear and Transparent Communication : Ensuring that all team members are on the same page regarding goals and progress.
• Active Listening : Encouraging active listening amongst team members to comprehend diverse perspectives and ideas effectively.
• Feedback Mechanisms : Establishing feedback loops to provide constructive criticism and improve solutions iteratively.
• Non-Verbal Communication : Understanding the significance of body language and other non-verbal cues in enhancing communication.
• Conflict Resolution Skills : Developing techniques to address conflicts constructively and maintain a positive team environment.

Strategies for Effective Collaboration

To effectively collaborate, employ clear communication techniques to make sure all team members are on the same page.

Utilize conflict resolution skills to address any disagreements or disputes that may arise during the problem-solving process.

These strategies are crucial for fostering a productive and harmonious collaborative environment.

Clear Communication Techniques

In successful collaborative problem-solving endeavors, employing clear and concise communication techniques is paramount for fostering productive interactions and achieving common goals. To enhance your collaborative communication skills, consider the following strategies:

• Practice active listening to demonstrate your attentiveness and understanding.
• Pay attention to nonverbal cues such as body language and facial expressions for deeper insights.
• Use open-ended questions to encourage discussion and gather diverse perspectives.
• Clarify any uncertainties promptly to avoid misunderstandings or confusion.
• Summarize key points to make certain alignment and reinforce shared understanding.

Conflict Resolution Skills

Developing proficient conflict resolution skills is essential for ensuring smooth and successful collaboration among team members. Conflict resolution involves addressing disagreements or disputes in a constructive manner to reach a mutually agreeable solution.

Effective conflict resolution requires active listening, empathy, and the ability to remain calm under pressure. Utilizing negotiation skills is important in finding compromises and resolving conflicts amicably.

Team members should focus on understanding the root causes of conflicts and work together to find solutions that benefit all parties involved. By fostering an environment that encourages open communication and respectful dialogue, teams can navigate conflicts productively and strengthen their collaborative efforts.

Conflict resolution skills are important for maintaining positive relationships and achieving shared goals within a team.

Enhancing Creativity Through Collaboration

Enhancing creativity through collaborative problem-solving techniques can yield innovative solutions that transcend individual contributions. When individuals come together to solve problems collectively, creativity flourishes, leading to groundbreaking ideas and outcomes. Here are key ways collaboration enhances creativity:

• Innovation Exploration : Collaborating allows for the exploration of innovative ideas that may not have been possible individually.
• Group Brainstorming : Brainstorming as a group fosters a diverse range of ideas and perspectives, fueling creativity.
• Team Synergy : Working together harnesses the collective strengths of team members, boosting creativity and problem-solving abilities.
• Creative Problem Solving : Collaboration enables the application of different problem-solving approaches, resulting in unique solutions.
• Cross-Pollination of Ideas : Sharing and building upon each other's ideas can lead to the creation of novel and inventive solutions.

Leveraging Diverse Perspectives

Collaborative problem-solving thrives on the ability to leverage diverse perspectives, which play a pivotal role in enhancing the innovative potential of a team. By incorporating various viewpoints and approaches, teams can tap into a wealth of creativity and expertise, acting as innovation catalysts and problem-solving synergy engines. Embracing solution diversity leads to collaborative excellence, where different team members bring unique skills and experiences to the table, enriching the problem-solving process.

To illustrate the significance of leveraging diverse perspectives, consider the following table:

Each row exemplifies how diverse perspectives contribute to collaborative problem-solving by fostering creativity, aiding in decision-making, sparking innovation, broadening problem-solving capabilities, and strengthening team dynamics. Fundamentally, embracing diversity is crucial to achieving collaborative excellence in problem-solving endeavors.

Implementing CPS in Various Settings

Implementing Collaborative Problem Solving (CPS) in various settings requires a meticulous understanding of the context and specific needs of the team or organization. When applying CPS, consider the following:

• Workplace applications: CPS can enhance teamwork, communication, and innovation in a corporate setting, leading to more effective problem-solving and decision-making processes.
• Community engagement: Utilizing CPS in community projects fosters collaboration, empowers stakeholders, and guarantees sustainable solutions to local challenges.
• Educational settings: Implementing CPS in schools promotes critical thinking, creativity, and teamwork among students, preparing them for future challenges in the workforce.
• Healthcare industry: CPS can improve patient care by encouraging interdisciplinary collaboration, addressing complex medical issues, and enhancing overall healthcare delivery.
• Tailored approaches: Customizing CPS methods to fit the unique demands of each environment maximizes its effectiveness and ensures successful outcomes.

Overcoming Challenges in Group Problem Solving

To effectively navigate group problem-solving challenges, it's imperative to acknowledge and address potential obstacles that may hinder productive collaboration and decision-making. Group dynamics play an essential role in the success of collaborative problem-solving efforts. Understanding how individuals interact within the group, recognizing communication patterns, and being aware of potential conflicts are essential for overcoming challenges.

One common obstacle in group problem solving is the presence of dominant personalities that may overshadow others' contributions. Implementing strategies to guarantee equal participation, such as setting time limits for each member to speak or using anonymous idea generation techniques, can help mitigate this issue. Additionally, differing problem-solving strategies among group members can lead to inefficiencies. Encouraging open dialogue to discuss and combine diverse approaches can enhance the overall problem-solving process.

Measuring Success in Collaborative Teams

To measure success in collaborative teams, it's essential to focus on team performance metrics and assess goal attainment. These metrics provide concrete data to evaluate the effectiveness of teamwork strategies and the overall performance of the team.

Team Performance Metrics

How can you effectively measure the success of collaborative teams through team performance metrics? Team performance metrics play an important role in evaluating the effectiveness of collaborative efforts.

To gauge the performance of your team, consider implementing the following strategies:

• Conduct team satisfaction surveys to gather feedback on team dynamics.
• Utilize performance evaluations to assess individual contributions to the team.
• Encourage peer feedback to understand how team members perceive each other's contributions.
• Measure team cohesion by evaluating how well members work together towards common goals.
• Track key performance indicators relevant to the project to make sure progress aligns with objectives.

Goal Attainment Assessment

Evaluating goal attainment is a key aspect of evaluating the success of collaborative teams, providing concrete evidence of achievement in working towards shared objectives. To assess goal attainment effectively, start by setting clear, specific, and measurable goals that align with the team's overarching objectives.

Utilize problem-solving techniques like brainstorming, root cause analysis, and action planning to address obstacles hindering goal achievement. Regularly monitor progress towards these goals through data tracking, milestone checkpoints, and progress reports.

Engage team members in reflective discussions to evaluate the effectiveness of strategies employed and make necessary adjustments. By focusing on goal setting and employing structured problem-solving techniques, collaborative teams can track their progress accurately and enhance their overall performance.

As you navigate the intricate web of collaborative problem solving, remember that each team member is a unique puzzle piece contributing to the bigger picture.

Just as a symphony orchestra harmonizes individual instruments to create a beautiful melody, your team must work together in perfect synchronization to overcome challenges and achieve success.

Embrace the diversity of perspectives, communicate effectively, and leverage each member's strengths to reveal the true potential of collaborative problem solving.

It's the key to revealing greatness.

The eSoft Editorial Team, a blend of experienced professionals, leaders, and academics, specializes in soft skills, leadership, management, and personal and professional development. Committed to delivering thoroughly researched, high-quality, and reliable content, they abide by strict editorial guidelines ensuring accuracy and currency. Each article crafted is not merely informative but serves as a catalyst for growth, empowering individuals and organizations. As enablers, their trusted insights shape the leaders and organizations of tomorrow.

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Collaborative Learning and How You Can Benefit From It

We first learned the importance of compromise, cooperation, and collaboration in grade school as we worked in harmony with our classmates to solve problems. It should come as no surprise that more and more decision-makers in the workplace are starting to realize the merits of collaborative learning.

Nearly all the members of an Eduflow survey responded that collaborative learning was either important or essential to their work, yet, surprisingly, only one in five learning specialists use it regularly. This means many organizations are missing out on the benefits that come with this type of learning experience.

In this article, we’ll explore what collaborative learning is, how it works, and how you can benefit from it as an instructional designer or other professional in the digital learning space. By the time you’re done, you’ll be ready to implement collaborative learning practices in your organization.

Let’s dive in!

• What is collaborative learning?

At its most fundamental level, collaborative learning is a method of learning that takes place when two or more people interact with each other to exchange information, ideas, and opinions to solve a problem. 

This type of learning requires the participants to actively engage with one another in an effort to reach their common goal. Collaborative learning can take many forms such as group discussions, brainstorming sessions, team projects, or other activities that involve multiple individuals working together towards a shared objective.

The beauty of this learning method is it can take place through many different mediums, including face-to-face conversations, digital platforms, social media , and even through virtual reality.

At ELM, we understand how difficult it can be to pick the right training methods for your employees based on individual learning styles, but collaborative learning can be a fun, innovative, and creative approach to guide you in your mission and create a truly symbiotic work environment.

• How collaborative learning works

At its core, collaborative learning involves multiple people working together on tasks or projects with the goal of achieving a common outcome. Rather than just staying in their lane, they can benefit from insights from each other while simultaneously growing their knowledge base. 

When done correctly, this type of team effort can be extremely powerful and beneficial for any organization or project. It encourages team members to think beyond their own experiences and perspectives and look for other solutions or ideas. Instructional designers can be especially well-positioned to benefit from collaborative learning in the digital learning space. 

By working together with developers, instructional designers can help them understand the needs of learners while also gaining insight into how best to design engaging content. 

Likewise, developers can offer feedback on what works best from a technical standpoint, which is invaluable information for instructional designers who are tasked with creating effective courses. 

By incorporating collaborative learning techniques into your training structure, you can create a learning organization that values knowledge as an integral component of its corporate identity and long-term vision for success.

Why should you consider collaborative learning?

So, why should you consider collaborative learning as part of your learning and development program? Here’s what we think at ELM:

• 1. Increased engagement

Collaborative learning has been proven to increase engagement in the classroom and workplace. Employees are more likely to be motivated, productive, and engaged when they have a sense of ownership over their work. 

• 2. Improved problem-solving skills

Working together on projects encourages employees to think outside the box, exchange ideas, and brainstorm solutions. The improved problem-solving skills resulting from this process can help improve productivity and team performance in the long run. 

• 3. Enhanced communication & teamwork

By working together as a team, employees learn how to communicate effectively with one another, which leads to better understanding and improved teamwork. This group mentality can also help build relationships amongst colleagues, which, in turn, can lead to increased job satisfaction and a more positive work environment overall. 

• 4. Improved knowledge & understanding

Collaborative learning encourages employees to ask questions and learn from each other’s experiences. This helps create an environment where everyone can share their knowledge and grow together as a team.

Clearly, collaborative learning has many benefits that can help enhance your learning and development program. But the benefits don’t stop there. 

Benefits of collaborative learning

Organizations that embrace collaborative learning stand to benefit in many other ways beyond the obvious ones mentioned above. These benefits include: 

• 1. Increased productivity

Studies have shown that collaborative learning can help organizations become more productive by allowing employees to work together to find solutions and develop better ideas faster. 

• 2. Enhanced creativity

As mentioned above, working collaboratively encourages employees to think more creatively and create unique solutions to problems. This can lead to improved creativity in the workplace, which can help organizations stay ahead of their competition. 

• 3. Increased motivation

Collaborative learning encourages employees to work together and support each other, providing a greater sense of camaraderie and motivation among staff members. This helps create an environment where employees are motivated to do their best work and push themselves further than they would on their own. 

• 4. Reduced costs

By encouraging collaborative learning in the workforce, organizations can reduce costs associated with training programs and other overhead expenses related to employee development initiatives. 

By leveraging these benefits, organizations can gain a competitive edge over their competitors while improving employee morale and reducing costs associated with training programs.

Collaborative learning theories

Theories of collaborative learning can be traced back to the late 19 th century when psychologist Lev Vygotsky developed his Zone of Proximal Development theory. This theory suggests that collaboration between learners with differing levels of knowledge and experience can help them develop skills more quickly than if they were working alone. 

Today, there are many popular theories on collaborative learning which have been adapted to fit modern learning environments. Let’s go over a few of them!

• Social interdependence theory

This theory states that collaboration between individuals is essential for successful learning and emphasizes the importance of teamwork and shared goals in order to achieve success. 

This theory can be applied in multiple ways in terms of workforce training. For example, a company could create teams of employees with complementary skills and assign them tasks that require collaboration to complete. 

This could help foster an environment of cooperation and support within the team, resulting in better performance overall. Additionally, collaborative learning can be used for problem-solving activities that encourage groups to devise creative solutions to workplace challenges. 

This type of activity helps learners develop their critical thinking skills and encourages them to work together as a team and learn from each other’s perspectives.

• Constructivism theory

The premise behind this theory is that learners are actively constructing knowledge through their interactions with one another rather than passively absorbing it from instructors or texts. 

L&D professionals can use constructivism theories of learning to create engaging, collaborative learning experiences for their employees. They can facilitate activities that encourage learners to ask questions, share ideas, and work together as a team to build solutions. 

By doing so, they’ll be able to foster an environment where everyone is an active participant in the learning process. At the same time, learning and development specialists should also focus on creating individualized training paths based on each worker’s unique needs and goals. 

By providing personalized support throughout the process, they’ll ensure that their employees get the most out of their experience. 

• Connectivism theory

This theory focuses on the idea that collaboration can create new networks of knowledge and understanding that can shape our thinking. 

Workers can benefit from the connectivism learning theory by engaging in activities such as group projects, collaborative brainstorming sessions, workshops, and more. These activities allow individuals to learn from each other while building relationships and trust within their teams.

 By facilitating open dialogue between employees, managers can create a culture of collaboration where everyone feels comfortable speaking up about their ideas or thoughts without fear of judgment or criticism. This helps create a more efficient, productive workplace where everyone works together towards common goals.

By understanding these theories, instructional designers and educators can design collaborative learning activities that foster active engagement in the classroom or the workplace and help learners develop new skills and knowledge.

5 great examples of collaborative learning

From providing more effective training for employees to creating a more productive working environment, collaborative learning can help organizations achieve great things. 

Here are five timely examples of how companies have used collaborative learning to their advantage: 

Adobe noticed that after a surge in remote working, employees sought to create or reinforce connections with their colleagues, so they tested useful models and standards for hybrid meetings to bring people together. Thus, Lab 82 was born—a space created to foster togetherness and experimentation for employees.

• 2. Microsoft

Microsoft developed its own in-house platform, Yammer , which allows teams across different offices and countries to communicate easily and work on projects together. 

• 3. Google 

One of the most innovative companies in the world, Google encourages employees to come up with creative solutions and collaborate on projects through its “ 20% time ” initiative that allows engineers to spend one day a week working on their own pet projects. 

• 4. Apple 

Apple is known for fostering collaboration among its teams by creating an atmosphere that encourages open dialogue and constructive criticism among team members. 

• 5. Amazon 

The creator of the world’s largest online retailer uses the “ two-pizza rule ” to ensure all groups are small enough to promote meaningful communication. If a group swells to a size that two pizzas cannot feed, that group does not promote meaningful collaboration. 

• Summing it all up

Collaborative learning is an effective way to foster a productive and engaged workforce. It enables employees to develop their own ideas, collaborate with peers on complex tasks, practice problem-solving skills, and create meaningful connections within the team.

With its focus on creativity, communication, critical thinking, and collaboration—all essential elements of successful teams—collaborative learning can improve job performance and productivity. 

If you want to create real change through a smart, beautiful learning experience, ELM Learning is ready to help you. We at ELM believe organizations can succeed when they commit to building a culture of continuous learning. 

Discover how collaborative learning can help you succeed. Contact us today ! 

Sign up to get the latest industry tips, insights, and trends from our team of learning experts.

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A 2024 Guide to Collaborative Learning

Unlock the potential of collaborative learning with Colleague Connect. Foster meaningful connections, enhance teamwork, and achieve shared professional goals.

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February 16, 2024

Updated on 

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Traditional mentor-mentee dynamics can be restrictive.

Isolated, passive learning simply doesn't cut it anymore. We need learning experiences that are dynamic, engaging, and empower individuals to contribute meaningfully. 

Collaborative learning helps with exactly that. 

What is collaborative learning?

Collaborative learning is an educational approach where learners work together in groups to solve problems, complete tasks, or create projects. Organizations should choose collaborative learning over conventional methods of L&D because it encourages creativity, enhances problem-solving, improves communication and teamwork, and leads to more engaged, motivated, and adaptable employees. That’s why 60% of L&D professionals believe that collaborative learning is essential. 

There are six key aspects of collaborative learning:

• Shared goals – In collaborative learning, participants work towards common objectives, like how employees at Google engage in cross-functional projects to innovate and solve complex problems.
• Interdependence – This aspect emphasizes reliance on others, as seen in NASA teams , where scientists and engineers depend on each other's expertise for successful space missions.
• Communication skills – Collaborative learning enhances communication, similar to how medical teams in hospitals must communicate effectively to provide patient care.
• Social skills – It involves developing social skills such as empathy, cooperation, and conflict resolution, crucial for successful teamwork.
• Peer teaching – Learners teach each other , as seen in software development teams using pair programming, where one writes code while the other reviews it.
• Motivation – This approach boosts motivation, mirrored in academic settings like Stanford University, where collaborative learning methods lead to higher engagement and academic success.

Lynita Taylor, Program Manager - Diversity, Equity, & Inclusion at Samsara leveraged mentoring programs to build a collaborative learning culture and develop their leadership pipeline.

👀 10 Employee Engagement Trends to Watch in 2024 (+ Tips)

Benefits of collaborative learning.

Collaborative learning activities enable knowledge sharing and skill development in a team environment. It helps improve productivity and employee engagement , and prepares your team for future roles. It has five major benefits:

1. Enhanced critical thinking

Collaborative learning encourages active engagement and diverse perspectives, which stimulate critical thinking. Through discussions, debates, and problem-solving activities, learners are exposed to different viewpoints, challenging them to analyze, evaluate, and synthesize information critically.

Collaborative learning can make participants more emotionally aware and broad-minded, motivated to learn, and improve cognitive development. 

To achieve enhanced critical thinking through collaborative learning:

• Facilitate group interactions using open-ended questions and encourage respectful debate and lively discussions.
• Embrace collaborative problem-solving through challenges where teams brainstorm, explore diverse perspectives, and evaluate different solutions.
• Implement peer review and self-reflection by encouraging constructive feedback.

🤝 Peer learning: 10 benefits of collaboration in the workplace

2. improved communication skills.

72% of business leaders and 52% of employees agree that effective communication results in increased productivity.

Collaborative learning helps improve communication skills of all participants. It encourages all participants to articulate their ideas cohesively and listen attentively to others. They develop confidence in verbal and written communication skills through frequent interactions and discussions.

Here’s what you can do to get this benefit of collaborative learning:

• Host collaborative presentations on group projects to boost confident oral communication.
• Simulate real-world scenarios via role-playing activities, allowing employees to practice communication skills in various contexts.
• Enable peer-to-peer feedback via constructive feedback on communication style and clarity.

🪓 How to Break Down Silos In Your Organization

3. increased knowledge retention and application.

Collaborative learning enhances knowledge retention. By discussing and teaching concepts to their peers, learners reinforce their own understanding and memory of the material. Collaborating online boosts problem-solving abilities and learning satisfaction.

Additionally, collaborative learning encourages learners to apply their knowledge to solve real-world problems, reinforcing learning through practical application.

To increase knowledge retention through collaborative learning, make sure you:

• Hold group work activities like group discussions, simulations, and collaborative projects – promoting active learning.
• Encourage peer tutoring and knowledge sharing via messaging and collaboration tools.
• Connect learning to real-world applications through tangible outcomes–facilitating knowledge application.

👬 What is Peer Mentoring, and how does it work?

4. development of diverse skill sets.

Collaborative learning environments bring together learners with diverse backgrounds, experiences, and skill sets. It helps them learn about each other's strengths and expertise– skills that are beyond academic knowledge. To get this benefit:

• Create diverse mentorship pairings – better achieved through sophisticated matching algorithms .
• Assign team roles with varied strengths, including leadership, in group projects.
• Implement cross-cultural projects that encourage research on diverse cultures for awareness and adaptability.

🎯 Examples of diversity, equity, inclusion, and belonging performance goals

5. improved peer support and feedback.

We already know collaborative online learning has a positive effect on participants’ emotional well-being, attitude, and social interactions. Learners feel more comfortable asking questions, sharing ideas, and seeking help in such environments. To improve peer support:

• Set ground rules for respectful interaction and communication.
• Utilize peer feedback tools to encourage constructive feedback and foster a supportive learning environment.
• Cultivate a sense of community, build connections, and encourage knowledge sharing–creating a supportive network for employees.

🔮 Your Guide to Implementing 360 Feedback (+ Free Template)

Introducing colleague connect: a collaborative learning tool.

What’s even more surprising, employees may not always notice when their company promotes collaborative learning. However, they can make the most of it once they are conscious about it.

And one way of making it obvious is through Together’s Colleague Connect ! 

It uses the groundwork laid down by traditional mentorship programs and networking initiatives. It then takes it a step further by providing a structured platform for employees to connect, form learning partnerships, and jointly pursue professional goals. 

Think of it as a dynamic hub where collaboration thrives, knowledge flows freely, and individuals empower each other to reach their full potential.

Some of its features include:

Connecting with colleagues

Gathering participants prior to learning sessions to connect can bolster collaborative learning experiences. And what better way to bring together colleagues across departments than with Colleague Connect?

The tool facilitates meaningful connections based on shared interests, goals, and expertise. So, it can be used for finding people with perfectly complementing skill sets. The result is a supportive network that goes beyond an immediate team. 

Whether you're a seasoned professional seeking fresh perspectives or a newcomer eager to learn from experienced colleagues, do it with Colleague Connect.

Forming learning partnerships

But it doesn't stop there. Colleague Connect encourages the formation of learning partnerships. This two-way exchange encourages individuals to share insights, experiences, and knowledge. Unlike the one-sided mentor-mentee dynamic, collaborative learning involves everyone contributing and benefiting. 

For instance, employees from different departments can tackle a shared challenge via Colleague Connect. The synergy of diverse skills and experiences leads to richer learning journeys and innovative solutions.

Interactive features

Internal communication is important to the employees' workplace efficiency. It promotes collaboration and raises productivity. Now imagine empowering learners with a way to communicate–no matter where they are!

Colleague Connect has built-in interactive features, like:

• Discussion forums
• Knowledge-sharing channels
• Real-time collaboration

They create a vibrant and engaging learning community. Participants can discuss industry trends, share best practices, and brainstorm solutions with colleagues in real-time. This dynamic environment keeps learning exciting and relevant, making continuous improvement a part of your organization’s culture. 

Encouraging continuous learning

Continuous learning isn't a luxury; it's a necessity, especially for remote teams . 

Colleague Connect promotes a higher learning culture that can be the catalyst for ongoing learning. It does so by providing a platform for individuals to:

• Adapt to new skills
• Explore emerging trends
• Stay ahead of the curve
• Perform better and grow at the company

It also prepares organizations to help survive in a dynamic environment. 

Supporting workplace culture

Supporting workplace culture isn't just about trendy perks and team-building exercises. Leverage Colleague Connect to demonstrate your organization's commitment to a collaborative environment. This kind of support can influence knowledge exchange, participative decision making, and turnover intention.

Through Colleague Connect, employees can share knowledge freely, actively support each other, and feel valued for their unique contributions. In short, the tool contributes to increased employee satisfaction , retention, and overall engagement. 

Build a stronger, more resilient organization with our tool.

Colleague Connect vs. Traditional (peer) mentorship programs 

Colleague Connect stands out from traditional peer mentorship and networking initiatives in several key ways.

Colleague Connect overcomes the limitations of traditional mentorship and networking by offering a structured, dynamic, and inclusive approach to unlock the transformative power of collaborative learning. Imagine a workforce where everyone is both a teacher and a student–sharing knowledge, building skills, and growing together. That’s what Colleague Connect helps you achieve.

The power of Colleague Connect lies in its ability to match people based on their shared skills and goals, or traits like location, department and ERGs. They aren't playing 'mentor' or 'mentee' roles - but they're learning, networking, and growing.

Join the collaborative learning revolution – invest in your people, invest in your future.

Learn more about our Colleague Connect today .

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Collaborative Problem Solving: Processing Actions, Time, and Performance

Paul de boeck.

1 Department of Psychology, The Ohio State University, Columbus, OH, United States

2 Department of Psychology, KU Leuven, Leuven, Belgium

Kathleen Scalise

3 Department of Educational Methodology, Policy, and Leadership, University of Oregon, Eugene, OR, United States

Associated Data

This study is based on one collaborative problem solving task from an international assessment: the Xandar task. It was developed and delivered by the Organization for Economic Co-operation and Development Program for International Student Assessment (OECD PISA) 2015. We have investigated the relationship of problem solving performance with invested time and number of actions in collaborative episodes for the four parts of the Xandar task. The parts require the respondent to collaboratively plan a process for problem solving, implement the process, reach a solution, and evaluate the solution (For a full description, see the Materials and Methods section, “Parts of the Xandar Task.”) Examples of an action include posting to a chat log, accessing a shared resource, or conducting a search on a map tool. Actions taken in each part of the task were identified by PISA and recorded in the data set numerically. A confirmatory factor analysis (CFA) model looks at two types of relationship: at the level of latent variables (the factors) and at extra dependencies, which here are direct effects and correlated residuals (independent of the factors). The model, which is well-fitting, has three latent variables: actions (A), times (T), and level of performance (P). Evidence for the uni-dimensionality of performance level is also found in a separate analysis of the binary items. On the whole for the entire task, participants with more activities are less successful and faster, based on the United States data set employed in the analysis. By contrast, successful participants take more time. By task part, the model also investigates relationships between activities, time, and performance level within the parts. This was done because one can expect dependencies within parts of such a complex task. Results indicate some general and some specific relationships within the parts, see the full manuscript for more detail. We conclude with a discussion of what the investigated relationships may reveal. We also describe why such investigations may be important to consider when preparing students for improved skills in collaborative problem solving, considered a key aspect of successful 21st century skills in the workplace and in everyday life in many countries.

Introduction

The construct explored here, collaborative problem solving (CPS), was first introduced to the Program for International Student Assessment (PISA) in 2015. Attempts to explore process data collected in complex activities such as CPS are emerging rapidly in education. Yet which models might best fit process data and the analytic techniques to employ to investigate patterns in the data are not well understood at this time. So here we investigate whether relationships seen in the actions taken by PISA respondents, as coded by PISA, might shed light on approaches for modeling complex CPS tasks.

In the CPS task released by PISA, the Xandar task, there are four parts. The parts of the task require the respondent to collaborate to plan a process for problem solving, implement the process, reach a solution, and evaluate the solution. (For a full description of these parts, see the Materials and Methods section, “Parts of the Xandar Task.”) Examples of actions in Part 1, for instance, include posting to a chat log, accessing a shared resource, or conducting a search on a shared map tool.

In each of the parts, process data are available on time spent and number of actions, as well as on the performance on specific items within the four parts. We explore modeling these Xandar data to address three research questions:

• simple RQ1. Does a factor model employing process data (actions and time) support evidence for a latent variable differentiation between the types of process data (actions, time) and between the latter two and quality of performance? The expected latent variables are Actions, Time, and Performance.
• simple RQ2. Do extra dependencies at the level of the observed variables improve model fit, including direct effects and correlated residuals (independent of the factors)? If they do, they reveal direct relationships between process aspects and performance, independent of the latent variables. These direct relationships are indications of the dynamics underlying collaborative problem solving, whereas the latent variables and their correlations inform us about global individual differences in process approaches and performance.
• simple RQ3. Can the performance also be considered as uni-dimensional at the specific level of the individual items (from all four Xandar parts)?

In this Xandar investigation, each factor (latent variable) is composed of four corresponding measures from the four Xandar parts. Data are fit with a latent variable model to answer RQ1. Dependencies within parts can be expected between the three measures. So we address the extra dependencies in RQ2. The dependencies are not only considered for methodological reasons when variables stem from the same part, but they may also reveal how subjects work on the tasks. Finally, because a good-fitting factor model would imply uni-dimensionality of the performance sum scores from the four parts, we also explore uni-dimensionality at the level of the individual items in RQ3.

Sections in this paper first discuss the PISA efforts to explore problem solving in 2012 and 2015 assessments, then offer a brief summary of the literature on CPS. Next in the Materials and Methods section, we discuss the PISA 2015 collaborative complex problem solving released task, “Xandar,” including the availability of the released code dictionary and data set. In the Results and Discussion, we model United States data from the Xandar task and report results to address the three research questions.

PISA and a Brief Summary of Literature on CPS

The PISA 2015 CPS construct, which included measuring groups in collaboration, was built on PISA’s 2012 conception of individual problem solving ( OECD, 2014 ). In PISA 2012, some student individual characteristics related to individual problem solving were measured. These measures were openness to learning, perseverance, and problem solving strategies.

For the 2015 PISA collaborative framework ( OECD, 2013 ), the construct of problem solving was extended from 2012 in order to include measures of group collaboration. For this new assessment in 2015, it was recognized that the ability of an individual to be successful in many modern situations involves participating in a group. Collaboration was intended to include such challenges as communicating within the group, managing conflict, organizing a group, and building consensus, as well as managing progress on a successful solution.

The PISA framework described the importance of improving collaboration skills for students ( Rummel and Spada, 2005 ; Vogel et al., 2016 ) The measurement of collaboration skills was at the heart of problem solving competencies in the PISA CPS 2015 framework. The framework specified first that the competency being described remained the capacity of an individual, not the group. Secondly, the respondent must effectively engage in a process whereby two or more agents attempt to solve a problem, where the agents can be people or simulations. Finally, the collaborators had to show efficacy by sharing the understanding and effort required to come to a solution, such as pooling knowledge to reach solutions.

Approaches to gathering assessment evidence cited by the PISACPS framework ( OECD, 2013 ) ranged from allowing actions during collaboration to evaluating the results from collaboration. Measures of collaboration in the research literature include solution success, as well as processes during the collaboration ( Avouris et al., 2003 ). In situ observables for such assessments could include analyses of log files in which the computer keeps a record of student activities, sets of intermediate results, and paths taken along the way ( Adejumo et al., 2008 ). Group interactions also offer relevant information ( O’Neil et al., 1997 ), including quality and type of communication ( Cooke et al., 2003 ; Foltz and Martin, 2008 ; Graesser et al., 2008 ) and judgments ( McDaniel et al., 2001 ).

The international Assessment and Teaching for twenty-first century Skills (ATC21S) project also examined the literature on disposition to collaboration and to problem solving in online environments. ATC21S described how interface design feature issues and the evaluation of CPS processes interact in the online collaboration setting ( Scalise and Binkley, 2009 ; Binkley et al., 2010 , 2012 ).

In the PISA 2015 CPS assessment, a student’s collaborative problem-solving ability is assessed in scenarios where the student must solve a problem. For collaboration, the problem is solving working with “agents,” or computer avatars that simulate collaboration. The CPS framework describes that a problem need not be subject-matter specific task,. Rather it could also be as a partial task in an everyday problem. Examples of subject-matter specific problem solving include setting up a sustainable fish farm in science, planning the construction of a bridge using engineering and mathematics, or writing a persuasive letter using language arts and literacy Examples of an “everyday” problem include communicating with others to delegate roles during collaboration for event planning, monitoring to ensure a group remains on task, and evaluating whether collaboration is complete. All these actions can be directed toward the ultimate goal.

In the PISA 2015 perspective, assessment is continuous throughout the unit and can incorporate student’s interactions with the digital agents. Each student response on a traditional question follows a stream of actions during which the student has chosen how to interact and collaborate with standardized agents in each particular task situation. Very few of the collaborative actions and tasks are released by PISA, but the number of collaborative actions in each part of the task are released and made available in the PISA data sets. So here we accept that PISA has coded the action as taking place, and analyze the numeric results provided.

Materials and Methods

Parts of the xandar task.

Here we analyze numeric data provided for the PISA 2015 Xandar unit ( OECD, 2017a , 2017b ). In the unit Xandar:

“A three-person team consisting of the student test-taker and two computer agents takes part in a contest where [the team] must answer questions about the fictional country of Xandar. The questions [involve] Xandar’s geography, people and economy. This unit involves decision-making and coordination tasks, requires consensus-building collaboration, and has an in-school, private, and non-technology-based context.”

Xandar is a fictional planet appearing in comic books published by Marvel Comics. In the PISA Xandar task, it is treated as a mythical location to be investigated collaboratively. The Xandar task has four parts:

• • Part 1 – Agreeing on a Strategy. This part of the Xandar activity familiarizes the student with how the contest will proceed, the chat interface and the task space including buttons that students can click to take actions in particular situations and a scorecard that monitors team progress. In Part 1, the student is assigned to work in a team with digital agents named Alice and Zach. A variety of actions are available. The respondent and the agents interact to generate a stream of actions. The respondent is expected to follow the rules of engagement provided for the contest and to effectively establish collaborative and problem-solving strategies that were the goal of Part 1.
• • Part 2 – Reaching a Consensus Regarding Preferences. In this part of the Xandar activity, group members should take responsibility for the contest questions in one subject area (Xandar’s geography, people, or economy). The team members must apportion the subject areas among themselves. The agents begin by disagreeing. The student has opportunities to help resolve the disagreement, can take a variety of actions, and the goal is to establish common understanding.
• • Part 3 – Playing the Game Effectively. In this part of the Xandar activity, group members begin playing the game by answering geography contest questions together. The group has the opportunity to choose among answers, during which the agents interject questions, pose concerns and even violate game rules. The student exhibits collaborative problem solving strategies through actions and responses.
• • Part 4 – Assessing Progress. In this part of the Xandar activity, agent Alice has posed a question about its progress. The student responds with an evaluation. Regardless of the student’s answer, agent Zach indicates he is experiencing trouble information foraging for his assigned subject area, economy. Responses and actions take place regarding both evaluating and supporting group members.

Each of the four parts comes with a number of items to score the performance. The complete Xandar released task is presented in an OECD PISA report that illustrates the items that students faced in the 2015 PISA collaborative problem-solving assessment ( OECD, 2016 ). The released code dictionary and data are also available on the 2015 PISA website. We do not repeat the Xandar information here (due in part to copyright), but summarize only. The Xandar released unit presents:

• • a screenshot of each item
• • the correct action(s) or response to the item
• • an explanation as to why the action or response is correct
• • the skills that are examined by the item
• • alignments describing the difficulty of the item.

As described earlier, this study employed data publicly released from the Organization for Economic Co-operation and Development Program for International Student Assessment (OECD PISA) for the optional collaborative problem solving (CPS) assessment. It was administered in 2015 to nationally representative samples of approximately age 15 students. Since PISA is designed to have systematically missing data in a matrix sample, only students who took the Xandar task were included. Students were sampled according to the PISA sample frame. Data analyzed here are representatively sampled United States participants from the Xandar released task. See Table 1 for descriptives by age, gender and race/ethnicity of the United States Xandar task sample used.

Descriptives for collaborative problem solving Xandar assessment for the United States sample.

From the 994 students who took the Xandar task, 986 have complete Xandar data. The descriptive statistics and all analyses are based on N = 986. (Note that limitations to be discussed later in this manuscript include only United States data examined to date in this exploration. Extensions to more countries and comparisons across countries are an exciting and interesting potential to the work. However, the international extensions are out of scope for this article.) For the purposes of the current study, the school variable was not employed. All students were treated as one group.

Regarding ethical approval and consent for human subjects data collection in PISA, OECD gains ethical approval and consent through PISA processes. Processes are established in coordination with each country for public release of some de-identified data collected in PISA main study assessments. Data sets made available for release are intended for purposes of secondary research. The CPS data set used here is available through the OECD data repository website 1 .

As discussed earlier, for the Xandar task, released data are available for actions, time and level of performance. The data for the current study included four indicators each of CPS actions taken (parts 1–4), time taken (parts 1–4), and success scores (parts 1–4). These become the three latent traits, or factors, in this study. To measure CPS actions, we used number of collaboration actions as measured by the data provided in the log transformation of C1A, C2A, C3A, and C4A. “C” indicates this was a collaborative assessment, the numeral indicates the Xandar part, and “A” indicates number of actions taken. To measure timing, we used timing as measured by data provided in the log transformation of C1T, C2T, C3T, and C4T. “C” indicates this was a collaborative assessment, the numeral indicates the Xandar part, and T indicates time taken. To measure student success, we used the sum of the binary item response success scores for each of the four parts, C1P, C2P, C3P, and CP4 (based on 5, 3, 2, and 2 items within the Xandar parts).

Exploratory data analysis following log transformation as described above for some variables revealed only minor deviations from normality. Skewness between −2 to 2 was used for all observed variables ( Cohen et al., 2002 ). Note, however, that this is not a strongly conservative range, as discussed in the limitations. So we also report for this study skewness with all observed variables approximately in the range −1 to 1 except for C1A (1.52) and C2A (1.48). Due to no major levels of deviation, the analysis proceeded without further transformation to the observed variables. Other descriptives for all observed variables are provided in Table 2 .

Descriptives for observed variables.

We fit the model using lavaan ( Rosseel, 2012 ) in R version 3.5.1 ( R Core Team, 2018 ). We used the weighted least squares “WLSMV” option which employs the diagonally weighted least squares (DWLS) estimator with robust standard errors and a mean and variance adjusted test statistic. We have estimated a confirmatory factor analysis (CFA) model with three factors (each with standardized latent variables). The factors are Actions, Time, and Performance. Each one has the four corresponding measures from the four Xandar parts.

Because dependencies within parts can be expected between the three measures, some parameters were added to the model. They are direct within-part effects of actions on time (more actions implies more time), direct within-part effects of performance on time (better performance may take more time), and correlated residuals for actions and performance within each part (exploring the relationship between actions and performance level).

Direct effects and residual correlations are two different types of dependencies. Direct effects are effects of one variable on another (e.g., of Y 1 on Y 2 ). The two directions, Y 1 → Y 2 and Y 2 → Y 1 , are not mathematically equivalent. Correlated residuals are equivalent with the effect of a residual of one variable on the other variable (e.g., of ε Y ⁢ 1 on Y 2 ). the two directions are mathematically equivalent and equivalent with the covariance of the residuals. To be clear, neither of the dependencies prove a causality relation. A causal hypothesis can be at the basis of hypothesizing a direct effect, whereas correlated residuals can be used for explorative purposes, without specifying a direction. For the present study, we hypothesized that more actions take more time and that a higher level of performance requires more time. For number of actions and level of performance we explore the dependency with correlated residuals.

See the row heads of Tables 3 , ​ ,4 4 and Figure 1 for a definition of the model estimated. It includes the latent variable structure as well as the dependencies. The model can also be derived from the R code for the analysis, which is available in the Supplementary Material .

CFA factor loadings Xandar measures.

Extra dependencies in CFA model for Xandar measures.

Latent variable and dependency model for Xandar data. The latent variables are Time, Actions, and Performance. The observed variables per factor are indicated with capital letters referring to the latent variable (T, A, P) and with a number referring to the Xandar part (1, 2, 3, and 4). The direct effects between observed variables from the same Xandar part are indicated with single headed dashed arrows (between the A and T and between the P and T). The correlated residuals are indicated with dotted lines without arrow. Significance ( p <–01) is denoted with a thicker dashed arrow (direct effects) or line (correlated residuals). All dependencies are positive except when indicated with “neg” (between Al and PI). Correlations between latent variables, factor loadings, residual variances, and dependency values are omitted to avoid clutter in the figure. The correlations between the latent variables can be found in the text, the factor loadings are presented in Table 3 , and the dependency values in Table 4 .

In this section we describe the results of the modeling. With the dependencies as described in the Methods section added to the model, the model fit was good (close), with a TLI of 0.95 and RMSEA of 0.038 (90% CI 0.029 to 0.048). Without the dependencies (without the eight direct effects and four residual correlations), the model fit is clearly worse, with a TLI of 0.574 and RMSEA of 0.112 (90% CI 0.104 to 0.119). These results address RQ1 and RQ2.

The correlations between the latent variables are −0.473, p <0.001 (Actions and Time), −0.732, p < 0.001 (Actions and Performance), and 0.190, p < 0.01 (Time and Performance). The loadings and dependencies are shown in Tables 3 , ​ ,4, 4 , respectively. As expected, the indicators of actions, time, and performance all showed significant positive factor loadings on the corresponding factors (see Table 3 ). The standardized coefficients in the last column indicate that the loadings of the Part 4 indicators are lower than those of the other three parts: 0.19 (Actions), 0.43 (Time), and 0.38 (Performance).

Table 4 shows the estimates of the dependencies:

• • Number of activities makes time longer: a significant positive effect was found for all four parts.
• • A significant positive effect of performance on time was found only for Part 4. For the other parts the effect was almost zero.
• • Number of activities and performance levels have significant correlated residuals for two parts. For explorative reasons the dependencies were not tested with a direction but with correlated residuals instead. The results were found to be different depending on the part. Results showed negative dependency for Part 1, a positive dependency for Part 4, and an almost zero dependency for the Parts 2 and 3.

Although the factor model with these dependencies fits well, we wanted to check whether the performance is also uni-dimensional at the level of the individual items (RQ3). Uni-dimensionality of the four sum scores as implied by the factor model, does not imply uni-dimensionality at the level of the 12 individual binary items. This is especially because the items represent four processes (exploring and understanding, representing and formulating, planning and executing, and monitoring and reflecting) and three competencies (establishing and maintaining shared understanding, taking appropriate action to solve the problem, and establishing and maintaining team organization), but not with a perfectly crossed design.

The answer to the dimensionality question based on the analysis with this data set is that the 12 items can be considered as uni-dimensional based on the empirical data, although they are designed to tap on a diversity of processes and competencies. The uni-dimensional model fit was good (close), with a TLI of 0.94 and RMSEA of 0.037 (90% CI 0.029, 0.046). The uni-dimensional model is the result of an ordinal confirmatory factor model for the binary items using WLSMV and the same lavaan version as for the earlier analysis. For the delta parameterization the loadings vary between 0.272 and 0.776 and they are all significant ( p < 0.001).

For the model with loadings and dependencies showing in Tables 3 , ​ ,4, 4 , the latent variable correlations of Actions with Time and with Performance are negative. Hence, participants showing more activities are faster and perform less well in their collaborative problem solving. This is based on the United States dataset with the Xandar task. Successful participants take more time, perhaps a consequence of the previous two relationships. Multiplying the two negative correlations yields −0.473 × −0.723 = 0.346, which is higher than the 0.190 estimate of the correlation between Time and Performance. This explains that in an alternative but formally equivalent model with an effect of Actions on Time and on Performance, the correlation between the residuals of the latent variables Time and Performance is negative. However, the correlation of −0.260 in question is not significant ( p > 0.05).

The negative correlation between Actions and Time suggests that highly active students are fast and not so active students are slow. The combination of fast and active on the latent variables seem to reflect an impulsive and fast trial-and-error style. This strategy shows itself in the Xandar task as not very successful versus a slower, more thoughtful and apparently more successful style. It makes sense that respondents who are more deliberative may have more knowledge to bring to considering a successful solution, or be exhibiting more test effort in the Xandar context. We do not have the information to examine what is happening during the deliberation. This is in part because descriptions of the possible actions are not available in the data set. As well there is no interpretive information provided by PISA for the sample. This could include think-alouds where students describe why they are doing what they are doing. It could also have included qualitative response process information in which student explain their processes, in-depth interviews, or other approaches that supply interpretive information.

However, it makes of course sense that more actions take more time, which shows in the analysis of the dependencies between observed actions and time. This illustrates why it is informative to differentiate relationships between latent variables from relationships which show in dependencies.

Other important dependencies concern Part 4, which is a clearly reflective task, a kind of reflective and evaluative pause. The nature of the task may explain why performance is associated with more actions and requires more time, in contrast with Part 1 (agreeing on a strategy) where the association between actions and performance is negative. For instance, too much discussion on a strategy may signal a lack of structure.

For the result that the items examined can be considered as uni-dimensional although they are designed to tap on a diversity of processes and competencies, this suggests that the collaborative ability generalizes across processes. In other words, the collaborative competencies rely on a general underlying ability. The specificities of the processes are reflected in the extra dependencies. Part 4 involves monitoring and reflecting. This may explain why more activities and more time are associated with better performance. Part 1 by contrast involves planning and execution and representing and formulating. This may lead to better results if not based on trial and error (many actions) but on a structured and goal-oriented approach (less actions).

These dependencies suggest that, depending on the task, the collaborative ability may rely on a general underlying ability but be implemented through a different approach in various collaborative actions, as has been discussed in the literature ( Fiore et al., 2017 ; OECD, 2017b ; Eichmann et al., 2019 ). The special and specific status of Part 4 is also reflected in its lower loadings on all three latent variables (see standardized loadings).

Note that the extra dependencies here are not only considered for methodological reasons when variables stem from the same part. They may also reveal how subjects work on the tasks. This is consistent with the findings here. Parts such as 1 and 4 have a distinct theoretical description in the PISA framework. But how they draw on the collaborative ability can be seen in the empirical data to seemingly require different approaches as indicated in the process data.

Taken together, these results for the United States data set are consistent with problem solving performance modeled as invested time and number of actions.

Potential impacts underscore that it seems possible both to collect and to scale information on the collaborative ability. Measures may help provide intervention support, since in today’s world especially, teams with good collaborative skills are necessary in any group. Groups can range from families to corporations, public institutions, organizations, and government agencies ( OECD, 2013 ). Previously, dispositions to collaborate were reported based on the PISA data ( Scalise et al., 2016 ). Indicators of collaborative ability also may be needed to create adequate interventions to train collaboration skills and to change current levels of individual collaboration.

As previously reported, the disposition dimensions of collaborate , negotiate , and advocate / guide might be useful starting points for creating such interventions ( Scalise et al., 2016 ; OECD, 2017a ). Alternatively, the factor structure here may yield suggestions on additional interesting starting points. This could include structures by which a student may approach collaboration ( OECD, 2017b ; Wilson et al., 2017 ) but more interpretive information would be needed. This could be combined with how participatory a student is disposed to be in collaboration, along with his or her team leadership inclinations, and beliefs in the value or efficacy of collaboration ( Scalise et al., 2016 ).

Limitations to the analysis here include that only the United States data set of many countries available in the PISA data was analyzed. So this analysis should be extended to more countries and results compared in future work.

Also, from a statistical standpoint as discussed earlier, missing data were excluded listwise. In addition, minor but not major skewness was seen in two of the observed variables. Finally, multilevel modeling was not employed so the nested nature of students within schools was not taken into account.

TLI and RMSEA were reported here as the two fit indices since they seem most commonly used in the educational assessment field for large scale analyses. But there have been limited considerations for CPS on this topic.

For limitations from a conceptual standpoint, OECD releases a limited range of information, for instance items for only one of the 2015 collaborative problem solving tasks (Xandar) was released and collaborative actions were numbered but not described in the data set and data dictionary.

For implications of future work from this study, there are several. First, the era of analyzing process data and not only item response data in robust assessment tasks is upon us (many researchers including Praveen and Chandra, 2017 ). Approaches such as used here could be applied for other constructs, not just problem solving. Models can consider how to explore two types of relationship:

• • at the level of general individual differences (the factors)
• • at extra dependencies, which are direct effects and correlated residuals (independent of the factors)

These extra dependencies may provide a window on the underlying process dynamics, see Figure 1 . It should be noted for implications for future work that it would be helpful if a range of simplified visualizations could be developed for such complex analyses. Standard plots after including dependencies seemed too complex to be fully useful.

For extensions to the specific modeling here, it would be important as discussed earlier to explore fitting the same or similar models across data sets from other countries ( Thomas and Inkson, 2017 ). This could be augmented by also modeling potential country-level effects at the item level, by exploring differential item functioning. Furthermore it would be interesting to consider covariates available in the PISA student questionnaire data set (SQ) in relation to the collaborative ability examined here. This could include indicators for dispositions for collaborative problem solving that moved forward to the main PISA study ( Scalise et al., 2016 ). These indicators include student-level indicators available in the CPS SQ data set regarding self-report of dispositions toward cooperation, guiding, and negotiating.

It should also be mentioned that other very interesting student-level indicators regarding additional preferences in collaboration had to be dropped from the PISA main study. This was due to time limitations. Dropped indicators included dispositions toward collaborative leadership , as well as student-level indicators of in-school and out-of-school collaborative opportunities . While these were not possible to include in the main study due to time limitations for the PISA administration, the indicators were part of the field testing. They could be very interesting to administer at the country-level in other national or international assessments.

Teacher-level indicators are also available in the PISA data set that provide information on opportunity to learn (OtL) for students in the PISA CPS. Data include classroom-level OtL reports of team activities, grouping practices, types of collaborative activities, and types of rewards provided for engaging in successful team work. Exploring relationships here might allow more reflection on connections to potential interventions. The PISA data are cross-sectional but might help to inform research studies within countries.

In closing, it is important to mention that the creation and delivery of the innovative PISA CPS instrument included both simulated collaboration of a hard-to-measure construct ( Scalise, 2012 ) and sharing of some process data. This was critical to the examination here, as has been the case for other collaboration-oriented assessments ( Greiff et al., 2014 , 2015 , 2016 ). This analysis underscores that addressing challenges of education in the 21st century may continue to require new data sources, to address new challenges for education worldwide.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1 www.oecd.org/pisa/data/

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.01280/full#supplementary-material

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Collaborative Robotics is prioritizing ‘human problem solving’ over humanoid forms

Humanoids have sucked a lot of the air out of the room. It is, after all, a lot easier to generate press for robots that look and move like humans. Ultimately, however, both the efficacy and scalability of such designs have yet to be proven out. For a while now, Collaborative Robotics founder Brad Porter has eschewed robots that look like people. Machines that can potentially reason like people, however, is another thing entirely.

As the two-year-old startup’s name implies, Collaborative Robotics (Cobot for short) is interested in the ways in which humans and robots will collaborate, moving forward. The company has yet to unveil its system, though last year, Porter told me that the “novel cobot” system is neither humanoid nor a mobile manipulator mounted to the back of an autonomous mobile robot (AMR).

The system has, however, begun to be deployed in select sites.

“Getting our first robots in the field earlier this year, coupled with today’s investment, are major milestones as we bring cobots with human-level capability into the industries of today,” Porter says. “We see a virtuous cycle where more robots in the field lead to improved AI and a more cost-effective supply chain.”

Further deployment will be helped along by a fresh $100 million Series B, led by General Catalyst and featuring Bison Ventures, Industry Ventures and Lux Capital. That brings the Bay Area firm’s total funding up to $140 million. General Catalyst’s Teresa Carlson is also joining the company in an advisory role.

Cobot has the pedigree, as well, with staff that includes former Apple, Meta, Google, Microsoft, NASA and Waymo employees. Porter himself spent more than 13 years at Amazon. When his run with the company ended in summer 2020, he was leading the retail giant’s industrial robotics team.

Amazon became one of the world’s top drivers and consumer of industrial robotics during that time, and the company’s now ubiquitous AMRs stand as a testament to the efficiency of pairing human and robot workers together.

AI will, naturally, be foundational to the company’s promise of “human problem solving,” while the move away from the humanoid form factor is a bid, in part, to reduce the cost of entry for deploying these systems.