empirical case study

• What is Empirical Research Study? [Examples & Method]

The bulk of human decisions relies on evidence, that is, what can be measured or proven as valid. In choosing between plausible alternatives, individuals are more likely to tilt towards the option that is proven to work, and this is the same approach adopted in empirical research. 

In empirical research, the researcher arrives at outcomes by testing his or her empirical evidence using qualitative or quantitative methods of observation, as determined by the nature of the research. An empirical research study is set apart from other research approaches by its methodology and features hence; it is important for every researcher to know what constitutes this investigation method. 

What is Empirical Research? 

Empirical research is a type of research methodology that makes use of verifiable evidence in order to arrive at research outcomes. In other words, this  type of research relies solely on evidence obtained through observation or scientific data collection methods. 

Empirical research can be carried out using qualitative or quantitative observation methods , depending on the data sample, that is, quantifiable data or non-numerical data . Unlike theoretical research that depends on preconceived notions about the research variables, empirical research carries a scientific investigation to measure the experimental probability of the research variables 

Characteristics of Empirical Research

• Research Questions

An empirical research begins with a set of research questions that guide the investigation. In many cases, these research questions constitute the research hypothesis which is tested using qualitative and quantitative methods as dictated by the nature of the research.

In an empirical research study, the research questions are built around the core of the research, that is, the central issue which the research seeks to resolve. They also determine the course of the research by highlighting the specific objectives and aims of the systematic investigation. 

• Definition of the Research Variables

The research variables are clearly defined in terms of their population, types, characteristics, and behaviors. In other words, the data sample is clearly delimited and placed within the context of the research. 

• Description of the Research Methodology

 An empirical research also clearly outlines the methods adopted in the systematic investigation. Here, the research process is described in detail including the selection criteria for the data sample, qualitative or quantitative research methods plus testing instruments. 

An empirical research is usually divided into 4 parts which are the introduction, methodology, findings, and discussions. The introduction provides a background of the empirical study while the methodology describes the research design, processes, and tools for the systematic investigation. 

The findings refer to the research outcomes and they can be outlined as statistical data or in the form of information obtained through the qualitative observation of research variables. The discussions highlight the significance of the study and its contributions to knowledge. 

Uses of Empirical Research

Without any doubt, empirical research is one of the most useful methods of systematic investigation. It can be used for validating multiple research hypotheses in different fields including Law, Medicine, and Anthropology. 

• Empirical Research in Law : In Law, empirical research is used to study institutions, rules, procedures, and personnel of the law, with a view to understanding how they operate and what effects they have. It makes use of direct methods rather than secondary sources, and this helps you to arrive at more valid conclusions.
• Empirical Research in Medicine : In medicine, empirical research is used to test and validate multiple hypotheses and increase human knowledge.
• Empirical Research in Anthropology : In anthropology, empirical research is used as an evidence-based systematic method of inquiry into patterns of human behaviors and cultures. This helps to validate and advance human knowledge.

Discover how Extrapolation Powers statistical research: Definition, examples, types, and applications explained.

The Empirical Research Cycle

The empirical research cycle is a 5-phase cycle that outlines the systematic processes for conducting and empirical research. It was developed by Dutch psychologist, A.D. de Groot in the 1940s and it aligns 5 important stages that can be viewed as deductive approaches to empirical research. 

In the empirical research methodological cycle, all processes are interconnected and none of the processes is more important than the other. This cycle clearly outlines the different phases involved in generating the research hypotheses and testing these hypotheses systematically using the empirical data. 

• Observation: This is the process of gathering empirical data for the research. At this stage, the researcher gathers relevant empirical data using qualitative or quantitative observation methods, and this goes ahead to inform the research hypotheses.
• Induction: At this stage, the researcher makes use of inductive reasoning in order to arrive at a general probable research conclusion based on his or her observation. The researcher generates a general assumption that attempts to explain the empirical data and s/he goes on to observe the empirical data in line with this assumption.
• Deduction: This is the deductive reasoning stage. This is where the researcher generates hypotheses by applying logic and rationality to his or her observation.
• Testing: Here, the researcher puts the hypotheses to test using qualitative or quantitative research methods. In the testing stage, the researcher combines relevant instruments of systematic investigation with empirical methods in order to arrive at objective results that support or negate the research hypotheses.
• Evaluation: The evaluation research is the final stage in an empirical research study. Here, the research outlines the empirical data, the research findings and the supporting arguments plus any challenges encountered during the research process.

This information is useful for further research. 

Learn about qualitative data: uncover its types and examples here.

Examples of Empirical Research 

• An empirical research study can be carried out to determine if listening to happy music improves the mood of individuals. The researcher may need to conduct an experiment that involves exposing individuals to happy music to see if this improves their moods.

The findings from such an experiment will provide empirical evidence that confirms or refutes the hypotheses. 

• An empirical research study can also be carried out to determine the effects of a new drug on specific groups of people. The researcher may expose the research subjects to controlled quantities of the drug and observe research subjects to controlled quantities of the drug and observe the effects over a specific period of time to gather empirical data.
• Another example of empirical research is measuring the levels of noise pollution found in an urban area to determine the average levels of sound exposure experienced by its inhabitants. Here, the researcher may have to administer questionnaires or carry out a survey in order to gather relevant data based on the experiences of the research subjects.
• Empirical research can also be carried out to determine the relationship between seasonal migration and the body mass of flying birds. A researcher may need to observe the birds and carry out necessary observation and experimentation in order to arrive at objective outcomes that answer the research question.

Empirical Research Data Collection Methods

Empirical data can be gathered using qualitative and quantitative data collection methods. Quantitative data collection methods are used for numerical data gathering while qualitative data collection processes are used to gather empirical data that cannot be quantified, that is, non-numerical data. 

The following are common methods of gathering data in empirical research

• Survey/ Questionnaire

A survey is a method of data gathering that is typically employed by researchers to gather large sets of data from a specific number of respondents with regards to a research subject. This method of data gathering is often used for quantitative data collection , although it can also be deployed during quantitative research.

A survey contains a set of questions that can range from close-ended to open-ended questions together with other question types that revolve around the research subject. A survey can be administered physically or with the use of online data-gathering platforms like Formplus. 

Empirical data can also be collected by carrying out an experiment. An experiment is a controlled simulation in which one or more of the research variables is manipulated using a set of interconnected processes in order to confirm or refute the research hypotheses.

An experiment is a useful method of measuring causality; that is cause and effect between dependent and independent variables in a research environment. It is an integral data gathering method in an empirical research study because it involves testing calculated assumptions in order to arrive at the most valid data and research outcomes. 

T he case study method is another common data gathering method in an empirical research study. It involves sifting through and analyzing relevant cases and real-life experiences about the research subject or research variables in order to discover in-depth information that can serve as empirical data.

• Observation

The observational method is a method of qualitative data gathering that requires the researcher to study the behaviors of research variables in their natural environments in order to gather relevant information that can serve as empirical data.

How to collect Empirical Research Data with Questionnaire

With Formplus, you can create a survey or questionnaire for collecting empirical data from your research subjects. Formplus also offers multiple form sharing options so that you can share your empirical research survey to research subjects via a variety of methods.

Here is a step-by-step guide of how to collect empirical data using Formplus:

Sign in to Formplus

In the Formplus builder, you can easily create your empirical research survey by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus. 

Once you do this, sign in to your account and click on “Create Form ” to begin. 

Unlock the secrets of Quantitative Data: Click here to explore the types and examples.

Edit Form Title

Click on the field provided to input your form title, for example, “Empirical Research Survey”.

Edit Form  

• Click on the edit button to edit the form.
• Add Fields: Drag and drop preferred form fields into your form in the Formplus builder inputs column. There are several field input options for survey forms in the Formplus builder.
• Edit fields
• Click on “Save”
• Preview form.

Customize Form

Formplus allows you to add unique features to your empirical research survey form. You can personalize your survey using various customization options. Here, you can add background images, your organization’s logo, and use other styling options. You can also change the display theme of your form. 

• Share your Form Link with Respondents

Formplus offers multiple form sharing options which enables you to easily share your empirical research survey form with respondents. You can use the direct social media sharing buttons to share your form link to your organization’s social media pages. 

You can send out your survey form as email invitations to your research subjects too. If you wish, you can share your form’s QR code or embed it on your organization’s website for easy access. 

Empirical vs Non-Empirical Research

Empirical and non-empirical research are common methods of systematic investigation employed by researchers. Unlike empirical research that tests hypotheses in order to arrive at valid research outcomes, non-empirical research theorizes the logical assumptions of research variables. 

Definition: Empirical research is a research approach that makes use of evidence-based data while non-empirical research is a research approach that makes use of theoretical data. 

Method: In empirical research, the researcher arrives at valid outcomes by mainly observing research variables, creating a hypothesis and experimenting on research variables to confirm or refute the hypothesis. In non-empirical research, the researcher relies on inductive and deductive reasoning to theorize logical assumptions about the research subjects.

The major difference between the research methodology of empirical and non-empirical research is while the assumptions are tested in empirical research, they are entirely theorized in non-empirical research. 

Data Sample: Empirical research makes use of empirical data while non-empirical research does not make use of empirical data. Empirical data refers to information that is gathered through experience or observation. 

Unlike empirical research, theoretical or non-empirical research does not rely on data gathered through evidence. Rather, it works with logical assumptions and beliefs about the research subject. 

Data Collection Methods : Empirical research makes use of quantitative and qualitative data gathering methods which may include surveys, experiments, and methods of observation. This helps the researcher to gather empirical data, that is, data backed by evidence.  

Non-empirical research, on the other hand, does not make use of qualitative or quantitative methods of data collection . Instead, the researcher gathers relevant data through critical studies, systematic review and meta-analysis. 

Advantages of Empirical Research 

• Empirical research is flexible. In this type of systematic investigation, the researcher can adjust the research methodology including the data sample size, data gathering methods plus the data analysis methods as necessitated by the research process.
• It helps the research to understand how the research outcomes can be influenced by different research environments.
• Empirical research study helps the researcher to develop relevant analytical and observation skills that can be useful in dynamic research contexts.
• This type of research approach allows the researcher to control multiple research variables in order to arrive at the most relevant research outcomes.
• Empirical research is widely considered as one of the most authentic and competent research designs.
• It improves the internal validity of traditional research using a variety of experiments and research observation methods.

Disadvantages of Empirical Research 

• An empirical research study is time-consuming because the researcher needs to gather the empirical data from multiple resources which typically takes a lot of time.
• It is not a cost-effective research approach. Usually, this method of research incurs a lot of cost because of the monetary demands of the field research.
• It may be difficult to gather the needed empirical data sample because of the multiple data gathering methods employed in an empirical research study.
• It may be difficult to gain access to some communities and firms during the data gathering process and this can affect the validity of the research.
• The report from an empirical research study is intensive and can be very lengthy in nature.

Conclusion 

Empirical research is an important method of systematic investigation because it gives the researcher the opportunity to test the validity of different assumptions, in the form of hypotheses, before arriving at any findings. Hence, it is a more research approach. 

There are different quantitative and qualitative methods of data gathering employed during an empirical research study based on the purpose of the research which include surveys, experiments, and various observatory methods. Surveys are one of the most common methods or empirical data collection and they can be administered online or physically. 

You can use Formplus to create and administer your online empirical research survey. Formplus allows you to create survey forms that you can share with target respondents in order to obtain valuable feedback about your research context, question or subject. 

In the form builder, you can add different fields to your survey form and you can also modify these form fields to suit your research process. Sign up to Formplus to access the form builder and start creating powerful online empirical research survey forms. 

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• advantage of empirical research
• disadvantages of empirical resarch
• empirical research characteristics
• empirical research cycle
• empirical research method
• example of empirical research
• uses of empirical research
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Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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• Published: 10 November 2020

Case study research for better evaluations of complex interventions: rationale and challenges

• Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
• Judith Green 2 ,
• Chrysanthi Papoutsi 1 ,
• Jamie Murdoch 3 ,
• Mark Petticrew 4 ,
• Trish Greenhalgh 1 ,
• Benjamin Hanckel 5 &
• Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

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The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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Team-Based Learning Analytics: An Empirical Case Study

Affiliation.

• 1 Y.Y.J. Koh is research associate, Medical Education and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; ORCID: https://orcid.org/0000-0003-2062-5617. H.G. Schmidt is professor of psychology, Institute of Medical Education Research, Erasmus University Medical Center, Rotterdam, the Netherlands; ORCID: https://orcid.org/0000-0001-8706-0978. N. Low-Beer is professor and director, Medical Education and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; ORCID: https://orcid.org/0000-0002-6801-0091. J.I. Rotgans is assistant professor, Medical Education and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; ORCID: https://orcid.org/0000-0003-4043-8261.
• PMID: 31972678
• PMCID: PMC7242170
• DOI: 10.1097/ACM.0000000000003157

Many medical schools that have implemented team-based learning (TBL) have also incorporated an electronic learning architecture, commonly referred to as a learning management system (LMS), to support the instructional process. However, one LMS feature that is often overlooked is the LMS's ability to record data that can be used for further analysis. In this article, the authors present a case study illustrating how one medical school used data that are routinely collected via the school's LMS to make informed decisions. The case study started with one instructor's observation that some teams in one of the undergraduate medical education learning modules appeared to be struggling during one of the team activities; that is, some teams seemed unable to explain or justify their responses to items on the team readiness assurance test (tRAT). Following this observation, the authors conducted 4 analyses. Their analyses demonstrate how LMS-generated and recorded data can be used in a systematic manner to investigate issues in the real educational environment. The first analysis identified a team that performed significantly poorer on the tRAT. A subsequent analysis investigated whether the weaker team's poorer performance was consistent over a whole module. Findings revealed that the weaker team performed poorer on the majority of the TBL sessions. Further investigation using LMS data showed that the weaker performance was due to the lack of preparation of one individual team member (rather than a collective poor tRAT performance). Using the findings obtained from this case study, the authors hope to convey how LMS data are powerful and may form the basis of evidence-based educational decision making.

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This project aimed to understand how researchers can contribute to social-ecological transformations. We picked​ a case that had already been thoroughly studied: the benthic fisheries policy that took place in Chile after the dictatorship. The policy shifted from an open access regime to management areas granting user rights to associations of fishers. In order to track cause-consequence relations between events we used a combined approach. We based our categories on realist social theory that appointed us to reconstructing the history of emergence of agency, structure and ideas; for operationalizing this principle we used the social-ecological action situation (SE-AS) tool – developed in our own team – which tracks specific emergent outcomes from interaction between specific agents. Data obtained from secondary sources was complemented by long distance interviews with key stakeholders.

Pamir small-holder farming

In the Pamir mountains of Tajikistan and Afghanistan, culture and nature have coevolved in deeply intertwined ways. These so-called ‘biocultural’ landscapes are also characterized by persistent poverty, which is the phenomenon that we study in this case. Our research seeks to understand what are development pathways that can unlock the vicious cycle of poverty and degradation, but do not erode the rich biocultural diversity of the Pamirs. Research methods include participant observation (harvesting, cooking and celebrating food), ethnographic methods, and interviews with farmers and development practitioners. The place-based case study methods in this case have been used to inform a dynamical systems model of poverty traps, which theorizes different effects of various development intervention styles.

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• Published: 16 April 2024

Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study

• Luning Shao 1 ,
• Yuxin Miao 2 ,
• Shengce Ren 3 ,
• Sanfa Cai 4 &
• Fei Fan   ORCID: orcid.org/0000-0001-8756-5140 5 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  519 ( 2024 ) Cite this article

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• Business and management

Entrepreneurship education (EE) has rapidly evolved within higher education and has emerged as a pivotal mechanism for cultivating innovative and entrepreneurial talent. In China, while EE has made positive strides, it still faces a series of practical challenges. These issues cannot be effectively addressed solely through the efforts of universities. Based on the triple helix (TH) theory, this study delves into the unified objectives and practical content of EE in Chinese higher education. Through a comprehensive literature review on EE, coupled with educational objectives, planned behavior, and entrepreneurship process theories, this study introduces the 4H objective model of EE. 4H stands for Head (mindset), Hand (skill), Heart (attitude), and Help (support). Additionally, the research extends to a corresponding content model that encompasses entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate as tools for achieving the objectives. Based on a single-case approach, this study empirically explores the application of the content model at T-University. Furthermore, this paper elucidates how the university plays a role through the comprehensive development of entrepreneurial learning, practices, services, and climate in nurturing numerous entrepreneurs and facilitating the flourishing of the regional entrepreneurial ecosystem. This paper provides important contributions in its application of TH theory to develop EE within the Chinese context, and it provides clear guidance by elucidating the core objectives and practical content of EE. The proposed conceptual framework serves not only as a guiding tool but also as a crucial conduit for fostering the collaborative development of the EE ecosystem. To enhance the robustness of the framework, this study advocates strengthening empirical research on TH theory through multiple and comparative case studies.

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Introduction

In the era of the knowledge economy, entrepreneurship has emerged as a fundamental driver of social and economic development. As early as 1911, Schumpeter proposed the well-known theory of economic development, wherein he first introduced the concepts of entrepreneurship and creative destruction as driving forces behind socioeconomic development. Numerous endogenous growth theories, such as the entrepreneurial ecosystem mechanism of Acs et al. ( 2018 ), which also underscores the pivotal role of entrepreneurship in economic development, are rooted in Schumpeter’s model. Recognized as a key means of cultivating entrepreneurs and enhancing their capabilities (Jin et al., 2023 ), entrepreneurship education (EE) has received widespread attention over the past few decades, especially in the context of higher education (Wong & Chan, 2022 ).

Driven by international trends and economic demands, China places significant emphasis on nurturing innovative talent and incorporating EE into the essential components of its national education system. The State Council’s “Implementation Opinions on Deepening the Reform of Innovation and Entrepreneurship Education in Higher Education” (hereafter referred to as the report) underscores the urgent necessity for advancing reforms in innovation and EE in higher education institutions. This initiative aligns with the national strategy of promoting innovation-driven development and enhancing economic quality and efficiency. Furthermore, institutions at various levels are actively and eagerly engaging in EE.

Despite the positive strides made in EE in China, its development still faces a series of formidable practical challenges. As elucidated in the report, higher education institutions face challenges such as a delay in the conceptualization of EE, inadequate integration with specialized education, and a disconnect from practical applications. Furthermore, educators exhibit a deficiency in awareness and capabilities, which manifests in a singular and less effective teaching methodology. The shortage of practical platforms, guidance, and support emphasizes the pressing need for comprehensive innovation and EE systems. These issues necessitate collaborative efforts from universities, industry, and policymakers.

Internationally established solutions for the current challenges have substantially matured, providing invaluable insights and guidance for the development of EE in the Chinese context. In the late 20th century, the concept of the entrepreneurial university gained prominence (Etzkowitz et al., 2000 ). Then, entrepreneurial universities expanded their role from traditional research and teaching to embrace a “third mission” centered on economic development. This transformation entailed fostering student engagement in entrepreneurial initiatives by offering resources and guidance to facilitate the transition of ideas into viable entrepreneurial ventures. Additionally, these entrepreneurial universities played a pivotal role in advancing the triple helix (TH) model (Henry, 2009 ). The TH model establishes innovation systems that facilitate knowledge conversion into economic endeavors by coordinating the functions of universities, government entities, and industry. The robustness of this perspective has been substantiated through comprehensive theoretical and empirical investigations (Mandrup & Jensen, 2017 ).

Therefore, this study aims to explore how EE in Chinese universities can adapt to new societal trends and demands through the guidance of TH theory. This research involves two major themes: educational objectives and content. Educational objectives play a pivotal role in regulating the entire process of educational activities, ensuring alignment with the principles and norms of education (Whitehead, 1967 ), while content provides a practical pathway to achieving these objectives. Specifically, the study has three pivotal research questions:

RQ1: What is the present landscape of EE research?

RQ2: What unified macroscopic goals should be formulated to guide EE in Chinese higher education?

RQ3: What specific EE system should be implemented to realize the identified goals in Chinese higher education?

The structure of this paper is as follows: First, we conduct a comprehensive literature review on EE to answer RQ1 , thereby establishing a robust theoretical foundation. Second, we outline our research methodology, encompassing both framework construction and case studies and providing a clear and explicit approach to our research process. Third, we derive the objectives and content model of EE guided by educational objectives, entrepreneurial motivations, and entrepreneurial process theories. Fourth, focusing on a typical university in China as our research subject, we conduct a case study to demonstrate the practical application of our research framework. Finally, we end the paper with the findings for RQ2 and RQ3 , discussions on the framework, and conclusions.

Literature review

The notion of TH first appeared in the early 1980s, coinciding with the global transition from an industrial to a knowledge-based economy (Cai & Etzkowitz, 2020 ). At that time, the dramatic increase in productivity led to overproduction, and knowledge became a valuable mechanism for driving innovation and economic growth (Mandrup & Jensen, 2017 ). Recognizing the potential of incorporating cutting-edge university technologies into industry and facilitating technology transfer and innovation, the US government took proactive steps to enhance the international competitiveness of American industries. This initiative culminated in the enactment of relevant legislation in 1980, which triggered a surge in technology transfer, patent licensing, and the establishment of new enterprises within the United States. Subsequently, European and Asian nations adopted similar measures, promoting the transformation of universities’ identity (Grimaldi et al., 2011 ). Universities assumed a central role in technology transfer, the formation of businesses, and regional revitalization within the knowledge society rather than occupying a secondary position within the industrial community. The conventional one-to-one relationships between universities, companies, and the government evolved into a dynamic TH model (Cai & Etzkowitz, 2020 ). Beyond their traditional roles in knowledge creation, wealth production, and policy coordination, these sectors began to engage in multifaceted interactions, effectively “playing the role of others” (Ranga & Etzkowitz, 2013 ).

The TH model encompasses three fundamental elements: 1) In a knowledge-based society, universities assume a more prominent role in innovation than in industry; 2) The three entities engage in collaborative relationships, with innovation policies emerging as a result of their mutual interactions rather than being solely dictated by the government; and 3) Each entity, while fulfilling its traditional functions, also takes on the roles of the other two parties (Henry, 2009 ). This model is closely aligned with EE.

On the one hand, EE can enhance the effectiveness of TH theory by strengthening the links between universities, industry, and government. The TH concept was developed based on entrepreneurial universities. The emerging entrepreneurial university model integrates economic development as an additional function. Etzkowitz’s research on the entrepreneurial university identified a TH model of academia-industry-government relations implemented by universities in an increasingly knowledge-based society (Galvao et al., 2019 ). Alexander and Evgeniy ( 2012 ) articulated that entrepreneurial universities are crucial to the implementation of triple-helix arrangements and that by integrating EE into their curricula, universities have the potential to strengthen triple-helix partnerships and boost the effectiveness of the triple-helix model.

On the other hand, TH theory also drives EE to achieve high-quality development. Previously, universities were primarily seen as sources of knowledge and human resources. However, they are now also regarded as reservoirs of technology. Within EE and incubation programs, universities are expanding their educational capabilities beyond individual education to shaping organizations (Henry, 2009 ). Surpassing their role as sources of new ideas for existing companies, universities blend their research and teaching processes in a novel way, emerging as pivotal sources for the formation of new companies, particularly in high-tech domains. Furthermore, innovation within one field of the TH influences others (Piqué et al., 2020 ). An empirical study by Alexander and Evgeniy ( 2012 ) outlined how the government introduced a series of initiatives to develop entrepreneurial universities, construct innovation infrastructure, and foster EE growth.

Overview of EE

EE occupies a crucial position in driving economic advancement, and this domain has been the focal point of extensive research. Fellnhofer ( 2019 ) examined 1773 publications from 1975 to 2014, introducing a more closely aligned taxonomy of EE research. This taxonomy encompasses eight major clusters: social and policy-driven EE, human capital studies related to self-employment, organizational EE and TH, (Re)design and evaluation of EE initiatives, entrepreneurial learning, EE impact studies, and the EE opportunity-related environment at the organizational level. Furthermore, Mohamed and Sheikh Ali ( 2021 ) conducted a systematic literature review of 90 EE articles published from 2009 to 2019. The majority of these studies focused on the development of EE (32%), followed by its benefits (18%) and contributions (12%). The selected research also addressed themes such as the relationship between EE and entrepreneurial intent, the effectiveness of EE, and its assessment (each comprising 9% of the sample).

Spanning from 1975 to 2019, these two reviews offer a comprehensive landscape of EE research. The perspective on EE has evolved, extending into multiple dimensions (Zaring et al., 2021 ). However, EE does not always achieve the expected outcomes, as challenges such as limited student interest and engagement as well as persistent negative attitudes are often faced (Mohamed & Sheikh Ali, 2021 ). In fact, the challenges faced by EE in most countries may be similar. However, the solutions may vary due to contextual differences (Fred Awaah et al., 2023 ). Furthermore, due to this evolution, there is a need for a more comprehensive grasp of pedagogical concepts and the foundational elements of modern EE (Hägg & Gabrielsson, 2020 ). Based on the objectives of this study, four specific themes were chosen for an in-depth literature review: the objectives, contents and methods, outcomes, and experiences of EE.

Objectives of EE

The objectives of EE may provide significant guidance for its implementation and the assessment of its effectiveness, and EE has evolved to form a diversified spectrum. Mwasalwiba ( 2010 ) presented a multifaceted phenomenon in which EE objectives are closely linked to entrepreneurial outcomes. These goals encompass nurturing entrepreneurial attitudes (34%), promoting new ventures (27%), contributing to local community development (24%), and imparting entrepreneurial skills (15%). Some current studies still emphasize particular dimensions of these goals, such as fostering new ventures or value creation (Jones et al., 2018 ; Ratten & Usmanij, 2021 ). These authors further stress the significance of incorporating practical considerations related to the business environment, which prompts learners to contemplate issues such as funding and resource procurement. This goal inherently underscores the importance of entrepreneurial thinking and encourages learners to transition from merely being students to developing entrepreneurial mindsets.

Additionally, Kuratko and Morris ( 2018 ) posit that the goal of EE should not be to produce entrepreneurs but to cultivate entrepreneurial mindsets in students, equipping them with methods for thinking and acting entrepreneurially and enabling them to perceive opportunities rapidly in uncertain conditions and harness resources as entrepreneurs would. While the objectives of EE may vary based on the context of the teaching institution, the fundamental goal is increasingly focused on conveying and nurturing an entrepreneurial mindset among diverse stakeholders. Hao’s ( 2017 ) research contends that EE forms a comprehensive system in which multidimensional educational objectives are established. These objectives primarily encompass cultivating students’ foundational qualities and innovative entrepreneurial personalities, equipping them with essential awareness of entrepreneurship, psychological qualities conducive to entrepreneurship, and a knowledge structure for entrepreneurship. Such a framework guides students towards independent entrepreneurship based on real entrepreneurial scenarios.

Various studies and practices also contain many statements about entrepreneurial goals. The Entrepreneurship Competence Framework, which was issued by the EU in 2016, delineates three competency domains: ideas and opportunities, resources and action. Additionally, the framework outlines 15 specific entrepreneurship competencies (Jun, 2017 ). Similarly, the National Content Standards for EE published by the US Consortium encompass three overarching strategies for articulating desired competencies for aspiring entrepreneurs: entrepreneurial skills, ready skills, and business functions (Canziani & Welsh, 2021 ). First, entrepreneurial skills are unique characteristics, behaviors, and experiences that distinguish entrepreneurs from ordinary employees or managers. Second, ready skills, which include business and entrepreneurial knowledge and skills, are prerequisites and auxiliary conditions for EE. Third, business functions help entrepreneurs create and operate business processes in business activities. These standards explain in the broadest terms what students need to be self-employed or to develop and grow a new venture. Although entrepreneurial skills may be addressed in particular courses offered by entrepreneurship faculties, it is evident that business readiness and functional skills significantly contribute to entrepreneurial success (Canziani & Welsh, 2021 ).

Contents and methods of EE

The content and methods employed in EE are pivotal factors for ensuring the delivery of high-quality entrepreneurial instruction, and they have significant practical implications for achieving educational objectives. The conventional model of EE, which is rooted in the classroom setting, typically features an instructor at the front of the room delivering concepts and theories through lectures and readings (Mwasalwiba, 2010 ). However, due to limited opportunities for student engagement in the learning process, lecture-based teaching methods prove less effective at capturing students’ attention and conveying new concepts (Rahman, 2020 ). In response, Okebukola ( 2020 ) introduced the Culturo-Techno-Contextual Approach (CTCA), which offers a hybrid teaching and learning method that integrates cultural, technological, and geographical contexts. Through a controlled experiment involving 400 entrepreneurship development students from Ghana, CTCA has been demonstrated to be a model for enhancing students’ comprehension of complex concepts (Awaah, 2023 ). Furthermore, learners heavily draw upon their cultural influences to shape their understanding of EE, emphasizing the need for educators to approach the curriculum from a cultural perspective to guide students in comprehending entrepreneurship effectively.

In addition to traditional classroom approaches, research has highlighted innovative methods for instilling entrepreneurial spirit among students. For instance, students may learn from specific university experiences or even engage in creating and running a company (Kolb & Kolb, 2011 ). Some scholars have developed an educational portfolio that encompasses various activities, such as simulations, games, and real company creation, to foster reflective practice (Neck & Greene, 2011 ). However, some studies have indicated that EE, when excessively focused on applied and practical content, yields less favorable outcomes for students aspiring to engage in successful entrepreneurship (Martin et al., 2013 ). In contrast, students involved in more academically oriented courses tend to demonstrate improved intellectual skills and often achieve greater success as entrepreneurs (Zaring et al., 2021 ). As previously discussed, due to the lack of a coherent theoretical framework in EE, there is a lack of uniformity and consistency in course content and methods (Ribeiro et al., 2018 ).

Outcomes of EE

Research on the outcomes of EE is a broad and continually evolving field, with most related research focusing on immediate or short-term impact factors. For example, Anosike ( 2019 ) demonstrated the positive effect of EE on human capital, and Chen et al. ( 2022 ) proposed that EE significantly moderates the impact of self-efficacy on entrepreneurial competencies in higher education students through an innovative learning environment. In particular, in the comprehensive review by Kim et al. ( 2020 ), six key EE outcomes were identified: entrepreneurial creation, entrepreneurial intent, opportunity recognition, entrepreneurial self-efficacy and orientation, need for achievement and locus of control, and other entrepreneurial knowledge. One of the more popular directions is the examination of the impact of EE on entrepreneurial intentions. Bae et al. ( 2014 ) conducted a meta-analysis of 73 studies to examine the relationship between EE and entrepreneurial intention and revealed little correlation. However, a meta-analysis of 389 studies from 2010 to 2020 by Zhang et al. ( 2022 ) revealed a positive association between the two variables.

Nabi et al. ( 2017 ) conducted a systematic review to determine the impact of EE in higher education. Their findings highlight that studies exploring the outcomes of EE have primarily concentrated on short-term and subjective assessments, with insufficient consideration of longer-term effects spanning five or even ten years. These longer-term impacts encompass factors such as the nature and quantity of startups, startup survival rates, and contributions to society and the economy. As noted in the Eurydice report, a significant impediment to advancing EE is the lack of comprehensive delineation concerning education outcomes (Bourgeois et al., 2016 ).

Experiences in the EE system

With the deepening exploration of EE, researchers have turned to studying university-centered entrepreneurship ecosystems (Allahar and Sookram, 2019 ). Such ecosystems are adopted to fill gaps in “educational and economic development resources”, such as entrepreneurship curricula. A growing number of universities have evolved an increasingly complex innovation system that extends from technology transfer offices, incubators, and technology parks to translational research and the promotion of EE across campuses (Cai & Etzkowitz, 2020 ). In the university context, the entrepreneurial ecosystem aligns with TH theory, in which academia, government, and industry create a trilateral network and hybrid organization (Ranga & Etzkowitz, 2013 ).

The EE system is also a popular topic in China. Several researchers have summarized the Chinese experience in EE, including case studies and overall experience, such as the summary of the progress and system development of EE in Chinese universities over the last decade by Weiming et al. ( 2013 ) and the summary of the Chinese experience in innovation and EE by Maoxin ( 2017 ). Other researchers take an in-depth look at the international knowledge of EE, such as discussions on the EE system of Denmark by Yuanyuan ( 2015 ), analyzes of the ecological system of EE at the Technical University of Munich by Yubing and Ziyan ( 2015 ), and comparisons of international innovation and EE by Ke ( 2017 ).

In general, although there has been considerable discussion on EE, the existing body of work has not properly addressed the practical challenges faced by EE in China. On the one hand, the literature is fragmented and has not yet formed a unified and mature theoretical framework. Regarding what should be taught and how it can be taught and assessed, the answers in related research are ambiguous (Hoppe, 2016 ; Wong & Chan, 2022 ). On the other hand, current research lacks empirical evidence in the context of China, and guidance on how to put the concept of EE into practice is relatively limited. These dual deficiencies impede the effective and in-depth development of EE in China. Consequently, it is imperative to comprehensively redefine the objectives and contents of EE to provide clear developmental guidance for Chinese higher education institutions.

Research methodology

To answer the research questions, this study employed a comprehensive approach by integrating both literature-based and empirical research methods. The initial phase focused on systematically reviewing the literature related to entrepreneurial education, aiming to construct a clear set of frameworks for the objectives and content of EE in higher education institutions. The second phase involved conducting a case study at T-University, in which the theoretical frameworks were applied to a real-world context. This case not only contributed to validating the theoretical constructs established through the literature review but also provided valuable insights into the practical operational dynamics of entrepreneurial education within the specific university setting.

Conceptual framework stage

This paper aims to conceptualize the objective and content frameworks for EE. The methodology sequence is as follows: First, we examine the relevant EE literature to gain insights into existing research themes. Subsequently, we identify specific research articles based on these themes, such as “entrepreneurial intention”, “entrepreneurial self-efficacy”, and “entrepreneurial approach”, among others. Third, we synthesize the shared objectives of EE across diverse research perspectives through an analysis of the selected literature. Fourth, we construct an objective model for EE within higher education by integrating Bloom’s educational objectives ( 1956 ) and Gagne’s five learning outcomes ( 1984 ), complemented by entrepreneurship motivation and process considerations. Finally, we discuss the corresponding content framework.

Case study stage

To further elucidate the conceptual framework, this paper delves into the methods for the optimization of EE in China through a case analysis. Specifically, this paper employs a single-case approach. While a single case study may have limited external validity (Onjewu et al., 2021 ), if a case study informs current theory and conceptualizes the explored issues, it can still provide valuable insights from its internal findings (Buchanan, 1999 ).

T-University, which is a comprehensive university in China, is chosen as the subject of the case study for the following reasons. First, T-University is located in Shanghai, which is a Chinese international technological innovation center approved by the State Council. Shanghai’s “14th Five-Year Plan” proposes the establishment of a multichannel international innovation collaboration platform and a global innovation cooperation network. Second, T-University has initiated curriculum reforms and established a regional knowledge economy ecosystem by utilizing EE as a guiding principle, which aligns with the characteristics of its geographical location, history, culture, and disciplinary settings. This case study will showcase T-University’s experiences in entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate, elucidating the positive outcomes of this triangular interaction and offering practical insights for EE in other contexts.

The data collection process of this study was divided into two main stages: field research and archival research. The obtained data included interview transcripts, field notes, photos, internal documents, websites, reports, promotional materials, and published articles. In the initial stage, we conducted a 7-day field trip, including visits to the Innovation and Entrepreneurship Institute, the Career Development Centre, the Academic Affairs Office, and the Graduate School. Moreover, we conducted semistructured interviews with several faculty members and students involved in entrepreneurship education at the university to understand the overall state of implementation of entrepreneurship education at the university. In the second stage, we contacted the Academic Affairs Office and the Student Affairs Office at the university and obtained internal materials related to entrepreneurship education. Additionally, we conducted a comprehensive collection and created a summary of publicly available documents, official school websites, public accounts, and other electronic files. To verify the validity of the multisource data, we conducted triangulation and ultimately used consistent information as the basis for the data analysis.

For the purpose of our study, thematic analysis was employed to delve deeply into the TH factors, the objective and content frameworks, and their interrelationships. Thematic analysis is a method for identifying, analyzing, and reporting patterns within data. This approach emphasizes a comprehensive interpretation of the data, as it extracts information from multiple perspectives and derives valuable conclusions through summary and induction (Onjewu et al., 2021 ). Therefore, thematic analysis likely serves as the foundation for most other qualitative data analysis methods (Willig, 2013 ). In this study, three researchers individually conducted rigorous analyses and comprehensive reviews to ensure the accuracy and reliability of the data. Subsequently, they engaged in collaborative discussions to explore their differences and ultimately reach a consensus.

Framework construction

Theoretical basis of ee in universities.

The study is grounded in the theories of educational objectives, planned behavior, and the entrepreneurial process. Planned behavior theory can serve to elucidate the emergence of entrepreneurial activity, while entrepreneurial process theory can be used to delineate the essential elements of successful entrepreneurship.

Theory of educational objectives. The primary goal of education is to assist students in shaping their future. Furthermore, education should directly influence students and facilitate their future development. Education can significantly enhance students’ prospects by imparting specific skills and fundamental principles and cultivating the correct attitudes and mindsets (Bruner, 2009 ). According to “The Aims of Education” by Whitehead, the objective of education is to stimulate creativity and vitality. Gagne identifies five learning outcomes that enable teachers to design optimal learning conditions based on the presentation of these outcomes, encompassing “attitude,” “motor skills,” “verbal information,” “intellectual skills,” and “cognitive strategies”. Bloom et al. ( 1956 ) argue that education has three aims, which concern the cognitive, affective, and psychomotor domains. Gedeon ( 2017 ) posits that EE involves critical input and output elements. The key objectives encompass mindset (Head), skill (hand), attitude (heart), and support (help). The input objectives include EE teachers, resources, facilities, courses, and teaching methods. The output objectives encompass the impacts of the input factors, such as the number of students, the number of awards, and the establishment of new companies. The primary aims of Gedeon ( 2017 ) correspond to those of Bloom et al. ( 1956 ).

Theory of planned behavior. The theory of planned behavior argues that human behavior is the outcome of well-thought-out planning (Ajzen, 1991 ). Human behavior depends on behavioral intentions, which are affected by three main factors. The first is derived from the individual’s “attitude” towards taking a particular action; the second is derived from the influence of “subjective norms” from society; and the third is derived from “perceived behavioral control” (Ajzen, 1991 ). Researchers have adopted this theory to study entrepreneurial behavior and EE.

Theory of the entrepreneurship process. Researchers have proposed several entrepreneurial models, most of which are processes (Baoshan & Baobao, 2008 ). The theory of the entrepreneurship process focuses on the critical determinants of entrepreneurial success. The essential variables of the entrepreneurial process model significantly impact entrepreneurial performance. Timmons et al. ( 2004 ) argue that successful entrepreneurial activities require an appropriate match among opportunities, entrepreneurial teams, resources, and a dynamic balance as the business develops. Their model emphasizes flexibility and equilibrium, and it is believed that entrepreneurial activities change with time and space. As a result, opportunities, teams, and resources will be unbalanced and need timely adjustment.

4H objective model of EE

Guided by TH theory, the objectives of EE should consider universities’ transformational identity in the knowledge era and promote collaboration among students, faculty, researchers, and external players (Mandrup & Jensen, 2017 ). Furthermore, through a comprehensive analysis of the literature and pertinent theoretical underpinnings, the article introduces the 4H model for the EE objectives, as depicted in Fig. 1 .

The 4H objective model of entrepreneurship education.

The model comprises two levels. The first level pertains to outcomes at the entrepreneurial behavior level, encompassing entrepreneurial intention and entrepreneurial performance. These two factors support universities’ endeavors to nurture individuals with an entrepreneurial mindset and potential and contribute to the region’s growth of innovation and entrepreneurship. The second level pertains to fundamentals, which form the foundation of the first level. The article defines these as the 4H model, representing mindset (Head), skill (Hand), attitude (Heart), and support (Help). This model integrates key theories, including educational objectives, the entrepreneurship process, and planned behavior.

First, according to the theory of educational objectives, the cognitive, emotional, and skill objectives proposed by Bloom et al. ( 1956 ) correspond to the key goals of education offered by Gedeon ( 2017 ), namely, Head, Hand, and Heart; thus, going forward, in this study, these three objectives are adopted. Second, according to the theory of planned behavior, for the promotion of entrepreneurial intention, reflection on the control of beliefs, social norms, and perceptual behaviors must be included. EE’s impact on the Head, Hand, and Heart will promote the power of entrepreneurs’ thoughts and perceptual actions. Therefore, this approach is beneficial for enhancing entrepreneurial intentions. Third, according to entrepreneurship process theory, entrepreneurial performance is affected by various factors, including entrepreneurial opportunities, teams, and resources. Consideration of the concepts of Head, Hand, and Heart can enhance entrepreneurial opportunity recognition and entrepreneurial team capabilities. However, as the primary means of obtaining external resources, social networks play an essential role in improving the performance of innovation and entrepreneurship companies (Gao et al., 2023 ). Therefore, an effective EE program should tell students how to take action, connect them with those who can help them succeed (Ronstadt, 1985 ), and help them access the necessary resources. If EE institutions can provide relevant help, they will consolidate entrepreneurial intentions and improve entrepreneurial performance, enabling the EE’s objective to better support the Head, Hand, and Heart.

Content model of EE

EE necessitates establishing a systematic implementation framework to achieve the 4H objectives. Current research on EE predominantly focuses on two facets: one focuses on EE methods to improve students’ skills, and the other focuses on EE outcome measurements, which consider the impact of EE on different stakeholders. Based on this, to foster innovation in EE approaches and enable long-term sustainable EE outcomes, the 4H Model of EE objectives mandates that pertinent institutions provide entrepreneurial learning, entrepreneurial practice, startup services, and a suitable entrepreneurial climate. These components constitute the four integral facets of the content model for EE, as depicted in Fig. 2 .

The content model of entrepreneurship education.

Entrepreneurial learning

Entrepreneurial learning mainly refers to the learning of innovative entrepreneurial knowledge and theory. This factor represents the core of EE and can contribute significantly to the Head component. It can also improve the entrepreneurial thinking ability of academic subjects through classroom teaching, lectures, information reading and analysis, discussion, debates, etc. Additionally, it can positively affect the Hand and Heart elements of EE.

Entrepreneurial practice

Entrepreneurial practice mainly refers to academic subjects comprehensively enhancing their cognition and ability by participating in entrepreneurial activities. This element is also a key component of EE and plays a significant role in the cultivation of the Hand element. Entrepreneurial practice is characterized by participation in planning and implementing entrepreneurial programs, competitions, and simulation activities. Furthermore, it positively impacts EE’s Head, Heart, and Help factors.

Startup services

Startup services mainly refer to entrepreneurial-related support services provided by EE institutions, which include investment and financing, project declaration, financial and legal support, human resources, marketing, and intermediary services. These services can improve the success of entrepreneurship projects. Therefore, they can reinforce the expectations of entrepreneurs’ success and positively impact the Heart, Hand, and Head objectives of EE.

Entrepreneurial climate

The entrepreneurial climate refers to the entrepreneurial environment created by EE institutions and their community and is embodied mainly in the educational institutions’ external and internal entrepreneurial culture and ecology. The environment can impact the entrepreneurial attitude of educated individuals and the Heart objective of EE. Additionally, it is beneficial for realizing EE’s Head, Hand, and Help goals.

Case study: EE practice of T-University

Overview of ee at t-university.

T-University is one of the first in China to promote innovation and EE. Since the 1990s, a series of policies have been introduced, and different platforms have been set up. After more than 20 years of teaching, research, and practice, an innovation and entrepreneurship education system with unique characteristics has gradually evolved. The overall goal of this system is to ensure that 100% of students receive such education, with 10% of students completing the program and 1% achieving entrepreneurship with a high-quality standard. The overall employment rate of 2020 graduates reached 97.49%. In recent years, the proportion of those pursuing entrepreneurship has been more than 1% almost every year. The T-Rim Knowledge-Based Economic Circle, an industrial cluster formed around knowledge spillover from T-University’s dominant disciplines, employs more than 400 T-University graduates annually.

In 2016, T-University established the School of Innovation & Entrepreneurship, with the president serving as its dean. This school focuses on talent development and is pivotal in advancing innovation-driven development strategies. It coordinates efforts across various departments and colleges to ensure comprehensive coverage of innovation and EE, the integration of diverse academic disciplines, and the transformation of interdisciplinary scientific and technological advancements (see Fig. 3 ).

T-University innovation and entrepreneurship education map.

T-University is dedicated to integrating innovation and EE into every stage of talent development. As the guiding framework for EE, the university has established the Innovation and EE sequence featuring “three-dimensional, linked, and cross-university cooperation” with seven educational elements. These elements include the core curriculum system of innovation and entrepreneurship, the “one top-notch and three excellences” and experimental zones of innovation and entrepreneurship talent cultivation model, the four-level “China-Shanghai-University-School” training programs for innovation and entrepreneurship, four-level “International-National-Municipal-University” science and technology competitions, four-level “National-Municipal-University-School” innovation and entrepreneurship practice bases, three-level “Venture Valley-Entrepreneurship Fund-Industry Incubation” startup services and a high-level teaching team with both full-time and part-time personnel.

T-University has implemented several initiatives. First, the university has implemented 100% student innovation and EE through reforming the credit setting and curriculum system. Through the Venture Valley class, mobile class, and “joint summer school”, more than 10% of the students completed the Innovation and EE program. Moreover, through the professional reform pilot and eight professional incubation platforms in the National Science and Technology Park of T-University and other measures, 1% of the students established high-quality entrepreneurial enterprises. Second, the university is committed to promoting the integration of innovation and entrepreneurship and training programs, exploring and practising a variety of innovative talent cultivation models, and adding undergraduate innovation ability development as a mandatory component of the training program. In addition, pilot reforms have been conducted in engineering, medicine, and law majors, focusing on integrating research and education.

T-University has constructed a high-level integrated innovation and entrepreneurship practice platform by combining internal and external resources. This platform serves as the central component in Fig. 3 , forming a sequence of innovation and entrepreneurship practice opportunities, including 1) the On-and-off Campus Basic Practice Platform, 2) the Entrepreneurship Practice Platform with the Integration of Production, Learning, and Research, 3) the Transformation Platform of Major Scientific Research Facilities and Achievements, and 4) the Strategic Platform of the T-Rim Knowledge-Based Economic Circle. All these platforms are accessible to students based on their specific tasks and objectives.

Moreover, the university has reinforced its support for entrepreneurship and collaborated with local governments in Sichuan, Dalian, and Shenzhen to establish off-campus bases jointly. In 2016, in partnership with other top universities in China, the university launched the Innovation and Entrepreneurship Alliance of Universities in the Yangtze River Delta. This alliance effectively brings together government bodies, businesses, social communities, universities, and funding resources in the Yangtze River Delta, harnessing the synergistic advantages of these institutions. In 2018, the university assumed the director role for the Ministry of Education’s Steering Committee for Innovation and Entrepreneurship. Through collaborations with relevant government agencies and enterprises, T-University has continued its efforts to reform and advance innovation and EE, establishing multiple joint laboratories to put theory into practice.

Startup service

In terms of entrepreneurial services, T-University has focused on the employment guidance center and the science and technology Park, working closely with the local industrial and commercial bureaus in the campus area to provide centralized entrepreneurial services. Through entities such as the Shanghai Municipal College Entrepreneurship Guidance Station, entrepreneurship seedling gardens, the science and technology park, and off-campus bases such as the entrepreneurship valley, the university has established a full-cycle service system that is tailored to students’ innovative and entrepreneurial activities, providing continuous professional guidance and support from the early startup stage to maturity.

Notably, the T-University Science and Technology Park has set up nine professional incubation service platforms that cover investment and financing, human resources, entrepreneurship training, project declaration, financial services, professional intermediaries, market promotion, advanced assessment, and the labor union. Moreover, the Technology Park has established a corporate service mechanism for liaison officers, counselors, and entrepreneurship mentors to ensure that enterprises receive comprehensive support and guidance. Through these services, T-University has successfully cultivated numerous high-tech backbone enterprises, such as New Vision Healthcare, Zhong Hui Ecology, Tongjie Technology, Tonglei Civil Engineering, and Tongchen Environmental Protection, which indicates the positive effect of these entrepreneurial services.

T-University places significant emphasis on fostering the entrepreneurial climate, which is effectively nurtured through the T-Rim Knowledge-Based Economic Circle and on-campus entrepreneurship activities. Moreover, T-University is dedicated to establishing and cultivating a dynamic T-Rim Knowledge-Based Economic Circle in strategic alignment with the district government and key agencies. This innovative ecosystem strategically centers around three prominent industrial clusters: the creative and design industry, the international engineering consulting services industry, and the new energy/materials and environmental technology industry. These industrial clusters provide fertile ground for graduates’ employment and entrepreneurial pursuits and have yielded remarkable economic outputs. In 2020, the combined value of these clusters surged to a staggering RMB 50 billion, with 80% of entrepreneurs being teachers, students, or alumni from T-University.

This commitment has led to the establishment of an intricate design industry chain featuring architectural design and urban planning design; it also supports services in automobile design, landscape design, software design, environmental engineering design, art media design, and associated services such as graphic production, architectural modeling, and engineering consulting.

The EE system at T-University

T-University has undertaken a comprehensive series of initiatives to promote EE, focusing on four key aspects: entrepreneurial learning, entrepreneurial practice, startup service, and the entrepreneurial climate. As of the end of 2021, the National Technology Park at T-University has cumulatively supported more than 3000 enterprises. Notably, the park has played a pivotal role in assisting more than 300 enterprises established by college students.

In its commitment to EE, the university maintains an open approach to engaging with society. Simultaneously, it integrates innovative elements such as technology, information, and talent to facilitate students’ entrepreneurial endeavors. Through the synergy between the university, government entities, and the market, EE cultivates a cadre of entrepreneurial talent. The convergence of these talents culminates in the formation of an innovative and creative industry cluster within the region, representing the tangible outcome of the university’s “disciplinary chain—technology chain—industry chain” approach to EE. This approach has gradually evolved into the innovative ecosystem of the T-Rim Knowledge-Based Economic Circle.

Findings and discussion

Unified macroscopic objectives of ee.

To date, a widespread consensus on defining EE in practical terms has yet to be achieved (Mwasalwiba, 2010 ; Nabi et al., 2017 ). Entrepreneurial education should strive towards a common direction, which is reflected in the agreement on educational objectives and recommended teaching methods(Aparicio et al., 2019 ). Mason and Arshed ( 2013 ) criticized that entrepreneurial education should teach about entrepreneurship rather than for entrepreneurship. Therefore, EE should not only focus on singular outcome-oriented aspects but also emphasize the cultivation of fundamental aspects such as cognition, abilities, attitudes, and skills.

This study embarks on a synthesis of the EE-related literature, integrating educational objective theory, planned behavior theory, and entrepreneurial process theory. The 4H model of EE objectives, which consists of basic and outcome levels, is proposed. This model aims to comprehensively capture the core elements of EE, addressing both students’ performance in entrepreneurial outcomes and their development of various aspects of foundational cognitive attributes and skills.

The basic level of the EE objective model includes the 4Hs, namely Head (mindset), Hand (skill), Heart (attitude), and Help (support). First, Head has stood out as a prominent learning outcome within EE over the past decade (Fretschner & Lampe, 2019 ). Attention given to the “Head” aspect not only highlights the development of individuals recognized as “entrepreneurs” (Mitra, 2017 ) but also underscores its role in complementing the acquisition of skills and practical knowledge necessary for initiating new ventures and leading more productive lives (Neck & Corbett, 2018 ).

Second, the Hand aspect also constitutes a significant developmental goal and learning outcome of EE. The trajectory of EE is evolving towards a focus on entrepreneurial aspects, and the learning outcomes equip students with skills relevant to entrepreneurship (Wong & Chan, 2022 ). Higher education institutions should go beyond fundamental principles associated with knowledge and actively cultivate students’ entrepreneurial skills and spirit.

Third, Heart represents EE objectives that are related to students’ psychological aspects, as students’ emotions, attitudes, and other affective factors impact their perception of entrepreneurship (Cao, 2021 ). Moreover, the ultimate goal of EE is to instill an entrepreneurial attitude and pave the way for future success as entrepreneurs in establishing new businesses and fostering job creation (Kusumojanto et al., 2021 ). Thus, the cultivation of this mindset is not only linked to the understanding of entrepreneurship but also intricately tied to the aspiration for personal fulfillment (Yang, 2013 ).

Fourth, entrepreneurship support (Help) embodies the goal of providing essential resource support to students to establish a robust foundation for their entrepreneurial endeavors. The establishment of a comprehensive support system is paramount for EE in universities. This establishment encompasses the meticulous design of the curriculum, the development of training bases, and the cultivation of teacher resources (Xu, 2017 ). A well-structured support system is crucial for equipping students with the necessary knowledge and skills to successfully navigate the complexities of entrepreneurship (Greene & Saridakis, 2008 ).

The outcome level of the EE objective model encompasses entrepreneurial intention and entrepreneurial performance, topics that have been extensively discussed in the previous literature. Entrepreneurial intention refers to individuals’ subjective willingness and plans for entrepreneurial behavior (Wong & Chan, 2022 ) and represents the starting point of the entrepreneurial process. Entrepreneurial performance refers to individuals’ actual behaviors and achievements in entrepreneurial activities (Wang et al., 2021 ) and represents the ultimate manifestation of entrepreneurial goals. In summary, the proposed 4H model of the EE objectives covers fundamental attitudes, cognition, skills, support, and ultimate outcomes, thus answering the question of what EE should teach.

Specific implementable system of EE

To facilitate the realization of EE goals, this study developed a corresponding content model as an implementable system and conducted empirical research through a case university. Guided by the 4H objectives, the content model also encompasses four dimensions: entrepreneurial learning, entrepreneurial practice, startup service, and entrepreneurial climate. Through a detailed exposition of the practical methods at T-university, this study provides support for addressing the question of how to teach EE.

In the traditional EE paradigm, there is often an overreliance on the transmission of theoretical knowledge, which leads to a deficiency in students’ practical experience and capabilities (Kremel and Wetter-Edman, 2019 ). Moreover, due to the rapidly changing and dynamic nature of the environment, traditional educational methods frequently become disconnected from real-world demands. In response to these issues, the approach of “learning by doing” has emerged as a complementary and improved alternative to traditional methods (Colombelli et al., 2022 ).

The proposed content model applies the “learning by doing” approach to the construction of the EE system. For entrepreneurial learning, the university has constructed a comprehensive innovation and EE chain that encompasses courses, experimental areas, projects, competitions, practice bases, and teaching teams. For entrepreneurial practice, the university has built a high-level, integrated innovation and entrepreneurship practice platform that provides students with the opportunity to turn their ideas into actual projects. For startup services, the university has established close collaborative relationships with local governments and enterprises and has set up nine professional incubation service platforms. For the entrepreneurial climate, the university cultivated a symbiotic innovation and EE ecosystem by promoting the construction of the T-Rim Knowledge-Based Economic Circle. Through the joint efforts of multiple parties, the entrepreneurial activities of teachers, students, and alumni have become vibrant and have formed a complete design industry chain and an enterprise ecosystem that coexists with numerous SMEs.

Development of a framework based on the TH theory

Through the exploration of the interactive relationships among universities, governments, and industries, TH theory points out a development direction for solving the dilemma of EE. Through the lens of TH theory, this study developed a comprehensive framework delineating the macroscopic objectives and practical methods of EE, as depicted in Fig. 4 . In this context, EE has become a common undertaking for multiple participants. Therefore, universities can effectively leverage the featured external and internal resources, facilitating the organic integration of entrepreneurial learning, practice, services, and climate. This, in turn, will lead to better achievement of the unified goals of EE.

Practical contents and objectives based on the triple helix theory.

Numerous scholars have explored the correlation between EE and the TH theory. Zhou and Peng ( 2008 ) articulated the concept of an entrepreneurial university as “the university that strongly influences the regional development of industries as well as economic growth through high-tech entrepreneurship based on strong research, technology transfer, and entrepreneurship capability.” Moreover, Tianhao et al. ( 2020 ) emphasized the significance of fostering collaboration among industry, academia, and research as the optimal approach to enhancing the efficacy of EE. Additionally, Ribeiro et al. ( 2018 ) underscored the pivotal role of MIT’s entrepreneurial ecosystem in facilitating startup launches. They called upon educators, university administrators, and policymakers to allocate increased attention to how university ecosystems can cultivate students’ knowledge, skills, and entrepreneurial mindsets. Rather than viewing EE within the confines of universities in isolation, we advocate for establishing an integrated system that encompasses universities, government bodies, and businesses. Such a system would streamline their respective roles and ultimately bolster regional innovation and entrepreneurship efforts.

Jones et al. ( 2021 ) reported that with the widespread embrace of EE by numerous countries, the boundaries between universities and external ecosystems are becoming increasingly blurred. This convergence not only fosters a stronger entrepreneurial culture within universities but also encourages students to actively establish startups. However, these startups often face challenges related to limited value and long-term sustainability. From the perspective of TH theory, each university can cultivate an ecosystem conducive to specialized entrepreneurial activities based on its unique resources and advantages. To do so, universities should actively collaborate with local governments and industries, leveraging shared resources and support to create a more open, inclusive, and innovation-supporting ecosystem that promotes lasting reform and sustainability.

There are two main ways in which this paper contributes to the literature. First, this study applies TH theory to both theoretical and empirical research on EE in China, presenting a novel framework for the operation of EE. Previous research has applied TH theory in contexts such as India, Finland, and Russia, showcasing the unique contributions of TH in driving social innovation. This paper introduces the TH model to the Chinese context, illustrating collaborative efforts and support for EE from universities, industries, and governments through the construction of EE objectives and content models. Therefore, this paper not only extends the applicability of the TH theory globally but also provides valuable insights for EE in the Chinese context.

Second, the proposed conceptual framework clarifies the core goals and practical content of EE. By emphasizing the comprehensive cultivation of knowledge, skills, attitudes, and resources, this framework provides a concrete reference for designing EE courses, activities, and support services. Moreover, the framework underscores the importance of collaborative efforts among stakeholders, facilitating resource integration to enhance the quality and impact of EE. Overall, the conceptual framework presented in this paper serves not only as a guiding tool but also as a crucial bridge for fostering the collaborative development of the EE ecosystem.

While EE has widespread global recognition, many regions still face similar developmental challenges, such as a lack of organized objectives and content delivery methods. This article, grounded in the context of EE in Chinese higher education institutions, seeks to address the current challenges guided by TH theory. By aligning EE with socioeconomic demands and leveraging TH theory, this study offers insights into the overall goals and practical content of EE.

This study presents a 4H objective model of EE comprising two levels. The first level focuses on outcomes related to entrepreneurial behavior, including entrepreneurial intentions and performance, which highlight the practical effects of EE. The second level is built as the foundation of the outcomes and encompasses the four elements of mindset, skill, attitude, and support. This multilayered structure provides a more systematic and multidimensional consideration for the cultivation of entrepreneurial talent. The framework offers robust support for practical instructional design and goal setting. Additionally, the research extends to the corresponding content model, incorporating four elements: entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate. This content model serves as a practical instructional means to achieve EE goals, enhancing the feasibility of implementing these objectives in practice.

Moreover, this study focused on a representative Chinese university, T-University, to showcase the successful implementation of the 4H and content models. Through this case, we may observe how the university, through comprehensive development in entrepreneurial learning, practice, services, and climate, nurtured many entrepreneurs and facilitated the formation of the innovation and entrepreneurship industry cluster. This approach not only contributes to the university’s reputation and regional economic growth but also offers valuable insights for other regions seeking to advance EE.

This study has several limitations that need to be acknowledged. First, the framework proposed is still preliminary. While its application has been validated through a case study, further exploration is required to determine the detailed classification and elaboration of its constituent elements to deepen the understanding of the EE system. Second, the context of this study is specific to China, and the findings may not be directly generalizable to other regions. Future research should investigate the adaptability of the framework in various cultural and educational contexts from a broader international perspective. Finally, the use of a single-case approach limits the generalizability of the research conclusions. Subsequent studies can enhance comprehensiveness by employing a comparative or multiple-case approach to assess the framework’s reliability and robustness.

In conclusion, this study emphasizes the need to strengthen the application of TH theory in EE and advocates for the enhancement of framework robustness through multiple and comparative case studies. An increase in the quantity of evidence will not only generate greater public interest but also deepen the dynamic interactions among universities, industries, and the nation. This, in turn, may expedite the development of EE in China and foster the optimization of the national economy and the overall employment environment.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, in particular for the individual informants who come from a small, specific population, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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Acknowledgements

We express our sincere gratitude to all individuals who contributed to the data collection process. Furthermore, we extend our appreciation to Linlin Yang and Jinxiao Chen from Tongji University for their invaluable suggestions on the initial draft. Special thanks are also due to Prof. Yuzhuo Cai from Tampere University for his insightful contributions to this paper. Funding for this study was provided by the Chinese National Social Science Funds [BIA190205] and the Shanghai Educational Science Research General Project [C2023033].

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Shao, L., Miao, Y., Ren, S. et al. Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study. Humanit Soc Sci Commun 11 , 519 (2024). https://doi.org/10.1057/s41599-024-03024-2

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Challenges to Student Interdisciplinary Learning Effectiveness: An Empirical Case Study

1 The Graduate Institute of Design Science, Tatung University, Taipei 104, Taiwan

2 The Department of Industrial Design, Tatung University, Taipei 104, Taiwan

Wen-Qian Lu

Kai-yi wang, associated data.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to The relevant data involves the internal information of the institution and personal information, and there is a need for confidentiality.

In order to meet industrial demands, some colleges and universities have offered interdisciplinary programs that integrate design, engineering, and business. However, how many changes these programs have brought to students, and whether students participating in these programs have had better interdisciplinary ability than students involved in a single discipline study have always been questions that many researchers want to explore. In a university that offers an interdisciplinary program, we found that there is no significant difference in interdisciplinary integration ability between the students participating in the interdisciplinary program and the students involved in a single discipline study through quantitative comparisons of 91 student questionnaires and analyses of interviews with nine teachers of interdisciplinary courses and other related staff members. This may result from the students’ lack of motivation, lack of prior experience, the influence of individual traits, the increase of learning pressure and academic burden, and the interference of disciplinary factors during interdisciplinary learning. The research finding is intended to improve student interdisciplinary learning effectiveness by facilitating interdisciplinary teachers’ understanding of the influencing factors of student interdisciplinary learning, and by providing a reference for interdisciplinary teaching design.

1. Introduction

In the past 20 years, discipline-based university education has undergone a great transformation towards interdisciplinary education. Interdisciplinary education and learning have become the focus of education and teaching research today ( Klaassen 2018 ). There has been growing interest in interdisciplinary education and the publications of interdisciplinary education research have increased significantly ( Heikkinen and Räisänen 2018 ). However, how effective is interdisciplinary education? To what extent have students improved their interdisciplinary ability? Although some studies in the fields of medical and nursing education have responded to these issues in recent years ( Bullard et al. 2019 ; Liu 2021 ), there is obviously a lack of interest in research in the fields of engineering and computer science education ( Heikkinen and Räisänen 2018 ). Overall, due to the challenges of effectiveness evaluation on interdisciplinary education, existing research has paid little attention to the growth and evaluation of students’ interdisciplinary ability ( Lattuca et al. 2017a ; Gao et al. 2020 ). It is even more difficult to find relevant literature on interdisciplinary teaching or empirical research ( Lindvig and Ulriksen 2019 ; Van den Beemt et al. 2020 ). Therefore, we could hardly find studies that demonstrate the effectiveness of interdisciplinary education through teaching and learning practices in class ( Gao et al. 2020 ). Are students involved in interdisciplinary education significantly different from students involved in a single discipline study in interdisciplinary ability? What specific teaching and learning factors affect the improvement of students’ interdisciplinary ability during interdisciplinary learning? Little has been learned so far. Biggs proposed a system model for teaching activities, which consists of four parts: student attributes, learning environment, learning process, and learning outcomes. Each part of the model follows the principle of alignment ( Biggs 1993 ). Spelt et al. believed that this theoretical model could more comprehensively explain the interrelationships among various elements in interdisciplinary teaching and pointed out that “interdisciplinary integration ability” as an interdisciplinary learning outcome would be affected by “student attributes” and “interdisciplinary learning environment” ( Spelt et al. 2009 ). This theory was applied and confirmed in subsequent studies by Spelt and Liu et al. ( Spelt et al. 2015 ; Liu et al. 2022 ). In this paper, we will combine this theory and previous research to discuss the definition and connotation of interdisciplinary integration ability, as well as the factors affecting students’ learning outcomes in the area of interdisciplinary learning environment and student attributes.

1.1. Interdisciplinarity Integration Ability

Spelt et al. believed that interdisciplinary integration ability is also interdisciplinary thinking, including interdisciplinary knowledge and interdisciplinary skills ( Spelt et al. 2009 ). The research findings of Menken et al. show that the key to interdisciplinarity being different from multidisciplinarity is the integration of related concepts, insights, theories and/or methods from different disciplines ( Menken et al. 2016 ) (see Figure 1 ). Lattuca et al. argued that interdisciplinary ability enables students to integrate knowledge and methods from different domains for a comprehensive understanding of a problem ( Lattuca et al. 2017a ). Spelt et al. pointed out that the decisive feature of interdisciplinarity is the ability to integrate disciplinary knowledge. If there is no cultivation and training of this ability during teaching, but simply increased knowledge of different disciplines, it can still only be called multidisciplinary education ( Spelt et al. 2015 ). Based on previous research and assertions, it is not difficult for us to come to the conclusion that interdisciplinary integration should be the core and key of interdisciplinarity. Therefore, the improvement of interdisciplinary integration ability should be the key to the evaluation of interdisciplinary teaching effectiveness and the concrete representation of students’ interdisciplinary learning outcomes. However, although “interdisciplinary integration ability” is of critical importance to interdisciplinary teaching, there is not yet a unified definition in the scientific and pedagogical literature ( Danilova 2018 ), and the expressions of its connotation vary. For example, the IPEC (Interprofessional Education Collaborative) in the US defines core interdisciplinary ability as values/ethics for interprofessional practice, roles/responsibilities, interprofessional communication, and teams and teamwork. In addition, core interdisciplinary ability defined by the University of Virginia (2016) includes: communication, professionalism, shared problem-solving, shared decision making, and conflict resolution ( Chen et al. 2017 ). Wilhelmsson et al. pointed out that interdisciplinary ability should include: teamwork and group processes, reflection and documentation, communication, shared knowledge or general common knowledge base, and ethics ( Wilhelmsson et al. 2012 ). Interdisciplinary ability advocated by Lattuca et al. ( 2013 ) includes: awareness of disciplinarity, appreciation of disciplinary perspectives, appreciation of non-disciplinary perspectives, recognition of disciplinary limitations, interdisciplinary evaluation, ability to find common ground, reflexivity, and integrative skill; and subsequently these eight abilities are extracted into three: interdisciplinary skill, reflective behavior, and recognizing disciplinary perspectives ( Lattuca et al. 2013 ). The definitions of the above-mentioned core interdisciplinary integration ability are slightly different, but they all highlight similar abilities (see Table 1 ), including: interdisciplinary communication, facilitating the formation of shared knowledge base and problem-solving teamwork, interdisciplinary reflection and evaluation, accepting other disciplinary values or perspectives, having disciplinary awareness and perspectives, being able to recognize disciplinary limitations, integrating knowledge from different disciplines to deal with complex problems, professionalism, and other skills.

Difference between Multidisciplinarity and Interdisciplinarity. Collated, modified and drawn from Menken Steph, Keestra Machiel, Rutting Lucas, Post Ger, De Roo Mieke, Blad Sylvia, De Greef Linda. 2016. An introduction to interdisciplinary research: Theory and practice. Amsterdam: Amsterdam University Press, pp. 31–32.

The Above-Mentioned Core Interdisciplinary Integration Abilities.

1.2. Student Attributes

Student attributes include motivation, individual traits, prior experience, etc. ( Spelt et al. 2009 , 2015 ; Liu et al. 2022 ). Some researchers discussed the motivations and goals of interdisciplinary learners. For example, Barnard pointed out that most students generally hold conflicting views on interdisciplinary learning ( Barnard et al. 2013 ). In their research, Kabo et al. mentioned that some reports indicated that people with engineering educational background put up resistance to interdisciplinary learning goals ( Kabo and Baillie 2009 ). Berasategi et al. believed that student individual conditions, including learning motivation and maturity, are closely related to their development of interdisciplinary thinking ( Berasategi et al. 2020 ). Some scholars discussed that learners’ prior experience seems to have an impact on interdisciplinary learning outcomes. Heiman pointed out that freshman students are reluctant to use learning methods different from what they have adopted in high school ( Heiman 2014 ). Studies found that students’ prior learning experience of a single discipline makes them feel overwhelmed and at a loss when faced with the teaching design and expectations of interdisciplinary courses ( Strain and Potter 2012 ).

1.3. Interdisciplinary Learning Environment

Interdisciplinary learning environment includes elements like courses, teachers, pedagogy, assessment, etc. ( Spelt et al. 2009 , 2015 ; Liu et al. 2022 ). Van den Beemt et al. suggested that any teachers and students involved in interdisciplinary education projects should be aware of the relation among the specific perspectives and visions of interdisciplinary education, and the chosen teaching methods ( Van den Beemt et al. 2020 ). Do believed that interdisciplinary courses should have a goal that can be achieved within a semester, while corresponding tasks should be designed and learning objectives should be set related to the level of difficulty ( Do 2013 ). Chen et al. pointed out that a course study load is critical to the effectiveness of interdisciplinary learning ( Chen et al. 2009 ). Hansen et al. believed the motivations and goals of the teaching program as the basis for an interdisciplinary approach to pedagogy in the context of interdisciplinary curriculum development ( Hansen and Dohn 2017 ). For the setting of teaching content, Biggs emphasized that if students want to obtain the desired learning outcomes, the basic task of teachers is to engage students in learning activities that may lead them to achieve these outcomes; during the process, deciding what students learn is far more important than what teachers do ( Biggs 1993 ). In addition, many scholars discussed teaching activities, curriculum design, teachers, teaching methods, assessment and other topics in terms of interdisciplinary learning environment ( Carreras Marín et al. 2013 ; Gómez Puente et al. 2013 ; Gouvea et al. 2013 ; Jones 2010 ; Lindvig and Ulriksen 2019 ).

The literature review provides a research framework for us to explore the effectiveness of interdisciplinary curriculum teaching practice that aims at the cultivation of interdisciplinary integration ability, and the influencing factors. Meanwhile, in view of the relative lack of empirical reports on interdisciplinary teaching, this paper will try to find out whether the students participating in an interdisciplinary program have a significant advantage over the students involved in a single discipline study by comparing their interdisciplinary integration ability through an empirical case study. Moreover, this paper will further analyze which elements in the areas of “student attributes” and “interdisciplinary learning environment” affect the interdisciplinary integration ability of the students participating in the interdisciplinary program based on the collected sample data and materials.

2. Materials and Methods

2.1. setting and teaching of interdisciplinary courses.

In the face of rapid technological change, global climate change, and an ever-changing market, product innovation and sustainable development of manufacturing are no longer complex problems that can be completely solved by a single discipline. Some scholars pointed out that the life cycle of a product is divided into three stages: design, engineering, and sales, but these three stages are not independent, and on the contrary, they should be integrated ( Buxton 2010 ). The researchers of design education indicated that as industrial projects are becoming increasingly complex and larger in scale, the boundaries between artifacts, structures, and processes are beginning to be more blurred. Since the requirements at each level are rising, the complexity of design problems will be significantly increased. Therefore, designers will be required to be familiar with working in interdisciplinary teams that integrate engineering and business ( McDermott et al. 2014 ). In addition, many successful large international companies, such as GE, Sony, Philip, etc., have adopted the design perspective as a problem-solving tool for the entire company and a key element in the formation of corporate strategies. Design is increasingly recognized as a key to the success of business practices, and design thinking has become increasingly popular in the field of business ( Matthews and Wrigley 2017 ). Gill et al. believed that interdisciplinarity integrating mechanics, electronics, information technology and design is the future of Industry 4.0, and the integration of these majors will create solutions for complex problems faced by intelligent manufacturing, and product innovation and development ( Gill et al. 2021 ). Driven by industrial development, in fact, some educational institutions have begun to try to carry out interdisciplinary teaching activities that integrate design, business, and engineering technology, such as Jiangnan University in mainland China, Arizona State University in the United States, etc. ( McDermott et al. 2014 ; Li et al. 2019 ).

2.1.1. Setting of Interdisciplinary Courses

The interdisciplinary courses mentioned in this paper are developed and designed by a comprehensive university in Taiwan according to the above-mentioned industrial talent development trend. The university’s mission is to cultivate applied and compound talent for industrial development. A great number of leaders of large international enterprises have graduated from the school successively. Every year, the school regularly invites people from the industry, including prominent alumni, to discuss with the school’s teachers and educational administrators industrial talent needs as well as current education trends and issues. The school’s interdisciplinary program was established in this context. The program is aimed at students majoring in Industrial Design, Media Design, Materials Engineering, Mechanical Engineering, Electrical Engineering, Computer Science and Engineering, Business Management, Applied Foreign Languages, etc. It integrates courses and teaching resources in the fields of business, engineering and design, with the goal of cultivating interdisciplinary integration ability, to form an interdisciplinary curriculum system consisting of interdisciplinary basic courses (i.e., introductory courses for business, engineering, and design majors) + interdisciplinary integration courses like Capstone + internship and practical courses. The interdisciplinary program is intended to improve students’ skills in interdisciplinary communication, interdisciplinary teamwork, interdisciplinary reflection and evaluation, interdisciplinary values or viewpoints, disciplinary limitation cognition, and interdisciplinary knowledge integration.

This program offers 23 courses, including Business Analysis, Applied Electronic Creation, Materials Processing and Analysis, Design Fundamentals, Capstone, etc. These courses are arranged in various stages from the first year to the fourth year in this university, and there is a progressive relationship between the courses before and after (see Table 2 for details). This is a semi-closed academic program, exit only and no entry. Therefore, students must join this program in the first semester of their freshman year and those who try to join the program midway are rejected. In addition to completing the courses of their own majors, they need to complete the various courses of the interdisciplinary program. Meanwhile, students can be exempted from taking the interdisciplinary basic courses in this program within their own disciplines. In addition, after the start of the program, the courses that have been registered and selected during the semester cannot be withdrawn, but in accordance with the principle of voluntariness, the participants are allowed to stop the study of subsequent unselected courses and withdraw from the program.

The Interdisciplinary Program.

2.1.2. Teaching of Interdisciplinary Courses

More than 30 teachers from the College of Engineering, College of Management, and College of Design are involved in this interdisciplinary program. The interdisciplinary basic courses are taught by the teachers from these three colleges respectively. Since most of these courses belong to introductory or entry level courses, the teachers of each college mainly adopt the teaching method that combines more traditional theoretical lectures and practice training to cultivate students’ cognition of other disciplines’ values, viewpoints and methods. The students participating in these courses are not students of the disciplines to which the courses belong. The teaching of Capstone interdisciplinary integration courses is led and aided by an interdisciplinary teaching team composed of teachers from the three colleges in PBL teaching mode. All students participating in these interdisciplinary capstones are required to form interdisciplinary teams when taking these courses to complete corresponding subject training by solving real and complex problems.

2.2. Measurements and Interviews

Self-assessment comparisons of interdisciplinary integration ability indicators were made in the above-mentioned university between the students in higher grades participating in an interdisciplinary program and the students in higher grades involved in a single discipline study. Considering that there may be some subjective bias of student self-assessments, the research group planned to conduct sample interviews with the tested students to more objectively evaluate the difference in interdisciplinary integration ability of this group of students by collecting their coursework and referring to expert evaluations. However, due to some obstacles, such as the students’ unwillingness and earlier departure of graduates caused by temporary school closures during the COVID-19 pandemic, the research group failed to collect relevant students’ interview materials and interdisciplinary coursework. Therefore, members of the research group interviewed the teachers, tutors, and program administrators involved in interdisciplinary teaching in the university, to ask about their understanding of these students. It is intended to more objectively understand the performance of this group of students making self-assessments from the perspectives of the teachers. All the students participated in the self-assessments of interdisciplinary integration ability indicators voluntarily, and all the staff members invited to the interviews approved the acquisition and study of the interview recordings. The details are as follows:

2.2.1. Interdisciplinary Integration Ability Measurements

Chen, Wang et al. synthesized previous research findings, summarized the connotations of interdisciplinary integration ability discussed in the first part of this paper and defined it as “common interdisciplinary integration-based core competencies”, i.e., shared interdisciplinary integration ability that students of different disciplines should have. Based on this definition and combining the characteristics of the cultural background and native language habits of local students growing up and the general conditions of curriculum teaching, they developed an interdisciplinary integration ability scale (see Appendix A for details), which specifically includes three sub-ability measurements, i.e., “interdisciplinary communication, interdisciplinary reflection, and interdisciplinary practice”, and a total of 16 questions ( Chen et al. 2017 ). Among them, “interdisciplinary communication” is reflected in the three main indicators of “respecting professional opinions, understanding different professional terms, and communicating through communication tools”, and includes six questions; “interdisciplinary reflection” is reflected in the three main indicators of “understanding the role differences among people with different expertise, making a reflection and generating new ideas through the process of interaction with others, and reflecting on the problems encountered in the process”, and includes five questions; “interdisciplinary practice” is reflected in the three main indicators of “discovering teamwork problems and proposing practical solutions, evaluating the work efficiency of team members, and evaluating the effectiveness and making suggestions for improvement”, and includes five questions. The 16 questions are answered based on a 5-point Likert scale, where 1 is strongly disagree, 2 is somewhat disagree, 3 is neutral/no opinion, 4 is somewhat agree, and 5 is strongly agree. This scale has been used in universities in Taiwan and has shown good reliability. In this case, considering the school’s understanding of the cultivation of students’ interdisciplinary integration ability, after discussing with some research experts, the research group adopted this scale to make measurement comparisons of “interdisciplinary communication, interdisciplinary reflection, and interdisciplinary practice” between the students participating in the interdisciplinary program and the students involved in a single discipline study in the university. In addition, in the pretests before using the scale, a total of 60 questionnaires were distributed, and 60 valid questionnaires were collected. The reliability value of Cronbach’s α is .88, which indicates high reliability of the questionnaire design and thus it can be used for testing.

Samples of Students Participating in the Interdisciplinary Program

The students participating in the interdisciplinary program are undergraduate students in higher grades majoring in Industrial Design, Media Design, Materials Engineering, Mechanical Engineering, Electrical Engineering, Computer Science and Engineering, and Business Management from the above-mentioned university. When they entered the university in the first year, they attended the introduction meeting for the interdisciplinary program, and signed up to participate in the program. The research group conducted a random sampling of the students in higher grades who have completed the integrated Capstone courses, and collected 19 valid interdisciplinary student samples (excluding the student samples for the pretests), including three samples of Industrial Design majors, four samples of Media Design majors, five samples of Materials Engineering majors, three samples of Mechanical Engineering majors, one sample of Electrical Engineering major, and three samples of Computer Science and Engineering majors.

Samples of Students not Participating in the Interdisciplinary Program

The research group randomly distributed questionnaires to the students in higher grades in the university who have not participated in the interdisciplinary program. A total of 72 valid questionnaires were collected (excluding the questionnaires for the pretests), and among them, 23 are from Industrial Design majors, 29 are from Materials Engineering majors, and 20 are from Electrical Engineering majors.

For details of the independent variables, dependent variables and the number of students tested in the measurement comparisons, please refer to Table 3 .

Measurement Variables and Number of Students Tested.

In addition, it should be noted in this paper that, due to factors such as students’ unwillingness, the research group were not able to collect any questionnaires from the students majoring in Business Management whether they participated in the interdisciplinary program or not.

2.2.2. Interview

Interview Design

The purpose of these interviews is to verify whether the teachers’ observations of the students participating in the interdisciplinary program are consistent with the students’ self-assessments of their own interdisciplinary integration ability. In this way, the research group can more objectively understand the differences in learning outcomes and learning status between the interdisciplinary students and non-interdisciplinary students of various majors and discuss what factors may affect the improvement of student interdisciplinary integration ability based on these differences. In order to more comprehensively grasp the learning conditions of the students participating in the interdisciplinary program, the research group decided to conduct focus group interviews. When considering the focuses of these interviews, in order to avoid the situation that the respondents may be induced by the vocabulary with a specific connotation, the research group did not place related words mentioned above such as students’ “learning methods, expectations, individual traits, prior experience, learning motivation, and maturity” in the questions when designing the discussion guide. An open-ended interview structure has been introduced to avoid any leading questions or guided answers. The clues are as follows:

Q1. What is the classroom atmosphere during class?

Q2. What is the performance of students in course learning and task completion? Are there any significant differences among them in this regard? Are the students of various majors the same in this regard?

Q3. When the courses are over, can the students achieve the ability target set when the courses were opened? If not, what is the reason? Are the students of various majors the same in this regard?

Interview Process

A total of 9 staff members (Pt-1 to Pt-9) from Industrial Design, Economics, Materials Engineering, Mechanical Engineering, and other disciplines were invited to the interviews. They are teachers of the interdisciplinary program, tutors of the interdisciplinary students, and program and teaching administrators. The respondents were asked to answer questions and have discussions according to the above clues based on their knowledge of the students in higher grades participating in the interdisciplinary program. These interviews were open-ended, and all the respondents fully expressed their opinions without any pressure or inducement. A total of 9 respondents have been interviewed, with the full process of interviews completed in 4 separated durations. The interviews were recorded only upon the agreement by the respondents. A total of 5 h, 49 min, and 13 s of audio recordings were produced.

Interview Analytical Methods

All the interview recordings were transcribed verbatim. Questions, explanations of questions, and follow-up questions in the verbatim transcript were removed, followed by a coding analysis of the verbatim transcript. The coding analysis was made within the theoretical framework of interdisciplinary integration ability, student attributes, and interdisciplinary learning environment discussed in the first part of this paper. After open coding, the textual material produced 185 units of thematic encoding distributed over 86 nodes. The same unit of thematic coding may belong to different nodes, but the same node can only be categorized into one subcategory and cannot be categorized into another subcategory. Therefore, through further summarization, mergence, and sorting, 18 subcategories were formed, and 8 categories were finally extracted. Due to the failure of sample collection of student coursework that can directly reflect students’ interdisciplinary ability, we could only rely on teachers’ judgments and subjective evaluations to understand students’ interdisciplinary learning and to infer the improvement of their interdisciplinary integration ability. Therefore, the research group used the three dimensions of “learning condition feedback, student attributes, and learning environment” to carry out axial coding of the 8 categories, instead of “interdisciplinary integration ability, student attributes, and learning environment”.

To ensure the high reliability of the initial open coding, the research assistants randomly selected 20% of the interview data for coding and compared it with the previous coding of the same part, finding no significant difference.

3.1. Self-Assessment Results of Student Core Interdisciplinary Integration Ability

This study compares the core interdisciplinary integration ability among the students in the Departments of Industrial Design, Materials, and Electrical Engineering who have not participated in the university’s interdisciplinary program and the students who have participated in the program. Questionnaires were distributed to the students in the Departments of Industrial Design, Electrical Engineering, and Materials, and interdisciplinary students, and a total of 91 valid questionnaires were collected. Among them, 23 are from Industrial Design students, 29 are from Materials students, 20 are from Electrical Engineering students, and 19 are from interdisciplinary students (including three from Industrial Design majors, four from Media Design majors, five from Materials Engineering majors, three from Mechanical Engineering majors, one from Electrical Engineering major, and three from Computer Science and Engineering majors). After sorting out the questionnaire data of the four different groups of students, the quantitative statistical method of one-way ANOVA was used for analysis. Please refer to Table 4 for the results.

Results of the One-Way ANOVA.

The quantitative analysis results of the students in the three sub-ability measurements of communication, reflection, and practice (see Table 4 ) show that a total of five questions indicate significant differences: Interdisciplinary Communication 2, Interdisciplinary Communication 5, Interdisciplinary Reflection 1, Interdisciplinary Reflection 3, and Interdisciplinary Practice 1. This means that each of the five questions can show significant differences in core interdisciplinary integration ability among the students of at least two or more disciplines. Post hoc multiple comparisons were required to explore specific situations. In addition, the results of the test of homogeneity of variances (see Table 5 ) show that Interdisciplinary Communication 3, Reflection 4, Practice 4 and Practice 5 all have the characteristics of heterogeneity of variance, which indicates the distribution of the samples of the above four questions, i.e., the degree of dispersion is very significantly heterogeneous. Therefore, in the case of multiple comparisons, these four questions needed to be tested by the Games–Howell method instead of the Scheffe method ( Mamiseishvili et al. 2016 ). The results of the multiple comparisons indicate that although there are significant differences in the previous One-way ANOVA, Communication 2, Reflection 3, and Practice 1 do not show any significant differences in multiple comparisons, and finally only Interdisciplinary Communication 5, Interdisciplinary Reflection 1, and Interdisciplinary Practice 4 show significant differences (see Table 6 for details). The specific findings are as follows: the scores of Industrial Design students and Materials Engineering students in Communication 5 are significantly higher than those of Electrical Engineering students, and their means show the relation of “Industrial Design students > Materials students > interdisciplinary students > Electrical Engineering students”, but the first three student groups do not show any significant difference, and there is no significant difference between interdisciplinary students and Electrical Engineering students. In Reflection 1, the means of the four student groups in core interdisciplinary integration ability show the relation of “Industrial Design students > interdisciplinary students > Materials students > Electrical Engineering students”, in which only the scores of Industrial Design students are significantly higher than those of Electrical Engineering students. There is no other significant difference in Reflection 1, and in other words, interdisciplinary students and Materials Engineering students are not significantly different from Industrial Design students or Electrical Engineering students, and meanwhile, there is no significant difference between interdisciplinary students and Materials Engineering students either. Practice 4 reflects the same situation as Reflection 1: the means of the four student groups in core interdisciplinary integration ability show the relation of “Industrial Design students > interdisciplinary students > Materials students > Electrical Engineering students”, and only the scores of Industrial Design students are significantly higher than those of Electrical Engineering students, and there is no significant difference among other student groups.

Results of the Test of Homogeneity of Variance.

Results of the Post Hoc Tests: Multiple Comparisons.

* represents Sig. < .05, InterD = Interdisciplinary, EE = Electrical Engineering, ID = Industrial Design, ME = Mechanical Engineering.

Students in the four groups show significant differences in only three out of the 16 questions of core interdisciplinary integration ability, and in these three questions, interdisciplinary students are not significantly different from any other student groups, which prevents us from drawing the conclusion that interdisciplinary students are significantly better than other students in core interdisciplinary integration ability. This result is surprising. Why the students participating in the interdisciplinary program do not have outstanding related ability deserves further discussions by the research group.

3.2. Qualitative Analysis Results of Teacher Interviews

Table 7 shows how many times the codes covering three dimensions (D-01 to D-03) were mentioned and how many teachers mentioned them. Among them, “Learning Condition Feedback” (D-01) was mentioned 36 times by six teachers in total, and it includes two categories (D-01-01 to D-01-02) and five subcategories (D-01-01a to D-01-02c); “Interdisciplinary Learning Environment” was mentioned 41 times by seven teachers in total, and it includes three categories (D-02-01 to D-02-03) and six subcategories (D-02-01a to D-02-03b); “Student Attributes” was mentioned 108 times by eight teachers in total, and it includes three categories (D-03-01 to D-03-03) and seven subcategories (D-03-01a to D-03-03b).

Codes and Frequencies.

In order to find shared feelings of the respondents, we only analyzed subcategories mentioned by two or more respondents. There are 15 subcategories (D-01-01a, D-01-02a to D-01-02c, D-02-01a to D-02-02b, D-03-01a to D-03-03b) and seven categories (D-01-01, D-01-02, D-02-01, D-02-02, D-03-01 to D-03-03) involved. The frequencies of the three main axis dimensions and seven categories are shown in Table 8 . The specifics of each main axis dimension will be explained in order.

Frequency Proportion and Ranking of Each Dimension and Category Mentioned.

3.2.1. D-01 Learning Condition Feedback

This dimension includes the description and evaluation made by the teachers being interviewed on the behavioral performance of the interdisciplinary integration ability of the students participating in the interdisciplinary program. In the dimension, the teachers gave specific feedback on students’ interdisciplinary values, interdisciplinary knowledge integration, interdisciplinary teamwork, interdisciplinary communication, and interdisciplinary team consensus building, as well as students’ words, deeds, and emotions under the program. This dimension, including the two categories of positive feedback and negative feedback identified by their positive and negative connotations, was mentioned 36 times in total by six respondents successively.

In the category of “D-01-01 Positive Feedback”, only the subcategory of “D-01-01a” was mentioned by two respondents. It mainly records the two teachers’ recognition of the growth of some students in interdisciplinary learning, including overcoming the difficulties in interdisciplinary communication, reaching an interdisciplinary team consensus, and being willing to carry out interdisciplinary teamwork practice. They clearly stated that they have seen the students’ growth in interdisciplinary ability (see Appendix B ). We therefore named D-01-01a “Growth in Interdisciplinary Integration Ability”. Unfortunately, positive feedback is the least among the seven categories in terms of the number of respondents who mention it and the frequency of mentions.

The category of “D-01-02 Negative Feedback” includes three subcategories: “D-01-02a, D-01-02b, and D-01-02c”. Six out of the nine respondents talked about the problems and negative words, deeds and emotions of students in higher grades in interdisciplinary learning from different perspectives (see Appendix B ). D-01-02a mainly reflects that the students still do not understand or agree with interdisciplinary values after participating in the interdisciplinary program; some students have interdisciplinary communication barriers and need to rely on their teachers to interpret and explain interdisciplinary knowledge. These all reflect that the students are still far from the acquisition of interdisciplinary integration ability. Thus, we named D-01-02a “Problems with Interdisciplinary Ability”. D-01-02b was mentioned by two teachers, and it mainly describes students’ doubts or distrusts during interdisciplinary learning. They not only distrust interdisciplinary learning, but also have no confidence in teachers of other disciplines and in this program. We can understand that the students cannot accept the views and values of other disciplines, but we are very surprised to find that teachers of other disciplines and even the program itself cannot be trusted. As a result, we also listed this subcategory and named it “Student Distrust” instead of classifying it into the subcategory of “Problems with Interdisciplinary Ability”. A total of three teachers mentioned the subcategory of D-01-02c, and they mainly talked about students’ frustration, emotional ventilation, and withdrawal from the interdisciplinary program due to the intensity of the courses, the gap between expectations and perceived reality, and problems with teamwork. These are indeed negative phenomena that students experience during interdisciplinary learning, so we named this subcategory “Negative Emotions and Behaviors”.

Based on the above analyses, negative feedback was mentioned 28 times by six respondents, which is significantly more than the positive feedback. Within the dimension of “Learning Condition Feedback”, the frequency proportion of negative feedback accounts for 77.8%, which is more than three times that of positive feedback. From the analysis of this feedback, we believe that the overall improvement of students’ interdisciplinary integration ability under this program is not satisfactory.

3.2.2. D-02 Interdisciplinary Learning Environment

The dimension obtained after the axial coding was mentioned 41 times by seven respondents. Two categories of “D-02-01” and “D-02-02” were mentioned by more than two respondents. In this dimension, the respondents described students’ feedback on the difficulty of interdisciplinary courses, the learning pressure imposed by the interdisciplinary teachers, the increased learning burden of interdisciplinary courses, and the influence of non-interdisciplinary teachers on the students participating in the interdisciplinary courses. Based on the previous discussion on the literature of “interdisciplinary learning environment”, we coded this dimension as “Interdisciplinary Learning Environment”.

The subcategories of D-02-01 include: D-02-01a and D-02-01b. D-02-01a was mentioned eight times by four respondents based on all the written materials of this study. The interviewed teachers described that the students reported that the basic design courses under this interdisciplinary program feature high-intensity learning, the engineering courses are too difficult to understand, so that they felt huge learning pressure, resulting in the negative emotions or behaviors mentioned above (see Appendix C for details). Hence, we named this sub-category “Study Pressure”. D-02-01b was mentioned 19 times by six respondents. The interdisciplinary teachers found that after a period of interdisciplinary learning, some students think that they have consumed too much time or energy in interdisciplinary learning instead of in their own discipline; and they worried that the final scores of the interdisciplinary courses will lower their average score, etc., all of which make students perceive interdisciplinary learning as a burden to their own discipline study (see Appendix C ). Based on this, we named the subcategory “Academic Burden”, and the category of D-02-01 including the two subcategories of “D-02-01a Study Pressure and D-02-01b Academic Burden” “Pressure and Burden”.

The subcategories of D-02-02 include: D-02-02a and D-02-02b. D-02-02a was mentioned 10 times by three teachers. The respondents mainly mentioned the influence of teachers of mono-disciplines on students’ disciplinary thinking, which mostly features contempt or rejection of interdisciplinary values (see Appendix C ). This seems to challenge students’ interdisciplinary values, and indeed affects students’ cognition, judgment and persistence in interdisciplinary learning to a considerable extent. Therefore, this subcategory was named “Influence of Departments”. D-02-02b was mentioned five times by five respondents. In this subcategory, the respondents mainly mentioned that when the students are switching between interdisciplinary courses and disciplinary courses under this program, the teachers of different disciplines have different evaluation criteria for the output of the same student, which can bring frustration and value conflicts to the students participating in the interdisciplinary program. Some students cannot adapt to, identify with, or accept different judging standards (see Appendix C ). Therefore, D-02-02b was named “Differences among Disciplines”.

Among the three main axis dimensions, interdisciplinary learning environment has the second highest number of respondents who mentioned it and the frequency of mentions. Two categories of D02-01 and D02-02 were both mentioned by six respondents. The former has a frequency of 19, and the frequency proportion within this dimension accounts for 46.3%; the latter has a frequency of 15, and the frequency proportion within this dimension accounts for 36.6%. In this dimension, the frequency difference between the two categories is about 10%. Based on this, we believe that the impact of interdisciplinary students’ learning pressure and burden may bring the respondents a stronger feeling than that of differences between disciplines. However, in any case, the interviews reveal such a finding, i.e., the intensity, pressure, and burden felt by the students in interdisciplinary learning, the unsupportive teachers of their own disciplines against interdisciplinary learning, and differences among disciplines are closely related to “D01-02 Negative Feedback” mentioned in the previous axis dimension.

3.2.3. D-03 Student Attributes

This dimension records the judgments made by the interviewed teachers on the students’ learning motivation, and the speculation and description of the motivational causes after observing the students’ interdisciplinary learning status. It also includes a description of the impact of students’ experiences before being involved in the interdisciplinary program on their interdisciplinary learning, and the impact of students’ individual traits, such as personal characteristics, learning habits, and learning responsibility on their interdisciplinary learning. According to the discussion of “student attributes” in the first part of this paper, we named this dimension “Student Attributes”. The number of respondents who mentioned the dimension is the most and the frequency of mentions is the highest among the three dimensions. The topic of student attributes was mentioned 108 times in total by eight respondents successively. This dimension includes three categories: “D03-01, D03-02, and D03-03”.

In this dimension, the respondents talked about the phenomena of students lacking motivation, dawdling their time away, and being unwilling to participate in interdisciplinary learning, and believed that students lack interdisciplinary learning motivation. Meanwhile, the teachers made some interpretations and analyses of the reasons for the lack of motivation of the students (see Appendix D ). Therefore, we named D03-01 “Motivation”. The number of respondents who mentioned D03-01 Motivation is the most and the frequency of mentions is the highest among the three categories. It was mentioned 56 times by eight respondents. The category of D03-01 Motivation includes three subcategories: “D03-01a, D03-01b, and D03-01c”. Self-determination theory suggests that pure curiosity or a desire to master can be called intrinsic motivation; all the other behaviors are driven by extrinsic motivation, derived from the integration and internalization of social values or rules ( Cook and Artino 2016 ). Based on this, we classified the phenomena of the students’ lacking motivation, dawdling their time away, and being unwilling to participate in interdisciplinary learning, as well as other related phenomena of a lack of motivation into the subcategory of D03-01a, and named it “Intrinsic Motivation”. D03-01a was mentioned 38 times by eight respondents successively. In addition, two respondents mentioned the incentives or restraints that are intended to stimulate students’ extrinsic motivations and suggested that these extrinsic motivations can be transformed into students’ intrinsic motivations. This was encoded into D03-01b and named “Extrinsic Motivation”. The reasons for the lack of motivation mentioned by the teachers, including students’ identification with the teachers, whether interdisciplinary learning can meet their short-term realistic goals, and interdisciplinary learning’s relevance to their own disciplines were encoded into D03-01c and named “Source of Motivation”. The subcategory of D03-01c was mentioned by six respondents.

In the category of D03-02, the respondents talked about the students’ incompatibility with the teaching methods not belonging to their own discipline, and their unaccustomedness and irritation of PBL teaching when taking Capstone courses due to their lack of prior interdisciplinary learning experience. In addition, the students do not have similar experiences in social cognition and life practice before participating in interdisciplinary learning, especially before taking the interdisciplinary integration courses in the upper grades. In fact, they showed confusion about interdisciplinary cognition both before and after participating in the program (see Appendix D ). The research team named this coding category “Prior Experience”. Meanwhile, the text content about interdisciplinary learning incompatibility due to the lack of prior learning experience was coded as the subcategory of “D-03-02a” and named “Influence of Prior Teaching and Learning Styles”; the relevant text content about the lack of interdisciplinary cognition in previous social cognition and life practice was coded as the subcategory of “D-03-02b”, and named “Prior Interdisciplinary Practice Experience and Cognition”. The number of respondents who mentioned D03-02 Prior Experience is the second most and the frequency of mentions is the second highest among the three categories. It was mentioned 29 times by seven respondents. D-03-02a was mentioned 21 times by six respondents. D-03-02b was mentioned 8 times by three respondents. The students from the Department of Design and the students from the Department of Engineering were compared, and it can be seen that different prior teaching styles of the two disciplines lead to different adaption conditions of the students after participating in the interdisciplinary program. For example, design students already have problem-oriented learning experience when they study in their own disciplines. Meanwhile, due to the nature of design disciplines, design students have more opportunities to be exposed to some interdisciplinary knowledge. As a result, design students are more adaptable in interdisciplinary learning, while students in other disciplines are the opposite. The teachers believed that the students’ lack of interdisciplinary experience and cognition before participating in the interdisciplinary program influences the formation of their interdisciplinary values or awareness, which may be one of the factors that cause the students to be at a loss or even withdraw from the program when facing interdisciplinary learning.

In the category of D03-03, the respondents reflected different characteristics of students in different departments in interdisciplinary learning; according to the content of the relevant text, the research group coded it as “D-03-03a” and named it “Different Characteristics of Students in Different Departments”. Besides, we coded the content about the impact of student personal characteristics like sense of responsibility, learning attitudes on interdisciplinary learning as D-03-03b, and named it “Student Personal Characteristics”. For the category of D03-03 that includes D-03-03a and D-03-03b, we named it “Individual Traits” (see Appendix D ). Although the number of respondents who mentioned D03-03 and the frequency of mentions are the least among the three categories of the dimension of Student Attributes, it was mentioned 23 times by five respondents. D-03-03a was mentioned 10 times by three respondents. D-03-03b was mentioned 13 times by five respondents. From the encoded text, some interdisciplinary learning conditions of Engineering, Design, and Business students can be seen, and it is found that different student characteristics shaped by different discipline education also seem to have an impact on interdisciplinary learning. For example, Engineering students are relatively not good at communication, while Design students are more creative, and Business students are considered to be more inclined to take shortcuts in interdisciplinary learning. From the text of the encoded unit, it can be seen that the teachers felt that the students’ own personal characteristics, learning attitudes, and sense of responsibility can also have an impact on interdisciplinary learning (see Appendix D ).

As mentioned above, the number of respondents who mentioned the dimension of Student Attributes is the most and the frequency of mentions is the highest among the three main axis dimensions. The frequencies of the three categories of “D03-01 Motivation, D03-02 Prior Experience, and D03-03 Individual Traits” are respectively 51.9%, 26.9%, and 21.3%. Among all the categories, the frequency proportion of Motivation accounts for 30.3%, the frequency proportion of Prior Experience accounts for 15.7%, and the frequency proportion of Individual Traits accounts for 12.4%. According to the interview transcript, the space of the dimension of Student Attributes is the greatest. It can be said that student attributes should have a very important relation with interdisciplinary learning outcomes, and they can play a key role in student interdisciplinary integration ability.

4. Discussion

The purpose of this study is to empirically explore whether there is significant difference in interdisciplinary integration ability between the undergraduate students participating in the interdisciplinary program that integrates design, engineering, and business, and the students studying a single discipline, and to discuss the reasons for the differences. To this end, the research group invited 91 students for self-assessment analyses of core interdisciplinary integration ability and nine teachers and related staff members involved in the interdisciplinary program to interviews on the conditions of the group of interdisciplinary students. The experimental data were obtained through quantitative comparative analyses and qualitative coding analyses. The results of quantitative analyses show that the students participating in the interdisciplinary program are not significantly different from those of other disciplines in the ability level of “interdisciplinary communication, interdisciplinary reflection, and interdisciplinary practice”. The results of qualitative analyses show that the teachers’ negative feedback on the interdisciplinary students is significantly more than positive feedback in the number of respondents who mentioned it and frequency of mentions. Meanwhile, through qualitative analyses, it is found that the interdisciplinary students’ disagreement with interdisciplinary values, distrust of interdisciplinary teachers, obstacles in interdisciplinary communication, and problems with teamwork. Therefore, the research team believes that the improvement of the students in ability after participating in interdisciplinary learning is not ideal. This clearly echo their insignificant interdisciplinary integration ability in the interdisciplinary integration ability measurements. Based on the results of the data analyses, we believe that the results of the qualitative analyses can confirm the objectivity of the students’ self-assessment results to a considerable extent. Based on this finding, we are more inclined to assert that there is no necessarily significant difference in interdisciplinary integration ability between the students participating in the interdisciplinary program and the students studying a single discipline. This may not support the research conclusion that student interdisciplinary integration ability is closely related to interdisciplinary course participation, or that students involved in interdisciplinary learning have better interdisciplinary integration ability than students studying a single discipline ( Y.-Y. Li and Lin 2018 ; Newell 1992 ; Wright 1992 ). However, this finding is similar to the findings of the 2017 study by Lattuca et al. Their research shows that students of interdisciplinary learning are not necessarily better than students of monodisciplinary learning in interdisciplinary-related abilities, and students’ acquisition of the ability may not necessarily change significantly due to the interdisciplinary characteristics ( Lattuca et al. 2017b ). Lattuca et al. also pointed out that their findings are consistent with evidence from Jacobs’ analysis of Arum et al.’s data ( Lattuca et al. 2017b ).

What causes this insignificant difference? Soares believed that curriculum designers often seem to underestimate the support that students need in interdisciplinary learning ( Soares et al. 2013 ). Borrggo et al. suggested that the design of course projects should avoid as much as possible the frustration of students due to overly difficult problem tasks ( Borrego et al. 2013 ). In fact, the research of Soares, Borrego et al. confirmed the systematic relation between interdisciplinary teaching and learning summarized by Spelt et al. through literature review, i.e., the impact of student attributes and interdisciplinary learning environment on interdisciplinary integration ability ( Spelt et al. 2009 ). This does echo our findings. The results of the quantitative analyses and the feedback of learning conditions in the qualitative analyses should reflect the level of student interdisciplinary integration ability. In the qualitative analyses of these interviews, student attributes, including motivation, prior experience, and individual traits, and the interdisciplinary learning environment, including pressure and burden, and disciplinary factors, should be the influencing factors of student interdisciplinary integration ability. Meanwhile, from the analyses of the frequency of mentions in the coding analysis research, student attributes’ impact on interdisciplinary learning is significantly more than the interdisciplinary learning environment; and for each category, their influence from more to less is respectively: Motivation, Prior Experience, Individual Traits, Pressure and Burden, and Disciplinary Factors. We will further explore these five categories further below.

In our interviews, the teachers pointed out that the students are unwilling to participate in the interdisciplinary program and dawdle their time away during interdisciplinary learning due to their lack of identification with interdisciplinary learning; students may join the program for some other reasons, so they are not very active; students think that they have spent time on interdisciplinary learning, but it does not help achieve their short-term goals, so they naturally withdraw from the program; students feel that they have spent energy on interdisciplinary learning, but the results are not satisfactory, and they are worried that their average score will be lowered, so they have negative reactions. Clearly, these are manifestations of a lack of motivation in interdisciplinary learning (see Appendix D for details). Motivation is defined as the process of initiating and maintaining goal-directed activities, while the goal-directed theory states that learners tend to engage in tasks related to mastering content or to do better than others or to avoid failure ( Cook and Artino 2016 ). In addition, as an important part of motivational structure, self-efficacy ( Lishinski et al. 2016 ) determines how much effort people are willing to put in, as well as people’s ability to cope and persevere in the face of challenges and difficulties ( Bandura 1977 ). The discussion of motivation by Cook, Lishinski, and Bandura et al. should be sufficient to explain the impact of students’ lack of motivation for interdisciplinary learning. Therefore, whether from the frequency results of the qualitative analyses or from previous research on learning motivation, perhaps the primary task of interdisciplinary education should be the cultivation, shaping, and enhancement of learning motivation.

How can we shape or enhance student motivation for interdisciplinary learning? In the interviews, some teachers mentioned that students may need to know what kind of ability the interdisciplinary program is designed to cultivate, or what they may get after completing the program, which may be important motivation to support them to continue their studies. In this regard, some researchers pointed out that understanding the utility and importance of interdisciplinary learning is very important for student interdisciplinary learning outcomes ( Chen et al. 2009 ; Matthews et al. 2010 ). In addition, Keller pointed out that establishing students’ motivation to learn requires successfully establishing the relevance of teaching to students as an individual ( Keller 1987 ). In fact, the respondents reported that they have conveyed under the interdisciplinary program to the students the idea that interdisciplinary learning is more conducive to acquiring the ability and vision of innovation and entrepreneurship, but this does not seem to be related to students’ more realistic short-term goals of furthering their study, going abroad, finding a job, or improving their average score of their own discipline, so they fail to convince these students to realize the importance of interdisciplinarity. Obviously, this relatively superficial interdisciplinary concept transfer has not successfully established the relevance of teaching to students. This may be one of the reasons for not effectively stimulating students’ motivation for interdisciplinary learning. This is similar to the findings of Self et al.’s 2019 study, i.e., compared with British students, Korean students cannot be identified with the interdisciplinary nature of a particular occupation, and they are particularly concerned about the appropriateness of interdisciplinary education in terms of employment, its negative impact on employment, and are worried about whether interdisciplinary education will be valued by discipline-oriented industries. Self et al. believed that different regional cultures may influence students’ driving force of interdisciplinary learning ( Self et al. 2019 ). Based on this, we infer that the students in East Asia may be more concerned about the relevance of interdisciplinary learning and the realization of short-term goals. In interdisciplinary education, the shaping or enhancement of student learning motivation should focus on this. In addition, judging from the introduction to the course teaching mentioned in the second part of this paper, for the students from different disciplines, the interdisciplinary basic courses under this program still use the original traditional teaching methods of each college. We speculate that this is bound to make it difficult for the students to establish the relevance of their own disciplines and interdisciplinary course teaching. Meanwhile, the original teaching methods of various disciplines retained in the teaching of interdisciplinary basic introductory courses have turned the teaching of interdisciplinary basic courses into multi-disciplinary teaching of disciplines plus disciplines, and fail to promote the integration of knowledge, methods, and viewpoints of various disciplines, which may make it difficult for the students from different disciplines to have effective interdisciplinary communication and interdisciplinary teamwork under this program. As Keller once pointed out, students’ effective learning and expectations of success are hindered, which will also lead to a decrease in learning motivation ( Keller 1987 ). Therefore, although this program has interdisciplinary integration courses in the later stage, it still uses the traditional teaching methods in the interdisciplinary basic introductory courses in the early stage, which should also be the reasons that lead to the lack of students’ motivation and the hinderance of the improvement of students’ interdisciplinary integration ability.

Prior Experience

The courses students have taken can significantly influence their learning experience ( Chen et al. 2009 ). The experience may affect student interdisciplinary learning. Spelt et al. pointed out that past social and educational experience, such as students’ previous thinking styles, the teaching styles they have been exposed to, and beliefs about the nature of knowledge and learning, may impact their interdisciplinary integration ability and thinking ( Spelt et al. 2009 ). In our interviews, some teachers mentioned that students are not used to the teaching methods of the interdisciplinary teachers; if the students do not start to get used to the teaching methods in their freshman year, it will be hard for them to be adapted to them in their junior year; the engineering students cannot adapt to problem-oriented learning in basic design courses, and cannot understand teaching methods that do not have the best solution to problems in integrated courses; the students have no successful experience in innovation and entrepreneurship, so it will be difficult for them to understand and identify with the teachers’ perspectives on interdisciplinary learning(see Appendix D for details). In contrast, design students, as mentioned above, have more opportunities to be exposed to interdisciplinary knowledge, have earlier problem-oriented learning experiences, and are more adaptable to interdisciplinary learning. Meanwhile, judging from the quantitative results of students’ ability, non-interdisciplinary design students have significant performance in Communication 5, Reflection 1, and Practice 4 in the questions can also explain this. The information gathered supports Spelt et al.’s perspective. Based on this, the authors infer that students may experience discomfort or confusion in new learning due to differences in their previous study habits or teachers’ teaching styles. Meanwhile, the lack of specific interdisciplinary experience will lead to students’ failure in interdisciplinary value formation, which is not conducive to the construction of interdisciplinary learning motivation and the improvement of interdisciplinary integration ability level. This is consistent with Ramalingam et al.’s point that student self-efficacy and academic performance are positively related to their prior experience ( Ramalingam et al. 2004 ), i.e., the amount of prior experience affects the amount of student’s interdisciplinary learning motivation and how much the learning effectiveness will be improved. On the other hand, as far as learning is concerned, researchers in the field of cognitive theory believed that how new information is organized and interrelated with previous knowledge has an important impact on learning, and interdisciplinary teachers should help students create a clear link between what they are going to learn and their prior experience, including what they have learned in the past ( Lattuca et al. 2004 ). We obtained similar confirmation from the discussion of motivation in the previous part of this paper. In practice, however, it is not an easy task for interdisciplinary teachers to correlate students’ experience before and after interdisciplinary learning. It can be seen from the interviews that it is especially difficult for interdisciplinary teaching practitioners to understand and organize students’ prior non-educational experience. Therefore, interdisciplinary education should be regarded as a long-term process, and students should be exposed to interdisciplinary learning earlier to have interdisciplinary experience, which may gradually build students’ interdisciplinary cognition, establish their interdisciplinary values, facilitate the growth of interdisciplinary learning motivation, and promote the improvement of interdisciplinary integration ability. In this regard, Wilhelmsson et al. have the same understanding: the acquisition of interdisciplinary integration ability is a process that must start early in education ( Wilhelmsson et al. 2009 ).

Individual Traits

The results of the interview analyses of this category show that the students majoring in Engineering, Management, and Design have different focuses and ways of dealing with problems in interdisciplinary learning, and the impact of their learning attitudes on learning outcomes. Several teachers pointed out that in interdisciplinary learning, Engineering students seem to be more conservative, so they think in a less creative way; Design students are original and have many ideas, but their consideration of practical application may be incomprehensive; Business students intend to save effort in their learning, and they often avoid wasting energy, time, and other learning risks. In this regard, some teachers pointed out that it is easier for the students to believe the value that is easier for them to understand or is more similar to their own major. On the other hand, the teachers believed that a serious attitude, a sense of responsibility and self-discipline reflected in students’ individual traits are still important factors for positive outcomes in interdisciplinary learning. Especially students who are willing to use what they have learned to analyze and organize can be a high achiever in the end (see Appendix D for details). It may be inferred that the values or learning styles of different disciplines affect students’ perspectives on problems and the learning strategies and actions they take. Meanwhile, students’ individual traits also seem to affect their own learning strategies, and thus affect the final interdisciplinary learning outcomes. Some researchers believed that disciplines affect the learning methods students adopt over time ( Tarabashkina and Lietz 2011 ). The study by Bruce et al. found that for successful interdisciplinary learning, personalities and attitudes should be at least as important as disciplinary foundations and specialization. They believed that an excellent interdisciplinary person should have a high tolerance for ambiguity, and they should not prematurely narrow a problem to a limited set of dimensions, but instead, they should spend time exploring a range of dimensions and testing several potential boundaries; therefore, they also believed that an ideal interdisciplinary person should have curiosity about and willingness to learn other disciplines, and be open to the ideas and experience from other disciplines, etc. ( Bruce et al. 2004 ). In this regard, Woods also believed that curiosity and openness represent a willingness to suspend doubts about other disciplinary cultures and suspend a hold on beliefs in their own disciplinary culture ( Woods 2007 ). Tik believed that openness refers to the characteristics of students who are curious and intelligent. They are open to new experiences and willing to adopt other learning strategies; while responsibility refers to the characteristics of achievement, organization and perseverance, and students with these traits tend to be more inclined to use higher-order cognitive skills, such as critical thinking and metacognition ( Tik 2020 ). Therefore, both students’ own individual traits and their characteristics caused by discipline attributes should have an impact on their interdisciplinary learning outcomes. Meanwhile, as shown in Table 7 , the influence of students’ individual traits is greater than students’ characteristics caused by discipline attributes based on the number of respondents who mentioned them and the frequencies of mentions. Together with the previous research on student learning attitudes, this may show that the influence of student individual traits is slightly more important than the influence of student discipline characteristics on interdisciplinary learning outcomes.

Pressure and Burden

The interview materials of this category reflect that the intensity of learning and the increased strictness of teachers’ demands for task completion appear to lead to negative effects on student interdisciplinary learning. For example, the teachers pointed out that the students reported that the courses in the department of Design bring a heavy course load, and the courses given by the teachers of the department of Engineering are too in-depth, so that the students feel a heavy burden; in the later integrated courses, the students cannot accept the course output requirements and strictness of the teachers. In addition, students’ inadaptation of interdisciplinary learning also causes them to worry that their academic GPA will be lowered, so they think interdisciplinary learning is a burden for them, and eventually many students withdraw from the program (see Appendix C for details). This finding may be supported by Matthews et al. They embed programming teaching content in the study of first-year Biology undergraduates and required students to apply their programming skills in a quantitative real-world setting. However, it was too complex for the students to respond effectively, so the students’ feedback on this were negative to a large extent ( Matthews et al. 2010 ). Moreover, Chen et al. have similar findings. They pointed out that a heavy study load increases the difficulty of students participating in various courses outside their own discipline and reduces their attention paid to interdisciplinary learning. Meanwhile, this may be a reason for the declining trend of students’ interest and value in interdisciplinary learning ( Chen et al. 2009 ). Indeed, judging from the total credits of undergraduate majors in the three colleges of the school, each major has 150 credits, and participating in this interdisciplinary program will add 35 credits, which is equivalent to adding more than four credits per semester and 70 credit hours of lessons. In fact, the number of all the courses is not evenly distributed in each semester. If the interdisciplinary teachers have higher requirements on coursework and put more pressure on their students, especially in certain semesters with more class hours, students will definitely feel the weight of a heavier study load, and as a result, they will naturally choose to give up interdisciplinary learning to ensure their own disciplinary learning. Therefore, when designing interdisciplinary curriculum content and student output requirements, teachers should comprehensively consider the pressure and burden brought to students by the learning load of both interdisciplinary courses and the courses of their own discipline. This requires more adequate and effective communication and coordination between interdisciplinary teachers and teachers of different disciplines, in order to bring positive effects on student learning outcomes.

Disciplinary Factors

Teachers’ disciplinary views and biases can influence how students learn and experience in interdisciplinary learning ( Self et al. 2019 ). Self et al. found that some teachers’ own disciplinary biases can be transformed into their expectations for students, which leads students to change their learning methods and learning outcomes in their studies to meet the expectations of disciplinary teachers. Our research findings support this view. The interview participants indicated that disciplinary teachers are accustomed to using their values to influence students. They lack support for the students participating in interdisciplinary courses. For example, non-interdisciplinary teachers show their inhibition or contempt for interdisciplinarity or the interdisciplinary program before the students being involved in interdisciplinary leaning when teaching their own disciplinary courses, so that the students have distrust of interdisciplinary courses and teachers. In fact, this attitude of disciplinary teachers towards interdisciplinary learning should be relatively common. First, teachers who lack interdisciplinary experience may also lack enthusiasm or willingness to develop interdisciplinary projects ( Gardner et al. 2014 ; Van den Beemt et al. 2020 ). Second, the academic community and higher education community generally regard disciplines as cornerstones, so they tend to marginalize more comprehensive areas of knowledge or educational programs ( Palaiologou 2010 ). Brew suggested in his research that many scholars tend to overemphasize the importance of disciplinary affiliation ( Brew 2008 ). In this regard, Lindvig et al. believed that interdisciplinary teaching, which is different from the accustomed way of disciplinary teaching, may be regarded as a threat to hinder the construction of the disciplinary identity, so this should be one of the difficulties that interdisciplinary teaching is facing ( Lindvig and Ulriksen 2019 ). Obviously, the influence of teachers’ words and deeds based on disciplinary thinking and values brings challenges to students in interdisciplinary learning. In the operation and management of interdisciplinary programs, schools need to establish common interdisciplinary educational values among teachers of various disciplines to avoid negative impact on interdisciplinary teaching by disciplinary teachers who are not involved in interdisciplinary teaching. In addition, some respondents pointed out that Design teachers and Engineering teachers have different evaluation criteria for student outputs, which has led to students’ frustration in interdisciplinary learning. For example, Engineering students’ award-winning works in disciplinary competitions cannot be recognized by Design teachers (see Appendix C for details). This is consistent with the findings of Self et al., and in their study, professors of Industrial Design rarely collaborate with professors of Ergonomics, and the differences between these two disciplines have an impact on course learning outcomes. What is considered important by everyone is not considered important in Ergonomics ( Self et al. 2019 ). If such disparities between disciplines are not balanced and integrated to form judging criteria based on a shared value, challenges will be created for interdisciplinary learning and teaching.

5. Conclusions

This study uses the Core Interdisciplinary Integration Ability Scale developed by Chen et al. to measure the interdisciplinary integration ability of the students participating in the interdisciplinary program that integrates design, engineering, business and other disciplines. Under the theoretical framework of Biggs, Spelt, and Liu et al. on interdisciplinary learning outcomes, student attributes, and interdisciplinary learning environment, a qualitative analysis of interviews with interdisciplinary teachers and related personnel is conducted. The research group found that there is no significant difference in interdisciplinary integration ability between the students participating in the interdisciplinary program and the students involved in a single discipline study, including the Industrial Design, Electrical Engineering, and Materials Engineering students. Based on the qualitative analysis results of the interview data, the authors believe that the reasons why there is no significant difference may be problems with student attributes, including the lack of motivation, lack of prior interdisciplinary experience, influence of individual traits, and problems with interdisciplinary learning environment, including the increased learning pressure and burden, and interference of disciplinary factors. Our findings can provide some references for the future development and design of interdisciplinary programs and interdisciplinary teaching. Especially for the establishment and shaping of interdisciplinary learning motivation, for students in East Asia, attention should be paid to the substantial connection between students’ short-term goals and interdisciplinary learning, as well as to the construction of the correlation between students’ own disciplines and interdisciplinary learning content; meanwhile, for the interdisciplinary basic course teaching in the early stage of interdisciplinary programs, we should take into account the fact that the students under these programs are from different disciplines, and carry out teaching from the perspective of knowledge integration, so as to avoid using original discipline teaching methods to simply make interdisciplinary teaching into multi-disciplinary teaching. Besides, it may be beneficial to start students’ experience in interdisciplinary learning or research at an earlier stage to gradually form students’ interdisciplinary cognitions and values. In addition, when establishing a teaching design for students, attention should be paid to their individual traits and there should be sufficient communication and coordination with students’ disciplinary teachers to achieve a balance between interdisciplinary and disciplinary learning, form a commonly recognized evaluation standard, and try to avoid negative effects on the learning outcomes of interdisciplinary students due to the increase of students’ learning pressure and academic burden or the interference of disciplinary factors.

Due to the different systems and structures of interdisciplinary programs among universities, this study did not collect data from other universities for comparison. Besides, because of some students’ unwillingness and the impact of the COVID-19 pandemic, the research group did not collect any samples of students majoring in Business Management and all tested students’ opinions on the directness of the interdisciplinary courses in this university. With the graduation of this group of students, the collection of relevant samples has become unlikely. The lack of such sample data makes it difficult for us to truly and directly understand the psychological state and opinions of the students participating in this interdisciplinary program. Only relying on the teachers’ observation, description and evaluation of the students may miss the details of some students’ conditions, resulting in some problems not being discovered in time. This indeed brings about some limitations to this research. Fortunately, the measurement results of students’ interdisciplinary ability and the analysis results of teacher interviews can confirm each other, so this research group believes that our experimental data are convincing. Our findings further confirm the previous view that students participating in interdisciplinary learning may not necessarily improve their interdisciplinary ability. Meanwhile, on this basis, according to the empirical results, this study points out the specific factors that bring interdisciplinary learning challenges to students. This provides inspiration for subsequent related research. In addition, this study only discusses the impact of student attributes and interdisciplinary learning environment on learning outcomes, but from the theoretical model of Biggs et al., learning outcomes can also affect student attributes and learning environment. This will open the way for our future research considering, e.g., how the improvement of students’ interdisciplinary ability will stimulate students’ interdisciplinary motivation.

Core Interdisciplinary Integration Ability Scale Questions.

Compiled and translated from ( Chen et al. 2017 ).

Coding of D-01 Learning Condition Feedback.

Under the principle of not changing the meaning of the respondents’ conversations, in order to show the content of the conversations more accurately, the authors annotate what has been omitted or referred to in the conversations through ( ) according to the context of the interviews.

Coding of D-02 interdisciplinary Learning Environment.

Coding of D-03 Student Attributes.

Funding Statement

The study was financially supported by MOST 110-2410-H-036-005-.

Author Contributions

Data curation, D.-D.X.; formal analysis, K.-Y.W.; investigation, C.X., D.-D.X., W.-Q.L. and K.-Y.W.; methodology, C.X.; project administration, C.-F.W.; supervision, C.-F.W.; writing—original draft, C.X.; writing—review & editing, C.X. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Research Ethics Committee of National Taiwan University (protocol code 202105ES161).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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