difference between result and conclusion in research paper

Research Skills

Results, discussion, and conclusion, results/findings.

The Results (or Findings) section follows the Methods and precedes the Discussion section. This is where the authors provide the data collected during their study. That data can sometimes be difficult to understand because it is often quite technical. Do not let this intimidate you; you will discover the significance of the results next.

The Discussion section follows the Results and precedes the Conclusions and Recommendations section. It is here that the authors indicate the significance of their results. They answer the question, “Why did we get the results we did?” This section provides logical explanations for the results from the study. Those explanations are often reached by comparing and contrasting the results to prior studies’ findings, so citations to the studies discussed in the Literature Review generally reappear here. This section also usually discusses the limitations of the study and speculates on what the results say about the problem(s) identified in the research question(s). This section is very important because it is finally moving towards an argument. Since the researchers interpret their results according to theoretical underpinnings in this section, there is more room for difference of opinion. The way the authors interpret their results may be quite different from the way you would interpret them or the way another researcher would interpret them.

Note: Some articles collapse the Discussion and Conclusion sections together under a single heading (usually “Conclusion”). If you don’t see a separate Discussion section, don’t worry.  Instead, look in the nearby sections for the types of information described in the paragraph above.

When you first skim an article, it may be useful to go straight to the Conclusion and see if you can figure out what the thesis is since it is usually in this final section. The research gap identified in the introduction indicates what the researchers wanted to look at; what did they claim, ultimately, when they completed their research? What did it show them—and what are they showing us—about the topic? Did they get the results they expected? Why or why not? The thesis is not a sweeping proclamation; rather, it is likely a very reasonable and conditional claim.

Nearly every research article ends by inviting other scholars to continue the work by saying that more research needs to be done on the matter. However, do not mistake this directive for the thesis; it’s a convention. Often, the authors provide specific details about future possible studies that could or should be conducted in order to make more sense of their own study’s conclusions.

• Parts of An Article. Authored by : Kerry Bowers. Provided by : University of Mississippi. Project : WRIT 250 Committee OER Project. License : CC BY-SA: Attribution-ShareAlike

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How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

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The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

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Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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Guide to Writing the Results and Discussion Sections of a Scientific Article

A quality research paper has both the qualities of in-depth research and good writing ( Bordage, 2001 ). In addition, a research paper must be clear, concise, and effective when presenting the information in an organized structure with a logical manner ( Sandercock, 2013 ).

In this article, we will take a closer look at the results and discussion section. Composing each of these carefully with sufficient data and well-constructed arguments can help improve your paper overall.

The results section of your research paper contains a description about the main findings of your research, whereas the discussion section interprets the results for readers and provides the significance of the findings. The discussion should not repeat the results.

Let’s dive in a little deeper about how to properly, and clearly organize each part.

How to Organize the Results Section

Since your results follow your methods, you’ll want to provide information about what you discovered from the methods you used, such as your research data. In other words, what were the outcomes of the methods you used?

You may also include information about the measurement of your data, variables, treatments, and statistical analyses.

To start, organize your research data based on how important those are in relation to your research questions. This section should focus on showing major results that support or reject your research hypothesis. Include your least important data as supplemental materials when submitting to the journal.

The next step is to prioritize your research data based on importance – focusing heavily on the information that directly relates to your research questions using the subheadings.

The organization of the subheadings for the results section usually mirrors the methods section. It should follow a logical and chronological order.

Subheading organization

Subheadings within your results section are primarily going to detail major findings within each important experiment. And the first paragraph of your results section should be dedicated to your main findings (findings that answer your overall research question and lead to your conclusion) (Hofmann, 2013).

In the book “Writing in the Biological Sciences,” author Angelika Hofmann recommends you structure your results subsection paragraphs as follows:

• Experimental purpose
• Interpretation

Each subheading may contain a combination of ( Bahadoran, 2019 ; Hofmann, 2013, pg. 62-63):

• Text: to explain about the research data
• Figures: to display the research data and to show trends or relationships, for examples using graphs or gel pictures.
• Tables: to represent a large data and exact value

Decide on the best way to present your data — in the form of text, figures or tables (Hofmann, 2013).

Data or Results?

Sometimes we get confused about how to differentiate between data and results . Data are information (facts or numbers) that you collected from your research ( Bahadoran, 2019 ).

Whereas, results are the texts presenting the meaning of your research data ( Bahadoran, 2019 ).

One mistake that some authors often make is to use text to direct the reader to find a specific table or figure without further explanation. This can confuse readers when they interpret data completely different from what the authors had in mind. So, you should briefly explain your data to make your information clear for the readers.

Common Elements in Figures and Tables

Figures and tables present information about your research data visually. The use of these visual elements is necessary so readers can summarize, compare, and interpret large data at a glance. You can use graphs or figures to compare groups or patterns. Whereas, tables are ideal to present large quantities of data and exact values.

Several components are needed to create your figures and tables. These elements are important to sort your data based on groups (or treatments). It will be easier for the readers to see the similarities and differences among the groups.

When presenting your research data in the form of figures and tables, organize your data based on the steps of the research leading you into a conclusion.

Common elements of the figures (Bahadoran, 2019):

• Figure number
• Figure title
• Figure legend (for example a brief title, experimental/statistical information, or definition of symbols).

Tables in the result section may contain several elements (Bahadoran, 2019):

• Table number
• Table title
• Row headings (for example groups)
• Column headings
• Row subheadings (for example categories or groups)
• Column subheadings (for example categories or variables)
• Footnotes (for example statistical analyses)

Tips to Write the Results Section

• Direct the reader to the research data and explain the meaning of the data.
• Avoid using a repetitive sentence structure to explain a new set of data.
• Write and highlight important findings in your results.
• Use the same order as the subheadings of the methods section.
• Match the results with the research questions from the introduction. Your results should answer your research questions.
• Be sure to mention the figures and tables in the body of your text.
• Make sure there is no mismatch between the table number or the figure number in text and in figure/tables.
• Only present data that support the significance of your study. You can provide additional data in tables and figures as supplementary material.

How to Organize the Discussion Section

It’s not enough to use figures and tables in your results section to convince your readers about the importance of your findings. You need to support your results section by providing more explanation in the discussion section about what you found.

In the discussion section, based on your findings, you defend the answers to your research questions and create arguments to support your conclusions.

Below is a list of questions to guide you when organizing the structure of your discussion section ( Viera et al ., 2018 ):

• What experiments did you conduct and what were the results?
• What do the results mean?
• What were the important results from your study?
• How did the results answer your research questions?
• Did your results support your hypothesis or reject your hypothesis?
• What are the variables or factors that might affect your results?
• What were the strengths and limitations of your study?
• What other published works support your findings?
• What other published works contradict your findings?
• What possible factors might cause your findings different from other findings?
• What is the significance of your research?
• What are new research questions to explore based on your findings?

Organizing the Discussion Section

The structure of the discussion section may be different from one paper to another, but it commonly has a beginning, middle-, and end- to the section.

One way to organize the structure of the discussion section is by dividing it into three parts (Ghasemi, 2019):

• The beginning: The first sentence of the first paragraph should state the importance and the new findings of your research. The first paragraph may also include answers to your research questions mentioned in your introduction section.
• The middle: The middle should contain the interpretations of the results to defend your answers, the strength of the study, the limitations of the study, and an update literature review that validates your findings.
• The end: The end concludes the study and the significance of your research.

Another possible way to organize the discussion section was proposed by Michael Docherty in British Medical Journal: is by using this structure ( Docherty, 1999 ):

• Discussion of important findings
• Comparison of your results with other published works
• Include the strengths and limitations of the study
• Conclusion and possible implications of your study, including the significance of your study – address why and how is it meaningful
• Future research questions based on your findings

Finally, a last option is structuring your discussion this way (Hofmann, 2013, pg. 104):

• First Paragraph: Provide an interpretation based on your key findings. Then support your interpretation with evidence.
• Secondary results
• Limitations
• Unexpected findings
• Comparisons to previous publications
• Last Paragraph: The last paragraph should provide a summarization (conclusion) along with detailing the significance, implications and potential next steps.

Remember, at the heart of the discussion section is presenting an interpretation of your major findings.

Tips to Write the Discussion Section

• Highlight the significance of your findings
• Mention how the study will fill a gap in knowledge.
• Indicate the implication of your research.
• Avoid generalizing, misinterpreting your results, drawing a conclusion with no supportive findings from your results.

Aggarwal, R., & Sahni, P. (2018). The Results Section. In Reporting and Publishing Research in the Biomedical Sciences (pp. 21-38): Springer.

Bahadoran, Z., Mirmiran, P., Zadeh-Vakili, A., Hosseinpanah, F., & Ghasemi, A. (2019). The principles of biomedical scientific writing: Results. International journal of endocrinology and metabolism, 17(2).

Bordage, G. (2001). Reasons reviewers reject and accept manuscripts: the strengths and weaknesses in medical education reports. Academic medicine, 76(9), 889-896.

Cals, J. W., & Kotz, D. (2013). Effective writing and publishing scientific papers, part VI: discussion. Journal of clinical epidemiology, 66(10), 1064.

Docherty, M., & Smith, R. (1999). The case for structuring the discussion of scientific papers: Much the same as that for structuring abstracts. In: British Medical Journal Publishing Group.

Faber, J. (2017). Writing scientific manuscripts: most common mistakes. Dental press journal of orthodontics, 22(5), 113-117.

Fletcher, R. H., & Fletcher, S. W. (2018). The discussion section. In Reporting and Publishing Research in the Biomedical Sciences (pp. 39-48): Springer.

Ghasemi, A., Bahadoran, Z., Mirmiran, P., Hosseinpanah, F., Shiva, N., & Zadeh-Vakili, A. (2019). The Principles of Biomedical Scientific Writing: Discussion. International journal of endocrinology and metabolism, 17(3).

Hofmann, A. H. (2013). Writing in the biological sciences: a comprehensive resource for scientific communication . New York: Oxford University Press.

Kotz, D., & Cals, J. W. (2013). Effective writing and publishing scientific papers, part V: results. Journal of clinical epidemiology, 66(9), 945.

Mack, C. (2014). How to Write a Good Scientific Paper: Structure and Organization. Journal of Micro/ Nanolithography, MEMS, and MOEMS, 13. doi:10.1117/1.JMM.13.4.040101

Moore, A. (2016). What's in a Discussion section? Exploiting 2‐dimensionality in the online world…. Bioessays, 38(12), 1185-1185.

Peat, J., Elliott, E., Baur, L., & Keena, V. (2013). Scientific writing: easy when you know how: John Wiley & Sons.

Sandercock, P. M. L. (2012). How to write and publish a scientific article. Canadian Society of Forensic Science Journal, 45(1), 1-5.

Teo, E. K. (2016). Effective Medical Writing: The Write Way to Get Published. Singapore Medical Journal, 57(9), 523-523. doi:10.11622/smedj.2016156

Van Way III, C. W. (2007). Writing a scientific paper. Nutrition in Clinical Practice, 22(6), 636-640.

Vieira, R. F., Lima, R. C. d., & Mizubuti, E. S. G. (2019). How to write the discussion section of a scientific article. Acta Scientiarum. Agronomy, 41.

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Conclusion vs. Results: What's the Difference?

Key Differences

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Discussion vs Conclusion: Researcher's Compact Guide

Table of Contents

If you are a researcher or a student, understanding the difference between a discussion and a conclusion is crucial especially when you are working on your academic projects. These two sections play distinct roles in your paper, and knowing how to approach each one can greatly improve the quality of your work.

This guide will delve into the nuances of both sections, providing a comprehensive overview of their purposes, structures, and writing strategies.

Understanding the discussion section of a research paper

The discussion section of a research paper is where you interpret and explain your research findings. It's a section for you to explore the implications of your results, compare them to previous research, and address any limitations in your study.

One of the main purposes of the discussion section is to answer the research question. You should provide a detailed explanation of the results and how they relate to your hypothesis or research question. This part of the paper is your opportunity to show that you have made a valuable contribution to your field of study.

Structure of the Discussion Section

The structure of the discussion section can vary depending on the nature of the research and the guidelines of the publication. However, a typical structure might include the following elements:

• Restatement of the research problem
• Summary of the main findings
• Interpretation of the results
• Comparison with previous research
• Explanation of any unexpected findings or discrepancies
• Discussion of the implications of the results
• Identification of limitations and suggestions for prospective research

Tips for Writing Discussion Section

When writing the discussion section, it's important to stay focused on your research question and avoid talking about unrelated areas. Be sure to interpret your findings in the context of the research question and the existing literature in your field.

It's also crucial to be honest about the limitations of your study. Acknowledging these limitations not only enhances the credibility of your research but also provides valuable information for budding researchers.

Understanding the Conclusion Section

The conclusion section of a research paper is where you summarize your research and its implications. Unlike the discussion section, the conclusion is not the place for a detailed analysis of your results. Instead, it's where you wrap up your argument and leave the reader with a clear understanding of your research and its significance.

The conclusion should provide a succinct summary of your research question, methods, results, and main findings. It should also discuss the broader implications of your research and suggest areas for future study.

Structure of the Conclusion Section

The structure of the conclusion section is typically more straightforward than that of the discussion section. A typical conclusion might include the following elements:

• Restatement of the research question
• Discussion of the implications of the research
• Suggestions for future research

Tips for Writing Conclusion Section

When writing the conclusion section, it's important to be concise and to the point. Try not to introduce new information or arguments in the conclusion section. Instead, simply focus on summarizing your research and highlighting its significance.

It's also important to make your conclusion engaging and indelible. Consider ending with a strong statement that emphasizes the importance of your research and leaves a lasting impression on the reader.

Discussion Vs. Conclusion: Key Differences

While the discussion and conclusion sections of a research paper have some similarities, they serve different purposes and should be approached differently. The discussion section is where you interpret and analyze your results, while the conclusion is where you summarize your research and highlight its significance.

Another key difference is the level of detail. The discussion section typically includes a detailed analysis of the results, while the conclusion provides a concise summary of the research. Let’s take a look at the differences between the discussion and conclusion sections in various aspects

Whether you're writing a discussion section or a conclusion, it's important to choose the right approach for your research. Consider the nature of your study, the guidelines of the publication, and the expectations of your audience when deciding how to structure and write these sections.

Remember, the goal of both sections is to communicate your research effectively and make a meaningful contribution to your field. By understanding the differences between a discussion and a conclusion, you can ensure that your research paper is clear, coherent, and impactful.

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Discussion and Conclusions

Your Discussion and Conclusions sections should answer the question: What do your results mean?

In other words, the majority of the Discussion and Conclusions sections should be an interpretation of your results. You should:

• Discuss your conclusions in order of  most to least important.
• Compare  your results with those from other studies: Are they consistent? If not, discuss possible reasons for the difference.
• Mention any  inconclusive results  and explain them as best you can. You may suggest additional experiments needed to clarify your results.
• Briefly describe the  limitations  of your study to show reviewers and readers that you have considered your experiment’s weaknesses. Many researchers are hesitant to do this as they feel it highlights the weaknesses in their research to the editor and reviewer. However doing this actually makes a positive impression of your paper as it makes it clear that you have an in depth understanding of your topic and can think objectively of your research.
• Discuss  what your results may mean  for researchers in the same field as you, researchers in other fields, and the general public. How could your findings be applied?
• State how your results  extend the findings  of previous studies.
• If your findings are preliminary, suggest  future studies  that need to be carried out.
• At the end of your Discussion and Conclusions sections,  state your main conclusions once again .

Back │ Next

Discussion Vs. Conclusion: Know the Difference Before Drafting Manuscripts

The discussion section of your manuscript can be one of the hardest to write as it requires you to think about the meaning of the research you have done. An effective discussion section tells the reader what your study means and why it is important. In this article, we will cover some pointers for writing clear/well-organized discussion and conclusion sections and discuss what should NOT be a part of these sections.

What Should be in the Discussion Section?

Your discussion is, in short, the answer to the question “what do my results mean?” The discussion section of the manuscript should come after the methods and results section and before the conclusion. It should relate back directly to the questions posed in your introduction, and contextualize your results within the literature you have covered in your literature review . In order to make your discussion section engaging, you should include the following information:

• The major findings of your study
• The meaning of those findings
• How these findings relate to what others have done
• Limitations of your findings
• An explanation for any surprising, unexpected, or inconclusive results
• Suggestions for further research

Your discussion should NOT include any of the following information:

• New results or data not presented previously in the paper
• Unwarranted speculation
• Tangential issues
• Conclusions not supported by your data

Related: Avoid outright rejection with a well-structured manuscript. Check out these resources and improve your manuscript now!

How to Make the Discussion Section Effective?

There are several ways to make the discussion section of your manuscript effective, interesting, and relevant. Hear from one of our experts on how to structure your discussion section and distinguish it from the results section:

Now that we have listened to how to approach writing a discussion section, let’s delve deeper into some essential tips with a few examples:

• Most writing guides recommend listing the findings of your study in decreasing order of their importance. You would not want your reader to lose sight of the key results that you found. Therefore, put the most important finding front and center. Example: Imagine that you conduct a study aimed at evaluating the effectiveness of stent placement in patients with partially blocked arteries. You find that despite this being a common first-line treatment, stents are not effective for patients with partially blocked arteries. The study also discovers that patients treated with a stent tend to develop asthma at slightly higher rates than those who receive no such treatment.

Which sentence would you choose to begin your discussion? Our findings suggest that patients who had partially blocked arteries and were treated with a stent as the first line of intervention had no better outcomes than patients who were not given any surgical treatments.   Our findings noted that patients who received stents demonstrated slightly higher rates of asthma than those who did not. In addition, the placement of a stent did not impact their rates of cardiac events in a statistically significant way.

If you chose the first example, you are correct!

• If you are not sure which results are the most important, go back to your research question and start from there. The most important result is the one that answers your research question.
• It is also necessary to contextualize the meaning of your findings for the reader. What does previous literature say, and do your results agree? Do your results elaborate on previous findings, or differ significantly?
• In our stent example, if previous literature found that stents were an effective line of treatment for patients with partially blocked arteries, you should explore why your interpretation seems different in the discussion section. Did your methodology differ? Was your study broader in scope and larger in scale than the previous studies? Were there any limitations to previous studies that your study overcame? Alternatively, is it possible that your own study could be incorrect because of some difficulties you had in carrying it out? The discussion section should narrate a coherent story to the target audience.
• Finally, remember not to introduce new ideas/data, or speculate wildly on the possible future implications of your study in the discussion section. However, considering alternative explanations for your results is encouraged.

Avoiding Confusion in your Conclusion!

Many writers confuse the information they should include in their discussion with the information they should place in their conclusion. One easy way to avoid this confusion is to think of your conclusion as a summary of everything that you have said thus far. In the conclusion section, you remind the reader of what they have just read. Your conclusion should:

• Restate your hypothesis or research question
• Restate your major findings
• Tell the reader what contribution your study has made to the existing literature
• Highlight any limitations of your study
• State future directions for research/recommendations

Your conclusion should NOT:

• Introduce new arguments
• Introduce new data
• Fail to include your research question
• Fail to state your major results

An appropriate conclusion to our hypothetical stent study might read as follows:

In this study, we examined the effectiveness of stent placement. We compared the patients with partially blocked arteries to those with non-surgical interventions. After examining the five-year medical outcomes of 19,457 patients in the Greater Dallas area, our statistical analysis concluded that the placement of a stent resulted in outcomes that were no better than non-surgical interventions such as diet and exercise. Although previous findings indicated that stent placement improved patient outcomes, our study followed a greater number of patients than those in major studies conducted previously. It is possible that outcomes would vary if measured over a ten or fifteen year period. Future researchers should consider investigating the impact of stent placement in these patients over a longer period (five years or longer). Regardless, our results point to the need for medical practitioners to reconsider the placement of a stent as the first line of treatment as non-surgical interventions may have equally positive outcomes for patients.

Did you find the tips in this article relevant? What is the most challenging portion of a research paper for you to write? Let us know in the comments section below!

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Helped massively with writing a good conclusion!

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE:   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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Discussion vs Conclusion: What is the Difference?

Understanding the difference between the discussion and conclusion sections in research papers is crucial for researchers to convey the importance of their work effectively. While both sections interpret research findings, they have distinct focuses.    

The conclusion summarizes the main points, and the discussion goes into more detail about the findings. Both are crucial components, each fulfilling its unique role in presenting and concluding a research study.   

Table of Contents

In this article, we explore these sections, guiding how to structure and articulate discussion vs conclusion clearly. Ultimately, both sections should address the question: What do the research results mean? (1)(2)  

Purpose: Discussion vs Conclusion  

The discussion section, as a comprehensive review of the research findings, plays a pivotal role in connecting all the preceding sections. It acts as a bridge, highlighting and illustrating the connections between them. Here, the author’s interpretation, analysis, and explanation of the research results matter and how they align with existing literature is crucial for the study’s continuity and significance.    

Moreover, the discussion section demands self-reflection, acknowledging the study’s limitations and identifying areas for future exploration. It deep dives into the implications of the findings, drawing meaningful conclusions from their analysis.   

On the other hand, the conclusion section serves as a concise summary of the paper’s main points. While it touches upon key aspects of the research, it does not offer the same level of depth and analysis as the discussion section.    

Rather than delving into interpretation and analysis, the conclusion section presents the essence of the study and emphasizes its significance to the reader. It acts as a final statement, leaving the reader with a clear understanding of the research’s implications without going into the details. (2)(3) 

What to include in the discussion section?

The discussion section can be divided into three parts: introduction, body, and conclusion.   

In the introduction, you present the main idea of your study without repeating what you said in the introduction section. You start with a clear statement and answer critical questions: What’s the issue? What solutions can you suggest? What’s new and innovative about your study? What contribution does it make towards resolving the problem?   

The body of the discussion section interprets your results, discusses their implications, and compares them to previous studies. You explain how your work is similar to or different from others and analyze the results thematically. You also explain why your study is essential for future research.   

In the conclusion section, you mention the strengths of your study while being objective about its limitations. You suggest future directions for research and summarize the main findings in a simple “take-home message.” (4)(5) 

What to include in the c onclusion section?

The conclusion section, as the opportunity to make a final statement on the topics discussed, is not just a summary. It’s a chance to leave a lasting impression. Just like the introduction sets the tone, the conclusion can leave a lasting impact. By highlighting key findings that shed new light on the research problem, are unexpected, or have significant practical implications, you can make your research memorable and impactful.   

Summarize your thoughts and emphasize the broader significance of your study and ideas. Use this section to elaborate on the impact of your findings, especially if your study offers a unique perspective on the research problem.   

Identify how your study fills a gap in the literature that you identified in your literature review. Explain how your research addresses this gap and adds to the understanding of the topic.   

Lastly, in the conclusion section, consider introducing new ways of thinking about the research problem based on your findings. This doesn’t mean adding new information but rather offering fresh insights. This is an opportunity to show the potential of your research to contribute to the field and inspire further studies. (6)(7) 

Discussion vs. Conclusion: Key Differences  

References:  

• Discussion and Conclusion – Springer  
• Discussion Section for Research Papers – San José State University   
• Organizing Academic Research Papers: 9. The Conclusion – Sacred Heart University  
• How to write a discussion section? – National Library of Medicine  
• How to Write the Discussion? – Springer  
• Organizing Your Social Sciences Research Paper – University of Southern California  
• How to Write a Conclusion for a Research Paper – Mind the Graph  
• Discussion vs Conclusion: Everything You Need to Know – FirstEditing.com  

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The difference between abstract and conclusion

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Table of Contents

Ready, set… wait! Having new research to share with the world is something truly amazing: standing just a few pages away of stirring science in a way that could actually generate effective changes in society as we know it. But the time comes when we, as authors, need to put excitement aside and stop to think the expectations of how to present our ideas. Sharing knowledge in academia is just like anything else in life. It takes discipline to follow certain rules, criteria, and guidelines to be clear and efficient about our message, in the end.

The large majority of scientific papers are organized under sections, with a specific order, that help readers recognize and follow the author’s train of thought. The most important ones, following the well-known IMRaD structure, include:

• I ntroduction
• D iscussion/Conclusion

In this article, however, we will focus on a section outside the paper’s main body, but essential to most scientific output formats: the abstract. A good abstract constitutes a fundamental tool to get attention for our work among scientists in the same field of study. Moreover, let’s also learn how to differentiate it from the conclusion, since separating these two sections, in terms of message, might turn out to be more challenging than one could expect.

What is the abstract of a paper?

Abstracts are independent short texts – generally, not exceeding 10% of the paper’s length and/or 250 words – where the main purpose is to capture the essence of a paper to let people decide quickly if it’s of their interest or not. At the same time, a good abstract should also generate curiosity and excitement among an audience by making the proper impression upon the target reader. In other words, abstracts must be more than plain descriptions of their related paper’s contents; above all, they should be powerful statements enhancing the scientific novelty of the research and its importance for science in general. In most abstracts, main findings and key questions for further discussion are included in order to stress, once more, the relevance of the presented work.

In general, it’s important to specify the topic, aim and scope of your research in the abstract. The vast majority of journals select papers for publication just by reading their abstracts. Hence, if you are in the process of submitting your paper to a journal, it is vital that you check the journal’s guidelines before you embrace the task of writing one. They may vary a lot, from publication to publication.

Other than selection, another main purpose of an abstract is to allow the indexation of larger works in academic and scientific databases. Careful word selection, and a handful of clever keywords, will make an effective difference whether your paper jumps easily before the eyes of those who want to read it, or in the contrary, remains forever hidden among many, many others.

Check these tips for choosing right keywords for your manuscript .

Knowing the rules, using the right language and producing a flawless text are all key to rendering a perfect abstract. On the one hand, you need very good summarizing skills to provide a clear and general overview on your main topic and arguments. On the other hand, an abstract should also contain powerful and meaningful words to create curiosity and excitement about your paper. Don’t forget to revise your abstract constantly – in Elsevier, our team of professional text editors and revisors can help you achieve the perfect balance that could be that extra nudge to skyrocket your science career.

What is the conclusion in a research paper?

Unlike the abstract, the conclusion is the last part of the main body of a paper or thesis. It is where a researcher actually answers the big question that impelled him or her to undertake the research project in the first place. However, despite of the different roles that an abstract and conclusion play in a scientific paper, many aspects in drafting a conclusion can actually relate to writing an abstract:

• Length – Both abstract and conclusion shouldn’t be very long.
• Concise character – Their content must be clear and expertly summarized, underlining important ideas and avoiding redundancy.
• Impactful language – Both sections are ideal to call attention to your work as a scientist. By using the right words, it is possible to point out how relevant your paper can be in the scientific community.

A conclusion must always start by addressing the main topic of the thesis, in order to remind the reader “where it all began.” The next step is to briefly bring forward results previously discussed at some point in the paper, however not too extensively. The aim is to put everything on the table in order to finish the line of thought presented throughout the document. Furthermore, in a conclusion section, it is not only important to bring forward results and findings but, above all, stress their significance. Add impactful language and construct clear, but solid statements. This tone should be strong enough to inspire other researchers to follow your work in the future, and to enhance your chances of growing into a respected scientist among your peers.

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Thesis: what is the difference between discussion and conclusion?

I am currently writing my bachelor thesis and I would like to have a clear definition of discussion and conclusion. Preferably including a distinction between the discussion and the conclusion part.

As far as I've understood the conclusion introduces the research question (again) and supplies the a (final?!) summary of ideas / solutions as well as an evaluation of the solutions...leading to the acceptance or refusal of (research) hypotheses. No new information is given, no examples are made, no issues interpreted.

In contrast to this the discussion starts with the results right away, highlights the best results, describes the contribution / purpose of the thesis and so on. Therefore less general than conclusion; more like a detailed view on the results and keeping in mind the specific topic.

Is this right?

Can someone supply a more descriptive or formal definition?

How can we determine which texts really fit to discussion or conclusion? For example, describing the contribution in the discussion needs to somehow mention the problem, that instantly leads to the idea behind the whole thing and here we are: writing conclusion stuff into the discussion :/

Note: I'm working/writing in a STEM-discipline (would suspect that things are really different in STEM fields compared to, for example, philosophy?!)

• 1 I suggest you read some bachelors theses written by other students in your field at your institution, to get a clearer sense of what's going on. Theses are often available in either the school library or the department library, or your thesis advisor can provide examples of theses written by his former students. –  ff524 Commented Jun 23, 2016 at 17:26

There isn't a common definition. Furthermore, how you're supposed to define formally a humans' texts.

It depends on a field, country, journal, etc.... I'll give a more general and brief explanation.

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• Published: 30 August 2024

Perceptions of biodiversity loss among future decision-makers in 37 countries

• Matthias Winfried Kleespies   ORCID: orcid.org/0000-0002-8413-879X 1 ,
• Max Hahn-Klimroth   ORCID: orcid.org/0000-0002-3995-419X 1 &
• Paul Wilhelm Dierkes   ORCID: orcid.org/0000-0002-6046-6406 1  

npj Biodiversity volume  3 , Article number:  21 ( 2024 ) Cite this article

Metrics details

• Environmental social sciences
• Social sciences

The decline of global biodiversity is a major environmental issue with far-reaching consequences for humans and the Earth System. When it comes to biodiversity conservation, university students play an important role because, as future decision makers, they will have an important influence on how society deals with biodiversity loss. Until now, there has been no international research examining how these future decision-makers in society perceive the causes of biodiversity loss. Using a recent method customized for this data, we show here that there are eight distinct response types across the 37 countries studied that differ in their perceptions of the drivers of biodiversity loss. In one of these response types, climate change was underestimated, while in others pollution or invasive species were rated substantially lower compared to the other main drivers. The distribution of the eight response types varied between the countries. Our results demonstrate how future decision-makers around the world evaluate the drivers of biodiversity loss. Country-specific conditions and differences between the surveyed countries were revealed. The findings serve as a starting point for decision-makers around the world to tailor education programs and policy measurements to the circumstances in their countries.

Introduction

Due to the ongoing decline in global biodiversity, the world is facing a biodiversity crisis 1 , 2 . Predictions suggest that this decline will continue throughout the 21st century 3 . The current extinction rate is approximately 1000 times higher than the background rate of extinction due to human activities 4 and may increase further in the future 5 . Biodiversity degradation has now already reached an irreversible level with unforeseeable consequences 6 . By now, it can be assumed that a major sixth mass extinction in Earth’s history is currently underway 7 .

The five main drivers of the global decline in biodiversity are well known: Habitat loss, overexploitation, pollution, climate change, and invasive species 8 . Various studies, have assigned different levels of importance to these factors 9 , 10 , 11 , 12 . However, ranking these drivers is criticized because it can lead to conservation actions being misguided. Therefore, it is preferable to consider the drivers collectively 13 , as they are closely interrelated and potentially reinforce each other 14 , 15 .

Despite the problems and the resulting severe consequences being well known, not enough actions are currently being taken to halt the loss of biodiversity 16 . The gaps in action may be due to the lack of mainstreaming of biodiversity in public policy and limited awareness of biodiversity loss among policy makers and the public 5 , 17 . There are also deficits in the general population’s understanding of biodiversity: studies provide evidence that many adults and high school students are not familiar with the term biodiversity 18 , 19 . What is understood by biodiversity often differs between individuals 20 and the terms nature and biodiversity are often used interchangeably 21 . As a result, there is often a discrepancy between institutional definitions of biodiversity and what people understand by it 22 . These differences in perception of biodiversity can be shaped, for example, by the social or cultural group 23 .

Perceptions of environmental problems are an important factor that influences people’s behavior 24 : People are more willing to behave in a sustainable way if they perceive biodiversity loss as an imminent environmental problem 25 . Education can foster environmentally friendly behavior by increasing knowledge, perceptions, and concern about biodiversity 26 , 27 , 28 . Therefore, educational strategies play a crucial role, as they can make a decisive contribution to sustainability by raising awareness and knowledge about the biodiversity loss 29 , 30 , 31 .

Interest in investigating public perceptions of biodiversity loss has also increased in recent years. National survey instruments were developed and evaluated 32 , and there are now also approaches to studying behavioral intentions in different cultures in an international context 33 .

In the field of environmental conservation and biodiversity, higher education institutions play a crucial role: With their unbiased information, they influence the decisions of politicians and industry leaders 34 . They reach a wide audience 35 and contribute to sustainability through research, university policy, and public engagement 36 . Training students is also a particularly important task: Universities educate the decision-makers, leaders, intellectuals, and professionals of the future 35 , 37 , 38 . While it is possible to be a decision-maker in society without a university education, universities provide critical skills and knowledge that increase the likelihood of reaching such a position 39 .

For this reason, it is particularly relevant to investigate students’ perceptions of the critical issue of biodiversity loss. The perceptions of students in the environmental field are particularly important in this context, as it is likely that they will later work in the environmental field and will therefore be confronted with environmental problems such as biodiversity loss. Since biodiversity loss is a global problem, an international perspective is also very important in this context. While student perceptions for other concepts, such as the Sustainable Development Goals 40 or planetary boundaries 41 , have been studied in an international context, there is currently a lack of comparative studies examining how students in the environmental field worldwide assess the drivers of global biodiversity loss. Therefore, this study examines how environmental students in different countries worldwide evaluate the drivers of global biodiversity loss (main drivers) and whether they can distinguish them from drivers that have barely any impact on global biodiversity (minor drivers).

For this purpose, 4441 students in the field of environmental and sustainability studies in 37 countries were surveyed using an online questionnaire. For data analysis, a method recently developed for specifically analyzing such data sets is used 42 . It is based on unsupervised learning methods to identify patterns in the ratings, thus classifying the questionnaires of the 4441 respondents into higher-level response types. The proportion of each response type per country, referred to as the country’s ‘fingerprint’, will provide important information about the perception of the students in a country and yields to a natural measure of similarity between countries. The advantage of the methodological approach used is that the countries can be compared without requiring a structure simplification procedure (such as a PCA) beforehand 42 .

In addition to the education described at the beginning, it can also be assumed that students’ awareness of biodiversity loss may be influenced by the specific environmental challenges their country faces. To account for this, this study correlates the proportion of response types in the countries with various environmental and economic indicators that reflect a country’s biodiversity status and environmental health (CO 2 emissions per capita, biodiversity, wealth, environmental performance, and invasive species).

We hypothesize that students from wealthier countries with higher CO 2 emissions per capita will be less concerned about biodiversity loss and will, therefore, rate the main reasons for biodiversity loss as less severe compared to students from less wealthy countries with lower CO 2 emissions. Similar hypotheses have previously been proposed in the literature for general environmental attitudes 43 , 44 , 45 . Additionally, we assume that students from countries with higher biodiversity are more likely to appreciate it 46 and are, therefore, better able to recognize and assess the reasons for biodiversity loss. By linking the indicators with response types, we aim to understand how national contexts influence students’ perceptions of biodiversity loss.

A total of 4441 people from 37 countries were surveyed (Table 1 ). The analysis of the questionnaires using the data analysis method showed a separation into eight different response types, which can be clearly distinguished from each other.

In response type 1, all factors except climate change were considered to have a high influence on biodiversity decline. The minor drivers were rated as less important than the main drivers. Response type 2 shows a similar pattern, but instead of climate change, pollution was assigned a lower influence than the other main drivers. There was also good differentiation between the minor and the main drivers. In response type 3, all factors were rated as having little influence on biodiversity loss. The minor drivers were not differentiated from the main drivers in this type. In response type 4, three of the five main drivers were rated as moderately strong influencing factors (exploitation, invasive species, habitat loss), climate change and pollution were rated as slightly stronger drivers. There was a medium differentiation between main and minor drivers in this response type. In response type 5 all main drivers were considered as strong influencing factors, but the differentiation between main and minor drivers was medium. In response type 6, all main drivers were identified as very strong drivers for biodiversity loss. Invasive species, however, were assessed as slightly less important than the other main drivers. Additionally, there was a clear differentiation between minor and main drivers, as the minor drivers were rated considerably lower. The results of response type 7 and 8 were similar: The main drivers were identified as such, with the exception of invasive species: In response type 7 these were rated as a minor driver, in response type 8 as a moderate driver. In both response types, minor drivers were distinguished from main drivers (Fig. 1 ). The mean values and standard deviation for the main drivers and the difference between the main and minor drivers for the individual response types can be found in Table 2 .

Five indicates the assessment as a major reason. The values in the brackets show the discrimination between main and minor reasons.

The eight response types occurred in different distributions within the countries. The percentage distribution of each response type (the so-called fingerprint) for each country is shown Fig. 2A and Supplementary Table 1 . An alternative visualization can be found in Supplementary Fig. 1 . Using the Euclidean distance between fingerprints, similarities and differences between countries can be described (Fig. 2B ). Distribution and pairwise interaction of the single questionnaire items per country can be found in Supplementary Figs. 2 , 3 .

A Graphical representation of the fingerprints within each country. The larger the circle, the higher the proportion of this response type. Purple = type 1, yellow = type 2; orange = type 3; light green = type 4; dark green = type 5; blue = type 6; red = type 7; dark blue = type 8. B Euclidian distance between fingerprints. The country abbreviations are explained in Table 1 .

To find explanations for the fingerprints of the countries, the percentages of response types within a country were correlated with country-specific indicators, using the Spearman correlation. CO 2 (fossil CO 2 emissions of a country) shows a medium correlation with response type 1 and 4 and a medium negative correlation with response type 7. The Environmental Performance Index (EPI) a medium negative correlation with types 3 and 7. It is also moderately correlated with response types 2 and 6. The GBI (Global Biodiversity Index) correlates moderately with type 5. NIS (Number of invasive species) shows medium correlations with types 2 and 6 and a high negative correlation with type 7. The LPI (Legatum Prosperity Index) is moderate correlated with type 2 and type 5 and moderate negatively correlated with types 3 and 7 (Table 3 ).

The results of our study show that there are eight different assessment patterns that differ significantly in their perception of the drivers of biodiversity loss, and that these different views are present in varying distributions in the individual countries. Respondents belonging to response type 1 strongly underestimated climate change compared to the other main drivers of global biodiversity loss. However, current research provides strong evidence that climate change is one of the main drivers of global biodiversity loss 10 , 11 . In addition, it can currently be assumed that in the coming years the consequences for biodiversity due to climate change will increase significantly 47 , threatening particularly areas and species that are not currently affected 48 . Therefore, it is important that especially students in the environmental field are well informed about the consequences of climate change for biodiversity. This response type occurs the least, which is probably also due to the great importance of climate change as a global environmental problem. The high correlation with the fossil CO 2 emissions suggests that this group is particularly prevalent in regions where humans emit higher amounts of CO 2 into the atmosphere. Especially in countries that have reached a higher percentage of type 1, additional (societal and political) measures should be taken to make future decision makers aware of the problems and consequences of climate change.

Students of response type 2 rated pollution as significantly less influential than the other main drivers. However, pollution as an environmental problem is currently more relevant than ever before, even if there is still a lack of research on the consequences of chemical pollution 49 . More than 20% of deaths and illnesses are due to some form of pollution 50 and novel entities are already affecting the Earth system with unforeseeable consequences 51 . Especially the negative impact of pollution on biodiversity has been documented 10 . Countries with a higher proportion of this cluster should specifically educate their future decision-makers about the consequences of pollution and make them aware of the impact on global biodiversity. The correlations indicate that this type occurs more often in wealthier countries and countries that are already doing more for the health of their ecosystems. It should be noted at this point that the EPI and the wealth of a nation are highly correlated 52 . One explanation for this could be that pollution tends to play a smaller role in these countries compared to the other environmental problems and that this factor is therefore underestimated.

Response type 3 deserves attention, as the people in this group assume a low influence of all main factors and do not differentiate between main and minor drivers. While it is well known that it is sometimes difficult for students to separate main from minor drivers of biodiversity loss 53 , this group does not even perceive the main drivers as such. This is problematic because environmental behavior and concern about environmental problems are closely related: If people do not see environmental problems as such and do not worry about them, the likelihood that environmentally friendly actions will be performed decreases 54 , 55 . Knowledge about the existence of environmental problems is also a factor that can influence environmental behavior 56 , 57 .

This type is especially interesting because it directly contradicts the educational objectives of the institutions surveyed, where students of majors in the environmental field perceive the main drivers of biodiversity loss as less significant. As the response type only occurred in small numbers, it can be assumed that the curriculum or the research institutions at which the students studied were not responsible for its occurrence. Possible explanations could be for example personal or cultural influences. Cognitive biases such as optimism bias where students underestimate environmental problems could also have led to the low rating of the main drivers of biodiversity loss. Further research is needed to explain why this response type occurs.

The negative correlation with the EPI indicates that in countries that already act sustainably, this cluster occurs less frequently. The cluster also seems to occur less frequently in wealthy countries. The observation that there is a higher level of concern for environmental problems in wealthier countries has also been made in previous international studies 43 , 44 , 58 . One explanation for this is provided by Inglehart (1995), who describes that postmaterialist values lead to more positive attitudes towards environmental protection. These postmaterialist values are more common in affluent industrialized countries 45 . Therefore, there is a great need for action especially in wealthier countries and countries that have not yet focused so strongly on sustainable development. Particularly countries with a high number of response type 3 should address the concept of biodiversity loss and its drivers in more detail in university education.

Response types 4, 5, and 6 occur most frequently on average. There is a pattern of gradation between these three types: In type 4 the main drivers are rated as moderately to strongly important and there is little differentiation to the minor drivers. In type 5, all main drivers are rated as such, but there are still weaknesses in discrimination. In type 6, not only are the main drivers rated as such, but they are also very well differentiated from the minor drivers. Policy makers and decision makers in society should make their decisions based on the best evidence available 59 . Therefore, it would be desirable for all countries to increase the occurrence of type 6 through additional policy measures and the reduction of other response types, such as type 1 or 2. Since types 4 and 5 are already relatively similar to type 6, it would be desirable to promote the synthesis of types 4 to 5 to 6 through additional educational programs and outreach. Type 5 shows a medium correlation with the GBI, from which it can be concluded that, especially in countries with a high level of biodiversity, there is an increased concern for the loss of biodiversity and a wide variety of drivers are perceived as potential threat to biodiversity and assessed as main influencing factors. Type 6 shows an almost high correlation with the wealth indicator LPI. This means that this type is more likely to be found in wealthier countries. As previously explained, people from wealthy countries are more likely to show concern for environmental problems and more positive attitudes towards the environment 43 , 45 , 58 . This effect could also be a factor here.

In response types 7 and 8, all main drivers for the loss of global biodiversity are identified as such, with the exception of invasive species. It is well known that invasive species are often not recognized as a major problem 53 . Even stakeholders in contact with invasive species often have little knowledge 60 , cannot identify invasive species 61 or tolerate their presence 62 . When invasive species are not perceived as a problem, individuals often do not advocate their management 63 . Interestingly, the occurrence of this group is closely related to the presence of invasive species in a country: the correlation shows that the number of response type 7 decreases when there are many invasive species in a country. The phenomenon that people are more likely to perceive invasive species problems when they themselves are affected is well known 64 . However, it is likely that the number of invasive species and their distribution will increase worldwide in the following years 65 . As a result, more people will come into contact with invasive species and be affected by their impact. Therefore, it is already necessary to educate about the consequences of the spread of invasive species in order to raise awareness about their dangers and possible management processes.

Since in the case of types 7 and 8 invasive species were undervalued, especially in these countries, education about invasive species and their consequences should be provided.

When interpreting the results, however, it must be noted that the selected indices can only provide initial explanations for the distribution of the response types within the countries. Studies have shown that cultural differences, social factors 66 , 67 or regional factors 68 can have a decisive impact on individual perceptions and environmental concern. In order to investigate the influence of other factors on the perception of the drivers of biodiversity loss, further studies are needed in the future in which regional comparisons within a country can also be taken into account. In particular, the influence of local biodiversity loss drivers on attitudes towards global drivers should be investigated in the future.

The distribution of response types within countries, the so-called fingerprints, provide today’s decision-makers with important information on where action is needed in each country. In general, it can be seen as very positive that across all countries, response types 5 and 6 occur most frequently, as the main drivers of global biodiversity loss are also assessed as such there. Nevertheless, the occurrence of type 6 in particular should be significantly improved in all countries, while the response types with incorrect perceptions should be reduced. In this context, the fingerprints provide the decision makers with country-specific information on which educational priorities make sense for their countries (Fig. 2B ).

This study focused in particular on the perception of the global drivers of biodiversity loss. Given the interconnectedness of ecosystems and the global nature of many environmental issues, students also need a deep understanding of global biodiversity challenges. It is important that global future decision-makers are well informed about these, as knowledge and concern about environmental problems are important factors in influencing behavior and decisions 24 , 25 , 57 . However, it is important to notice that local and social factors can also play roles in influencing behaviors, and there are notable intercultural variations in the strength of these associations 67 , 69 .

The lack of urgent action to halt biodiversity loss is partly due to the incomplete understanding of the complex factors influencing biodiversity 70 . Policy makers are confronted with a difficult-to-structure variety of reasons for biodiversity loss, making it challenging to assess individual risks, combine them in an approach and implement a strategy to achieve sustainable development based on them. In this regard, there are current approaches to clarify these relationships in a multidimensional perspective on biodiversity to facilitate mainstreaming and support national decision-making. Soto-Navarro et al. 71 propose a Multidimensional Biodiversity Index to link biodiversity science to the political agenda that takes into account the diversity of values underlying nature-human relationships. In this respect, it will be important to include the perceptions of environmental students as future policy makers.

The method underlying the conducted analysis was recently developed to analyze exactly such datasets: questionnaire studies in different groups such that the different groups perception of the underlying concepts might vary 42 . Moreover, the used clustering approaches combined are themselves well studied in the mathematical literature and frequently applied to understand data in different applications in sciences 72 , 73 , 74 . A main challenge in the analysis of questionnaires are latent group variables (as the country) which might influence the given answers. By clustering questionnaires to response types, the latent group variable country vanishes implicitly and has no influence on the response type. This means that, instead of trying to analyze a typical questionnaire given the country, a country is described by its distribution of different response types. This approach naturally allows to incorporate higher-dimensional dependencies of the single questionnaire items in different groups without the need to satisfy assumptions of classical multi variate analyses such as the Bartlet-test or the Kaiser–Meyer–Olkin test 42 .

When interpreting the different response types, it is important to note that the five main drivers of global biodiversity loss are not always weighted equally and the official rankings differ between important panels: While the IPBES classifies habitat loss as the most important driver, followed by exploitation, climate change, pollution, and invasive species, the WWF ranks invasive species in third place. According to the IUCN, most extinctions are associated with invasive species 13 . Other scientific publications give varying degrees of importance to the impact of climate change on biodiversity: Some rank it second, after habitat loss 11 , while others rank climate change fourth, ahead of invasive species 8 . The UN environment program does not rank the five reasons at all 75 . These different assessments show that ranking the reasons is not expedient when evaluating the response types, especially since biodiversity decline is usually due to several drivers and their synergies and interactions 13 . Response types that accurately identify the five major drivers of biodiversity loss and distinguish them from minor drivers may be better able to prioritize actions and allocate resources effectively to prevent biodiversity loss. On the other hand, response types that underestimate certain threats or fail to distinguish between major and minor drivers may lead to misallocation of resources and ineffective or missing conservation strategies. Therefore, when interpreting the results of this study, the focus should be on whether the main drivers were recognized as such and could be differentiated from the minor drivers. A recent study of experts (scientists in the field of biodiversity and climate change) has shown that they have a very good assessment of the causes of biodiversity loss 76 .

Biodiversity fulfils many functional and cultural roles and is therefore of particular importance - at local, national and global levels. In order to develop and implement national biodiversity conservation targets, it is important that countries tailor these targets to their own circumstances and, in doing so, train policy makers who can clearly identify drivers for biodiversity loss based on scientific facts and develop staged, target-oriented measures 5 . The aim should be that students can recognize all five main drivers of global biodiversity loss and distinguish them from non-important drivers.

The results of our study provide the first empirical evidence of how environmental students around the world assess the drivers of biodiversity loss and show a positive picture among worldwide. The five most important drivers of biodiversity loss are uniformly recognized by a high percentage (response types 5 and 6). However, the results also show potential for improvement. The results for a small proportion (response type 3) show that the students in this group assume a low influence of all main factors and do not differentiate between main and minor drivers. In other groups, individual main drivers are considered less important, which could partly be due to national circumstances. These fingerprints of individual countries are a possible starting point to determine which drivers need to be educated about in more detail. This national perspective in the individual countries is particularly important, as local conditions must be considered. However, the global perspective should also be taken into account, as the problems for biodiversity are subject to constant change and are currently increasing significantly.

Furthermore, the results show that all countries should promote response type 6 (recognition of the main drivers and differentiation from the minor drivers). In addition, the correlations of the cluster distribution with the country-specific indices provide additional information on which global factors have an influence on the students’ perceptions. The results can help to better understand how biodiversity conservation through education could be optimized globally and which scientific findings on drivers of biodiversity loss should be integrated in a more targeted way in this regard, especially in the university education of future decision makers.

Although the study was conducted with great care, some limitations must be addressed. For example, the sample size in some countries was comparatively small. This could have led to the result not being representative of the environmental students in that country.

As only students in the environmental field were surveyed in the study, the results are not representative for all students or for the population of the countries. Further studies are needed to investigate how biodiversity loss is perceived by other groups.

The study was also conducted on a voluntary basis by e-mail. It is therefore possible that people who were interested in the topic were more likely to complete the questionnaire than those who were less interested. However, as this was the case in all countries, the results remain comparable.

Data collection procedure

An online questionnaire was used to conduct the survey. To ensure a high level of data protection and anonymity of the participants, the survey was completed using the survey platform evasys. This platform has high standards of data and information security and is ISO-27001 certified. To collect data, scientists (professors, lecturers, laboratory directors, department heads, or other department staff) in the countries surveyed were emailed and asked to share the survey link with their students. Only scientists in the environmental field (e.g., biology, ecology and conservation, environmental science) were contacted, as their students were the target group of the study. The countries surveyed were selected by the authors with the aim of surveying a large number of diverse countries on different continents. In addition to the link to the survey, a short explanatory text describing the purpose of the study, data protection, and the voluntary nature of participation was included in the email. As the persons were informed of the voluntary nature of the study and no data was requested that could enable the person to be identified, written consent was not obtained. After being informed of the voluntary nature of the study, participation was considered as informed consent. The survey was conducted in one of the official languages of the countries surveyed. The translations were carried out beforehand by native speakers and checked by another person. Data from students who stated in the questionnaire that they were not majoring in the environmental field, for example because they were taking a surveyed course as part of another degree program, were excluded from the analysis. Data from PhD or students exchange students who came from another country were also not included in the data analysis. The sample size per country is shown in Supplementary Table 2 . The minimum sample size was set at 25 students per country. Countries with a smaller sample were not used in the analysis. The survey took place between September 2020 and July 2021 and was approved by the ethics committee of the science didactic institutes and departments of the Goethe University Frankfurt am Main under approval number 15-WLSD-2104. If universities in which the survey was conducted required the additional approval of a local ethics committee, this was also obtained.

Measuring instrument

The battery of questions used for this study began with a brief definition of the term biodiversity: “Biodiversity (the diversity of species, the diversity of ecosystems, genetic diversity) is today undergoing massive global change. Please assess the extent to which the following reasons are responsible for the decline in global biodiversity.” The students were asked in closed-ended items to rate on a 5 points Likert-scale (minor impact to major impact) how much impact they thought the following drivers had on the decline of global biodiversity. Nine possible drivers for biodiversity loss were presented to the students. Among these were the five main drivers of global biodiversity decline (habitat loss, overexploitation, pollution, climate change, and invasive species) and four minor drivers that do not have a significant impact on global biodiversity (electromagnetic pollution, entering nature reserves, factory and vehicle noise and the internet). These minor drivers were chosen by the authors with the aim of selecting concepts that may sound plausible, but objectively have no or a negligible impact on global biodiversity. These minor drivers were used to investigate whether students can differentiate between significant global drivers of biodiversity loss and drivers that have no (global) impact on biodiversity loss. The goal was to determine whether the students really have an understanding of the drivers of biodiversity loss or whether everything is assessed as a problem without any reflection. Due to content-related concerns, the minor driver “entering nature reserves” was not included in the analysis and work continued with only 3 minor drivers. As these minor drivers equally have no significant effect on global biodiversity, their mean value was used for the analysis. In addition, demographic data such as age, gender, semester, university, and country were collected.

Methodological procedure

The analysis of the given dataset is, actually, a typical example of a “supervised learning task”. Each questionnaire consists of a number of variables, called “features”, and the dependent variable, the country, is known. In machine learning language, the country would be called “label”. Supervised learning means that a model needs to be found that enables us to predict the country from the features, thus the given answers. If a decent model is found, this indicates that the country can be estimated from the answers given in a questionnaire and conclusions can then be drawn by understanding the importance of single features or their influence on the label. But this approach cannot be applied to most of studies based on questionnaires. Indeed, as a relatively small number of possible answers can be given, the same features will lead to different labels quite frequently - this is a contradiction to very basic assumptions in supervised learning.

“Unsupervised learning tasks”, on the other hand, are designed to find patterns in a dataset, or to exploratively explain a dataset where labels are not present (or not known). Of course, standard tools from unsupervised learning theory can be applied to a collection of questionnaires, and questionnaires can be grouped (or “clustered”) by such algorithms. However, this does not allow to use the actual underlying information that different questionnaires stem from different countries. Recently, a methodological approach combining classical statistics and unsupervised learning was published in the data scientific community 42 . This method uses unsupervised learning techniques in the first step to cluster questionnaires. Second, for each country, a “fingerprint” is calculated which encodes the proportion of questionnaires of every cluster in the country. Third, unsupervised learning on those fingerprints is used to measure similarity between different clusters and classical statistical tools are applied. While a method paper appeared recently, we are not aware of any study in the field of environmental psychology that already applies the method to a cross-country study, and we believe that this approach itself is of interest to a larger community.

The data were processed and analyzed as proposed by ref. 42 . As previously described, there are 8 items represented by integers between 1 (low impact on global biodiversity) and 5 (high impact on global biodiversity) in each questionnaire. To keep the sample size as large as possible, incomplete questionnaires were imputed using scikit ‘s KNN imputation function. More specifically, the missing values of each sample were imputed by the average of the 8 nearest neighbors, where the closeness of two questionnaires was measured only by the features that neither was missing. This is a standard approach to impute data reliably 77 . In the second step, a feature engineering step, the 3 minor drivers were replaced by the difference between the means of the main and minor drivers, respectively. This adds an integer coordinate between −5 and 5 to each questionnaire, representing discriminability. Thus, each questionnaire is now represented by a 6th dimensional vector. Third, the data were slightly perturbed by Gaussian noise with mean zero and variance 0.001, independently in each coordinate, to ensure that there are no duplicates, which is a requirement for numerical stability of the clustering algorithms, while keeping each questionnaire in the dataset. In addition, this perturbation increases the stability of the final clustering against small changes in the original data, which is a desirable property 78 . The fourth step was to cluster the questionnaires according to their similarity. As the clustering algorithm, Ward’s clustering algorithm was chosen as suggested by ref. 42 . The algorithm was implemented by Python’s scikit library and identified eight clusters of questionnaires. Following the notation of the method paper, the central element (“the average questionnaire”) is called “response type”. More specifically, the algorithm takes the number of clusters as input, and the number of clusters was optimized for stability such that fewer clusters show significantly more variance within a cluster, but an additional cluster does not noticeably reduce variance.

One decision during the analysis is to determine the number of response types. Hahn-Klimroth et al. 42 suppose to use the “gap statistic” to determine the number of response types 42 . The main idea behind the gap statistic is to compare the given data to “randomly generated data without any structure”. Given a number of clusters, the corresponding gap value signifies how unlikely it is to find the cluster structure on the random data. Hence, the optimal number of clusters corresponds to either a local maximum or at least an “elbow” in the scree-plot which plots the gap value against the number of clusters. In the current study, the optimal number of clusters turns out to be 8.

Given the eight response types, a so-called “fingerprint” was calculated for each country as an 8-dimensional point such that the i-th coordinate represents the proportion of questionnaires of type i in the country. These fingerprints come with a natural interpretation of how similar two countries are, namely when their Euclidean distance is small. This similarity can be expressed visually as a “dendrogram”, sometimes called a “phylogenetic tree”. Again, the dendrogram connections are defined by Ward’s method. With the fingerprints, it is not only possible to measure the similarity between countries, but also to apply standard regression tools such as Spearman’s rank correlation, implemented in Python’s statistics library. More precisely, it is possible to measure the correlation between the proportion of type i questionnaires in a country and known indices. Here we call a correlation coefficient | r | > 0.3 a moderate correlation and say that the correlation is significantly different from zero if the corresponding p value is at most 0.05.

Five indices were selected to be used to explain the distribution of the types of questionnaires within different countries.

Fossil CO 2 emissions [CO 2 ] of a country from 2021: Countries’ CO 2 emissions from fossil emissions. This includes fossil fuel combustion, industrial processes and product use 79 .

Environmental Performance Index from 2022 [EPI]: This is an index that examines how environmentally sustainable a country is using 40 performance indicators 80 .

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Number of invasive species [NIS]: Number of reported invasive species in a country 83 .

Data Availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank all study participants and the more than three hundred researchers and universities that shared our questionnaires. This study was partly supported by the Opel-Zoo foundation professorship in zoo biology from the “von Opel Hessische Zoostiftung.”

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Conceptualization: M.W.K., P.W.D.; data collection: M.W.K.; methodology: M.H.K., M.W.K., P.W.D.; validation, formal analysis, investigation: M.W.K., M.H.K., P.W.D.; figures: P.W.D., M.H.K.; writing – original: M.W.K. M.H.K., P.W.D.; writing – review and editing: M.W.K., M.H.K., P.W.D., funding acquisition: P.W.D. All authors contributed to the article and approved the submitted version.

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Kleespies, M.W., Hahn-Klimroth, M. & Dierkes, P.W. Perceptions of biodiversity loss among future decision-makers in 37 countries. npj biodivers 3 , 21 (2024). https://doi.org/10.1038/s44185-024-00057-3

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary : A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasize weaknesses or failures.

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Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that…
• The study demonstrates a correlation between…
• This analysis supports the theory that…
• The data suggest that…

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualizing your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis…
• Contrary to the hypothesized association…
• The results contradict the claims of Smith (2022) that…
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is y .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of…
• The results do not fit with the theory that…
• The experiment provides a new insight into the relationship between…
• These results should be taken into account when considering how to…
• The data contribute a clearer understanding of…
• While previous research has focused on  x , these results demonstrate that y .

Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalizability is limited.
• If you encountered problems when gathering or analyzing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalizability of the results is limited by…
• The reliability of these data is impacted by…
• Due to the lack of data on x , the results cannot confirm…
• The methodological choices were constrained by…
• It is beyond the scope of this study to…

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish…
• Future studies should take into account…
• Avenues for future research include…

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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Effects of adaptive scaffolding on performance, cognitive load and engagement in game-based learning: a randomized controlled trial

• Tjitske J. E. Faber 1 , 2 ,
• Mary E. W. Dankbaar 2 ,
• Walter W. van den Broek 2 ,
• Laura J. Bruinink 3 ,
• Marije Hogeveen 3 &
• Jeroen J. G. van Merriënboer 4   na1  

BMC Medical Education volume  24 , Article number:  943 ( 2024 ) Cite this article

Metrics details

While game-based learning has demonstrated positive outcomes for some learners, its efficacy remains variable. Adaptive scaffolding may improve performance and self-regulation during training by optimizing cognitive load. Informed by cognitive load theory, this study investigates whether adaptive scaffolding based on interaction trace data influences learning performance, self-regulation, cognitive load, test performance, and engagement in a medical emergency game.

Sixty-two medical students from three Dutch universities played six game scenarios. They received either adaptive or nonadaptive scaffolding in a randomized double-blinded matched pairs yoked control design. During gameplay, we measured learning performance (accuracy, speed, systematicity), self-regulation (self-monitoring, help-seeking), and cognitive load. Test performance was assessed in a live scenario assessment at 2- and 6–12-week intervals. Engagement was measured after completing all game scenarios.

Surprisingly, the results unveiled no discernible differences between the groups experiencing adaptive and nonadaptive scaffolding. This finding is attributed to the unexpected alignment between the nonadaptive scaffolding and the needs of the participants in 64.9% of the scenarios, resulting in coincidentally tailored scaffolding. Exploratory analyses suggest that, compared to nontailored scaffolding, tailored scaffolding improved speed, reduced self-regulation, and lowered cognitive load. No differences in test performance or engagement were found.

Our results suggest adaptive scaffolding may enhance learning by optimizing cognitive load. These findings underscore the potential of adaptive scaffolding within GBL environments, cultivating a more tailored and effective learning experience. To leverage this potential effectively, researchers, educators, and developers are recommended to collaborate from the outset of designing adaptive GBL or computer-based simulation experiences. This collaborative approach facilitates the establishment of reliable performance indicators and enables the design of suitable, preferably real-time, scaffolding interventions. Future research should confirm the effects of adaptive scaffolding on self-regulation and learning, taking care to avoid unintended tailored scaffolding in the research design.

Trial registration

This study was preregistered with the Center for Open Science prior to data collection. The registry may be found at https://osf.io/7ztws/ .

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Introduction

Game-based learning (GBL) is a promising tool to support learning [ 1 , 2 , 3 ], but differences in effectiveness between learners and learner groups have been observed [ 4 , 5 , 6 ]. Adaptive scaffolding, meaning the automatic modulation of support measures based on players’ characteristics or behaviors, has been shown to improve learning outcomes [ 7 , 8 ], possibly through the optimization of cognitive load [ 3 , 9 , 10 ]. However, the number of studies into the effects of adaptive scaffolding on cognitive load and learning outcomes in GBL is low [ 9 , 10 , 11 ]. This study aims to investigate the effects of adaptive scaffolding in a medical emergency simulation game.

Theoretical background

• Cognitive load theory

To understand how the same instruction may have different effects on different learner groups, we turn to cognitive load theory (CLT [ 12 ]). This theory assumes a limited working memory and unlimited long-term memory holding cognitive schemas. Expertise comes from knowledge stored as schemas, and learning is described as the construction and automation of such schemas. To create schemas, new information must be ‘mindfully combined’ with other information or existing schemas. When working memory is overloaded, learning is impaired [ 13 ]. It follows that learners who have already developed relevant schemas will have more working memory resources to spare to deal with the task. These experienced learners may perform worse at a task when detailed instructions are provided (the “expertise reversal effect” [ 14 ]) because working memory becomes bogged down with attempts to cross-reference the instruction with existing schemas in long-term memory. Novice performers will benefit from instruction as the instruction may act as a central executive to organize the relevant information in working memory [ 3 ], freeing up cognitive load. Accordingly, instructional design should aim to 1) deliver learning activities, which present new information to be combined into more complex schemas (construction) or the opportunity to repeatedly apply existing schemas to new problems (automation), and 2) optimize cognitive load, to allow the learner to mindfully combine the new information.

In understanding how instruction influences cognitive load it is helpful to consider different types of cognitive load. Intrinsic cognitive load refers to the demands on working memory caused by the learning task itself. The more complex the learning task, or the lower the learner’s expertise, the higher the intrinsic cognitive load. Thus, the same learning task may cause a high cognitive load for a low-expertise learner but a low cognitive load for a high-expertise learner. Extraneous cognitive load is the load caused by demands on working memory caused by the instruction and the environment, rather than the information to be learned. Finally, germane cognitive load is the load required to deal with intrinsic cognitive load. It redistributes working memory resources to activities relevant to learning so that it promotes schema construction and automation. Techniques to measure cognitive load include direct measures such as subjective rating scales, including the popular 1-item Paas scale for mental effort [ 15 , 16 , 17 ], and dual-task methods (e.g. [ 18 ], Rojas, Haji [ 19 ], as well as indirect measures such as learning outcomes [ 20 ], physiological measures [ 21 ], and behavioral measures [ 22 ].

To optimize cognitive load in learning environments several principles have been described (e.g. [ 3 , 23 , 24 ]), including tailoring the instructional design to varying levels of learner expertise [ 9 ]. This may be accomplished through scaffolding, “the process whereby the support given to students is gradually reduced to counteract the adverse effects of excessive task complexity” [ 25 ]. Scaffolding is closely related to Vygotsky’s Zone of Proximal Development [ 26 ]. The additional support may take the form of supportive information (the provision of domain-general strategies to perform a task) or procedural information (specific information on how to complete routine aspects of a task) [ 27 ]. With scaffolding, the learner can perform more complex tasks or perform tasks more independently [ 27 , 28 , 29 ]. Scaffolding in general has been shown to improve learning outcomes in GBL [ 30 ]. However, superfluous scaffolding will increase extraneous cognitive load, for example by causing the learner to cross-reference provided instructions with information already present in their long-term memory, while insufficient or unnecessary scaffolding fails to lower the burden placed on the learner’s working memory, impeding the learning process in both situations [ 7 , 31 ]. Consequently, it is critical to provide contingent scaffolding: the right type and level of support at the appropriate time and rate.

Adaptive scaffolding

To ensure contingent scaffolding in computer-based learning environments such as digital GBL, adaptivity may be used: the automatic adjustment of a system to input from the player’s characteristics and choices [ 32 ]. While nonadaptive systems exacerbate differences between individuals, adaptations that are responsive to individual differences have been proposed to improve the equality and diversity of educational opportunities [ 33 ]. Adaptivity improves learning in hypermedia environments [ 34 ]. In GBL, several studies have investigated adaptivity, demonstrating promising effects on skill acquisition [ 35 , 36 , 37 ]. However, not all studies demonstrate favourable results [ 38 ].

Appropriate adaptive scaffolding should be triggered by indicators that identify the learner’s need for support. These indicators may be obtained before, during, or after a learning task. Examples include the learner's current knowledge level, cognitive load, stress measurements, performance assessments, or interaction traces documenting in-game events, choices, and behaviors, either separately or in combination [ 9 , 10 , 32 , 39 , 40 ]. Of these options, interaction traces in particular offer the advantage of unobtrusive and real-time collection, allowing for adaptations on a small timescale with short feedback loops. Examples of traces that can be used as indicators of performance in GBL include accuracy, speed, systematicity, and self-monitoring actions [ 41 , 42 , 43 , 44 ].

From the analysis presented above, we assume that adaptive scaffolding based on interaction traces is likely to positively influence cognitive load and improve learning task performance by freeing up working memory resources. In addition, this mechanism may improve the learner’s ability to self-regulate their learning, increase the transfer of learning, and influence learner engagement. We will discuss each of these below.

First, self-regulation of learning (SRL) refers to the modulation of affective, cognitive, and behavioral processes throughout a learning experience to reach the desired level of achievement [ 45 ]. Improved SRL can facilitate the learning of complex skills [ 46 , 47 , 48 , 49 , 50 , 51 ]. For example, students with higher developed SRL skills are better able to monitor their learning process during a task, recognize points of improvement, and use cognitive resources to support their learning, including help-seeking. Accordingly, SRL skills have been associated with improved confidence in learning, academic achievement, and success in clinical skills [ 47 , 49 , 52 , 53 ]. SRL is especially important in GBL, as the inherent openness of the learning environment requires students to take control of their learning [ 54 ]. Several authors have presented suggestions on how to integrate CLT and SRL theory, arguing that metacognitive and self-regulatory demands should be conceptualized as a form of working memory load that can add to the cognitive load related to task performance [ 55 , 56 , 57 ]. In this light, optimizing cognitive load through adaptive scaffolding allows more resources for SRL activities. Indeed, adaptive scaffolding has been shown to improve self-regulated learning in non-game environments [ 8 , 34 , 38 ] and it has been suggested that adaptive scaffolding can prompt students to consciously regulate their learning [ 7 ].

Second, we expect adaptive scaffolding to influence the transfer of learning: applying one’s prior knowledge or skill to novel tasks, contexts, or related materials [ 58 ]. In GBL transfer may not arise naturally, as learning takes place in an environment that can be notably different from real-life practice. However, well-designed simulations and games are favorable for situated learning, which is known to improve learning and transfer [ 59 ]. Transfer can be promoted by effortful learning conditions that trigger active and deep processing. Instructional strategies aiming to create these conditions include variability in practice and encouraging elaboration. From the CLT perspective, these strategies aim to increase germane cognitive load. Adaptive scaffolding can enhance this process by decreasing extraneous load when the learner is overloaded and increasing germane load in the case of cognitive underload. Research demonstrating these effects is scarce, with a notable paper by Basu, Biswas [ 60 ] reporting improved transfer of computational thinking skills in students who received adaptive scaffolding during training.

Third, scaffolding is likely to influence game engagement, meaning the experience of being fully involved in an activity. The ease of starting, playing, and progressing in the game are important factors that influence engagement [ 61 ]. Engagement improves learning and increases information retention [ 62 ]. Different effects of scaffolding on engagement in GBL have been reported. For example, Barzilai and Blau [ 63 ] found no effect on engagement, while others have demonstrated decreases in engagement (e.g. [ 63 , 64 , 65 ]. It should be noted that these findings relate to nonadaptive scaffolding. If this scaffolding fails to optimize cognitive load, it is likely that learners will lose motivation to continue working on a task [ 66 ] and be less engaged. On the contrary, adaptive scaffolding designed to optimize cognitive load may positively influence engagement, as observed in one study by Chen, Law and Huang [ 7 ].

Evaluating adaptive interventions

To specifically evaluate the effects of adaptive scaffolding, a yoked control research design may be applied [ 9 , 35 , 40 ]. In this design, matched participants are yoked (joined together) by receiving exactly the same treatment or interventions. From each pair, at random one participant is assigned to the adaptive condition and receives scaffolding tailored to their needs while their counterpart, assigned to the nonadaptive condition, is exposed to exactly the same scaffolding. Consequently, for the participant in the nonadaptive condition, the scaffolding is not intentionally adapted to their needs. The advantage of the yoked control design is that it allows the evaluation of the adaptation specifically. A difference in outcome may be attributed to the adaptation rather than the received support. However, depending on the heterogeneity in input used for the adaptive scaffolding, the nonadaptive scaffolding may coincidentally match the needs of the participant if their needs are the same as their counterpart adaptive in the adaptive condition. We will refer to the situation where participants in the nonadaptive condition coincidentally receive needed scaffolding as tailored scaffolding and the situation where they do not receive needed support as nontailored scaffolding.

Purpose of the study

In the present study, we will investigate the effects of adaptive scaffolding in a medical emergency simulation game. We hypothesize that adaptive scaffolding will result in lower cognitive load through a decrease in extraneous cognitive load (hypothesis 1). This decrease in cognitive load will free up working memory capacity, allowing the learner to better process the information in the learning task. This will result in improved learning task performance (hypothesis 2) during gameplay, measured as accuracy (hypothesis 2a), speed (hypothesis 2b), and systematicity (hypothesis 2c). Working memory capacity may also be used for self-regulatory activities, including (more) self-monitoring (hypothesis 3a) and (more) help-seeking (hypothesis 3b). We hypothesize that improved task performance and self-regulation will lead to more effective learning, measured as improved transfer test performance (hypothesis 4). Regarding engagement, we hypothesize that adaptive scaffolding will improve learner engagement (hypothesis 5). In the current study, we will compare the adaptive and nonadaptive scaffolding groups for each hypothesis, as well as discuss post hoc exploratory analyses regarding the influence of tailored scaffolding in the non-adaptive group.

To specifically evaluate the effects of adaptive scaffolding, we used a yoked control design as described above. Participants from the same university and either the same or immediately adjacent emergency care experience (0 cases, 1–2 cases, 3–5 cases) were matched in pairs. From each pair, one participant was randomly assigned to the adaptive scaffolding condition and the other to the nonadaptive condition. Ethical approval was provided by the Ethical Review Board of the Netherlands Association for Medical Education (dossier number 2021.3.5). Participants signed informed consent.

Participants

Demographics questionnaire.

A questionnaire was available regarding age, gender, study year, university of enrollment, and experience in emergency care. The questionnaire can be found in Appendix 1 .

E-learning and knowledge test

In emergency care, healthcare professionals are trained to adhere to the ABCDE approach. This is an internationally used method in which the acronym “ABCDE” guides healthcare providers to examine and treat patients in the following phases: Airway, Breathing, Circulation, Disability, and Exposure. Following the ABCDE structure ensures that the most life-threatening conditions are treated first. For example, in the ‘B’ phase, the healthcare provider focuses on the breathing by listening to the lungs, checking for blue discoloration of the skin (cyanosis), ordering a chest X-ray if necessary, and providing inhalation medication if needed.

To provide students with knowledge of the ABCDE approach, an e-learning module consisting of ± 90 screens of information, illustrations, interactive questions, and videos on emergency medicine and the ABCDE method was available online. To confirm sufficient knowledge, we used a validated knowledge test on the ABCDE approach developed using the Delphi method [ 67 ]. The test contained 29 multiple-choice items. We applied a pass rate of 60% to ensure an adequate knowledge level. The test could be re-taken an unlimited number of times.

The abcdeSIM simulation game

In the abcdeSIM simulation game, players must assess and treat a virtual patient in a simulated virtual emergency department [ 5 ]. For familiarization, a walk-through tutorial and a practice scenario are available. In the practice scenario, the patient is healthy and their condition does not deteriorate. The game contains different scenarios in which a patient presenting with a medical condition must be examined, diagnosed, and treated within 15 min. After completing a scenario, a score and feedback on interventions are displayed. The game score is generated by adding points for correct interventions and subtracting points for harmful interventions or overlooked necessary interventions. If all vital interventions are performed, a time bonus of one extra point per second remaining is awarded. The patient’s underlying condition determines the required interventions, which were established by a panel of content experts.

We used the practice scenario and six emergency scenarios in a fixed order as follows: practice, deep venous thrombosis, chronic obstructive pulmonary disease, gastrointestinal bleeding, acute myocardial infarction, sepsis caused by pneumonia, and anaphylactic shock. Complexity increases with subsequent scenarios, meaning the patient’s condition is more severe and requires more or more urgent interventions.

Scaffolding in the abcdeSIM game

To enable scaffolding in the abcdeSIM game, we implemented additional supportive information and procedural information as described by Faber, Dankbaar and van Merriënboer [ 68 ]. Both types of information can be toggled on and off separately, resulting in four possible scaffolding combinations: both supportive and procedural information provided, neither provided, only supportive information provided and only procedural information provided.

Supportive information explains to the learners how a learning domain is organized and how to approach problems in that domain. It supports the learner in developing general schemas and problem-solving approaches [ 27 ]. In the abcdeSIM game, supportive information consisted of an extended checklist designed to facilitate the construction of a cognitive schema representing the ABCDE approach. The original abcdeSIM game includes a basic checklist intended to help the learner structure their approach (Fig.  1 ), consisting of simple checkboxes for the general approach in each ABCDE phase. However, it does not specify which actions or measurements should be performed. The extended checklist prompts the player to evaluate specific items in each phase, such as looking at skin color, listening to the heart, and measuring blood pressure in the ‘C’ phase (Fig.  2 ).

The basic checklist in abcdeSIM

The extended checklist in abcdeSIM. A tab for general information (e.g. patient characteristics, presenting complaints) and one for each ABCDE phase prompt the player to examine specific features

Additional procedural information, meaning information provided in a just-in-time manner to complete routine aspects of tasks in the correct way [ 27 ], was implemented by showing a dialogue box upon tool selection. This dialogue box displays information on how and when to use the tool and appears every time the tool is selected until the player indicates to have read the information (Fig.  3 ).

Tool information is provided in a dialogue box when a tool is selected. A checkbox in the bottom left corner enables the player to indicate they have read the information and do not want it to be shown again

Adaptive scaffolding algorithm

Adaptive scaffolding was provided based on different measures of task performance in the previously played scenario. The algorithm for adaptive scaffolding is summarized in Fig.  4 . First, supportive information was provided when cognitive strategy use was deemed inadequate. We used systematicity in approach as a measure for adequate cognitive strategy use. Systematicity in approach, quantified using a Hidden Markov Model as described by Lee et al. [ 44 ], describes the level to which a player takes actions in the correct order. The model yields a score ranging from 0 to 1. A high systematicity indicates efficient knowledge-based cognitive strategies. To establish cutoff points for systematicity, we used data from a previous study with medical students playing the abcdeSIM game [ 41 ] ( M  = 0.71 and SD  = 0.11). If the systematicity in the first scenario was below 0.70, additional supportive information was activated in the form of the extended checklist described above. For each subsequent scenario, the extended checklist was deactivated when systematicity increased at least 0.05 or was above 0.95, and activated if systematicity decreased by 0.05 or more.

Algorithm for adaptive support

Secondly, procedural information about tool use was provided based on the frequency of inappropriate tool use, quantified by counting the number of times the in-game nurse issued a warning to the player during a scenario. We consider this an indicator of insufficient procedural knowledge regarding the correct application of the instruments available in the game. The presence of any warnings led to additional procedural scaffolding by activating tool information for the subsequent scenario. If no warnings occurred, tool information was deactivated in the subsequent scenario.

Outcome measures

Learning performance.

To operationalize learning performance, meaning the performance in the game, we measured the accuracy of clinical decision-making, speed, and systematicity. Accuracy represents applied domain knowledge and was measured as the game score minus the time bonus. Speed represents the strength of cognitive strategies used and was shown to distinguish between experts and novices by Lee et al. [ 44 ]. We measured speed both as the total time to scenario completion and as the relative time to complete three critical interventions: introducing oneself, attaching the vital functions monitor, and providing oxygen. To allow comparison between different scenarios, z -scores were calculated per scenario after checking the normality of distribution. Finally, systematicity represents the quality of cognitive strategies, or how to approach unfamiliar problems in this context. We operationalized systematicity as a measure of how well the player adhered to the ABCDE approach, calculated as described under ‘Adaptive scaffolding algorithm’ above. An overview of all included outcome measures is provided in Table  1 .

Cognitive load

Using an online questionnaire, we measured cognitive load for each game scenario using the Paas subjective rating scale [ 69 ] asking how much mental effort they invested in the task on a 1–9 scale, labeled from 1 = ‘very, very low mental effort’ to 9 = ‘very, very high mental effort’. According to Paas, Tuovinen [ 15 ], mental effort measured using this scale refers to “the aspect of cognitive load that is allocated to accommodate the demands imposed by the task” and as such may be considered to reflect the actual cognitive load.

Self-regulated learning

Interaction traces can offer insight into the use of specific SRL strategies in the game, such as monitoring, problem-solving, and decision-making processes [ 39 , 70 ]. To quantify the use of specific SRL strategies, we recorded the number of times participants accessed the checklist as a measure of monitoring and the number of telephone calls to a medical specialist or consultant as a measure of help-seeking .

Transfer test performance

To quantify transfer test performance, we used a live scenario-based skill assessment of the ABCDE approach at two time points (immediate assessment and delayed assessment). Four different scenarios were designed by content experts to be distinct from the game scenarios and checked for similar complexity. The scenarios concerned patients presenting with hypoglycemia, urosepsis, pneumothorax, and ruptured aneurysm of the abdominal aorta. In the immediate assessment, participants were presented with first the hypoglycemia and then the urosepsis scenario. In the delayed assessment, they were presented with first the pneumothorax and then the ruptured aneurysm of the abdominal aorta scenario. Expert clinicians experienced in simulation-based training and assessment facilitated the scenarios, playing the role of nurse, and assessed the participants’ performance. A basic manikin and practice crash cart were used. Vital functions, patient responses, and additional information were provided by the scenario assessor. The participants did not have to perform psychomotor skills, such as placing an iv or attaching the monitor, but did have to indicate when to apply these skills. The assessor rated performance using an assessment instrument adapted from Dankbaar et al. [ 71 ]. The rating consisted of a Competency Scale (6 items on the ABCDE method and diagnostics, rated on a 7-point scale from 1 = “very weak” to 7 = “excellent”) and a Global Performance Scale using a single 10-point scale to rate ‘independent functioning in caring for acutely ill patients in the Emergency Department’ (10 = “perfect”) as if the participant were a recently graduated physician. The assessment instruments are shown in Appendix 2 . To improve inter-rater reliability, the first author briefed all raters on the content of the scenarios, how to run the scenarios, how much support and guidance to provide during the assessment, and how to use the assessment instruments. Raters were blinded to the scaffolding conditions and the participant’s year of study. Feedback to the participant was provided only after the delayed assessment.

Game engagement

To measure game engagement, we used a questionnaire on participants’ experience adapted from Dankbaar, Stegers-Jager, Baarveld, Merrienboer, Norman, Rutten, et al. [ 5 ]. The questionnaire consists of 9 statements, including items such as: “I felt actively involved with the patient cases”, to be scored on a 5-point Likert scale (5 = fully agree). The questionnaire can be found in Appendix 3.

The overall study design is visualized in Fig.  5 . After enrollment, all participants were given access to the e-learning module and completed the demographics survey. Next, they were randomly divided into matched pairs. After passing the knowledge test, participants gained online access to the six game scenarios.

Study design

In the scenarios, scaffolding was provided as follows:

Adaptive scaffolding condition : in the first patient scenario, no scaffolding was provided. In subsequent scenarios, adaptive scaffolding was provided as described above.

Non-adaptive condition : the yoked participant received the same scaffolding as the participant they were matched to. Each training sequence was allocated only once to one participant in the non-adaptive condition.

During the game scenarios, learning performance outcome measures were collected automatically. After each game scenario, participants were requested to indicate the cognitive load for the scenario in the separate online cognitive load questionnaire. After the sixth and final game scenario, they completed the engagement questionnaire. Within two weeks of completing the final game scenario, participants performed the first live scenario-based skill assessment. Six to twelve weeks later, participants returned for a delayed live scenario-based skill assessment to measure long-term retention. They could not access the abcdeSIM game between the two assessments.

Confirmatory analysis

For each game session, we used a specialized JavaScript parser to extract accuracy, scenario completion time, systematicity in approach, self-monitoring, and help-seeking as described by Faber, Dankbaar, Kickert, van den Broek and van Merriënboer [ 41 ]. The analysis was performed in R [ 72 ] using the Rstudio software version 1.2.1335 [ 73 ]. Data were visually inspected for normality. Differences between the groups in participant characteristics were tested for significance using paired t -tests for continuous variables and Stuart-Maxwell tests for categorical variables. We calculated Cronbach’s alpha for the questionnaires and assessment instruments to evaluate reliability. Multilevel correlations between the learning performance outcome measures were calculated using the correlation package [ 74 ].

For hypotheses 1, 2 and 3, we used multilevel regression (also known as linear mixed) models, taking into account the number of scenarios already played by the student. This type of model has been widely used in longitudinal data where repeated measurements of the same participants are taken over the study period [ 75 ]. We fitted a partially crossed linear mixed model, using the lme4 package [ 76 ]. We fit separate models for the following outcome measures: cognitive load (H1), accuracy, time spent on the scenario, time to vital interventions, and systematicity (H2), and frequency of self-monitoring and help-seeking (H3). We used the outcome measures as criterion measures and random intercepts for pair and participant as random effects, to account for the dependent data structure. As fixed effects, we included the number of scenarios played and the scaffolding condition (adaptive vs. non-adaptive). To calculate p values, we performed likelihood ratio tests comparing the full model with the effects in question against the model without the effects in question. Model comparisons can be found in Supplementary Table A. To test hypotheses 4 and 5, we performed a paired t -test for transfer test performance and engagement outcomes per condition.

Exploratory analysis

Because tailored scaffolding occurred, meaning participants in the nonadaptive group received the same support as they would have in the adaptive group, we performed separate exploratory subgroup analyses within the nonadaptive group. For learning performance, SRL, and cognitive load, we included these outcome measures as criterion measures and random intercepts for participants as random effects in multilevel regression models. As fixed effects, we included the number of scenarios played and whether supportive and procedural information was tailored. Model comparisons for the tailored scaffolding models can be found in Supplementary Table B. For test performance and engagement, we calculated Pearson’s r to test for correlations between the number of scenarios played with tailored scaffolding and the outcome measure.

Baseline characteristics

Eighty-three medical students (age M  = 22.8 years , SD  = 1.8) participated in the study. One participant was excluded because they did not adhere to the study protocol. Sixty-nine participants completed all six game scenarios, resulting in 32 complete pairs. The other 19 participants either could not be matched or failed to complete the game scenarios.

Participants in the adaptive and nonadaptive groups were similar in age, gender, experience with emergency care, study year, and score on the knowledge test. Detailed characteristics are shown in Table  2 . Tailored scaffolding was observed in 64.9% of the game scenarios played in the nonadaptive group, with an average of 3.9 tailored scenarios per participant (range 2–6). One participant in the nonadaptive group received tailored scaffolding on all six scenarios.

Sixty-four students matched in 32 pairs played a total of 384 game scenarios. The cognitive load questionnaire was completed for 244 game sessions played by 49 participants in 30 pairs (64.7% of game sessions). For seven game scenarios data were not available for analysis due to technical problems, resulting in data available for analysis for 377 game sessions played by 63 participants in 32 pairs for learning performance (accuracy, scenario completion time, and systematicity) and self-regulated learning (help-seeking and monitoring). Time to vital interventions could not be calculated in 160 sessions because one or more vital actions had been omitted, resulting in 221 sessions available for this analysis. Thirty student pairs completed the initial transfer test and twenty-three the delayed transfer test.

Reliability of instruments

In contrast to previous research validating the knowledge test with acceptable internal consistency (Cronbach’s α = . 77, [ 67 ]) our data show poor consistency (α = 0.55, 95% CI [0.38—0.69]). Internal consistency for the assessment scores was excellent (α = 0.95, 95% CI [0.93—0.97]). There was a strong correlation between the score for the competency scale and the global performance scale, for both the immediate (r p  = 0.89, p  < 0.001) and the delayed assessment (r p  = 0.90, p  < 0.001).

A weak positive correlation was found between accuracy and total scenario time (r = 0.27, p  = 0.015). For cognitive load, a significant correlation was present with systematicity ( r  = -0.28, p  = 0.008) and total scenario time ( r  = 0.27, p  = 0.015) but not accuracy, self-monitoring or help-seeking. Self-monitoring significantly correlated with accuracy ( r  = 0.32, p  = 0.001) and total scenario time ( r  = 0.33, p  < 0.001) but not with systematicity or help-seeking. For help-seeking we found a positive correlation with both accuracy ( r  = 0.35, p  < 0.001) and total scenario time ( r  = 0.42, p  < 0.001).

Adaptive scaffolding condition did not significantly predict accuracy, time to vital interventions, and systematicity (Supplementary Table A). A trend toward longer scenario completion time was found for the adaptive scaffolding condition (β = 52.60 s, SE  = 27.71, 95% CI = [-1.89 – 107.09], Supplementary Table B).

The model including scaffolding condition could not significantly predict cognitive load compared with the model without scaffolding condition (χ 2  = 1.71, df  = 1, p  = 0.191, Supplementary Table A).

Adaptive scaffolding condition predicted a non-significant increase in the frequency of self-monitoring (β = 0.65, SE  = 0.35, 95% CI [-0.03 – 1.34], Supplementary Table B). Help-seeking was not predicted by scaffolding condition.

We did not find differences in initial test performance between the conditions on both competency and global performance (respectively t = 0.71, df  = 29, p  = 0.480 and t = 0.93, df  = 29, p  = 0.357). Similarly, there were no differences in test performance on the delayed test (respectively t = -0.97, df  = 22, p  = 0.341 and t = -0.96, df  = 21, p  = 0.350). Results are shown in Table 3 .

Engagement was not significantly different between the adaptive and nonadaptive groups ( t  = 0.75662, df  = 29, p  = 0.455).

Thirty-two students in the non-adaptive group played a total number of 192 game scenarios. One scenario was not available for analysis due to technical issues, resulting in data for 191 game scenarios available for accuracy, scenario completion time, systematicity, help-seeking and self-monitoring. For 111 scenarios the time to vital interventions could be calculated. For 110 sessions, cognitive load data were measured. In 168 scenarios (87.9%) tailored supportive information was provided, while tailored procedural information was provided in 142 scenarios (74%). Descriptive statistics by tailored supportive and procedural scaffolding is available in Supplementary Table G and Supplementary Table H.

Full model estimates can be found in Supplementary Table F. Tailored scaffolding significantly predicted scenario completion time (χ 2  = 8.12, df  = 2, p  = 0.017) and time to vital interventions (χ 2  = 8.54, df  = 2, p  = 0.014), but not accuracy and systematicity. As can be seen in Fig.  6 , scenario completion time decreased both with tailored supportive and procedural information (respectively β = -90.57, SE  = 35.35, 95% CI [-160.13 – -21.02] and β = -36.76, SE  = 27.10, 95% CI [-90.23 – 16.72]). Tailored supportive information strongly decreased time to vital interventions (β = -0.82, SE  = 0.32, 95% CI [-1.45 – -0.19]) while tailored procedural information had a weaker opposite effect, slowing the participants down (β = 0.32, SE  = 0.25, 95% CI [-0.18 – 0.83]).

Scenario completion time and tailored supportive information. Participants receiving tailored supportive information (blue) are faster, compared to participants receiving nontailored supportive information (red). Left: participants who do not need supportive information are faster to complete the scenario when information is not provided (blue) compared to those who are provided with supportive information (red). Right: when supportive information is indicated, providing the information results in a faster completion (blue) compared to not providing supportive information (red)

Including tailored scaffolding significantly improved the model to predict cognitive load (χ 2  = 14,85, df  = 6, p  = 0.021, Supplementary Table B). As shown in Fig.  5 , tailored supportive information significantly lowered cognitive load (respectively β = -0.88, SE  = 0.34, 95% CI [-1.56 – -0.20] Fig.  5 ) and a similar trend was observed for tailored procedural information (β = -0.51, SE  = 0.30, 95% CI [-1.10 – 0.09]). Full results of the model can be found in Supplementary Table C.

In the nonadaptive group, tailored scaffolding significantly predicted both self-monitoring and help-seeking (respectively χ 2  = 8.39, df  = 2, p  = 0.015 and χ 2  = 6.99, df  = 2, p  = 0.030). Tailored supportive information decreased the frequency of self-monitoring in the scenario in which it was provided (β = -0.85, SE  = 0.30, 95% CI [-1.44 – -0.26]) but had no large influence on help-seeking. In contrast, tailored procedural information did not influence self-monitoring significantly, but decreased help-seeking (β = -0.81, SE  = 0.31, 95% CI [-1.41 – -0.21]), as can be seen in Fig.  7 . Visual inspection (Figs.  8 and 9 ) suggests that the presence of the extended checklist increased monitoring behavior, regardless of the student’s needs. A post hoc multilevel model was constructed using self-monitoring as a criterion measure, random intercepts for participants, and as fixed effects the number of scenarios played, whether or not supportive and procedural information was available, and whether supportive and procedural information was tailored. This model was significantly different from the original model without the availability of supportive and procedural information (χ 2  = 45.49, df  = 2, p  < 0.001) and showed that the presence of the extended checklist significantly increased self-monitoring (β = 1.52, SE  = 0.21, 95% CI [1.11– 1.94]).

Cognitive load and tailored supportive information. Tailored supportive information (blue) results in a lower cognitive load compared with nontailored supportive information (red). Left: participants who do not need supportive information experience higher cognitive load when information is provided compared to those who are not provided with supportive information. Right: when supportive information is indicated, providing the information results in a lower cognitive load compared to not providing supportive information

Help-seeking actions. Participants for whom procedural information is tailored (blue) seek help less often compared to participants for whom procedural information is not tailored (red)

Self-monitoring behavior increases when supportive information is available, regardless of whether the information was tailored to the player’s behavior

Looking at the influence of tailored scaffolding in the nonadaptive group, competency and global performance were not significantly correlated with the number of scenarios with tailored scaffolding on the first assessment (respectively r p  = 0.07, p 0.694 and r p  = -0.01, p  = 0.944), and on the delayed assessment (r p  = -0.13, p  = 0.537 and r p  = -0.10, p  = 0.641).

The number of scenarios with tailored scaffolding did not correlate with engagement in the non-adaptive group (r p  = 0.04, p  = 0.838).

This study investigated the effects of adaptive scaffolding in a medical emergency simulation game on cognitive load, self-regulation, learning performance, transfer test performance, and engagement in a yoked control design. Apart from a trend towards more frequent self-monitoring and a longer time to scenario completion, we found no significant differences between the adaptive and nonadaptive groups. Unfortunately, the study’s power to detect differences between the groups was reduced because participants in the nonadaptive group also received scaffolding tailored to their needs in 64.9% of the game scenarios. This likely occurred because participants in both groups displayed comparable in-game behaviors. A similar limitation was mentioned by Salden, Paas and van Merriënboer [ 40 ], proposing that homogeneity in prior knowledge and expertise level explain this phenomenon, although they do not describe to what extent it occurred. Consequently, we performed exploratory analyses in the nonadaptive subgroup investigating the effects of tailored versus non-tailored scaffolding.

Regarding hypothesis 1, the results of the exploratory analyses suggest that tailored scaffolding lowered cognitive load. This effect can be explained by a reduction in extraneous load: students who do not require support do not need to cross-reference the information provided by the scaffolding with existing schemas, while students who lack knowledge on how to proceed are given scaffolding that can organize their learning [ 3 ].

Regarding learning performance (hypothesis 2), accuracy and systematicity could not be predicted and results regarding speed were mixed. While the adaptive group as a whole took longer to complete the scenarios compared with the nonadaptive group, in the nonadaptive group tailored scaffolding shortened the time to scenario completion. Time to vital interventions decreased with tailored supportive information but increased with tailored procedural information. In the literature, different effects from different types of scaffolds have been described (e.g., Wu and Looi [ 77 ]), with general prompts (similar to the supportive information used in this study) stimulating metacognitive activities, like self-monitoring, and specific prompts stimulating reflection on domain-related tasks and task-specific skills. Two explanations for our findings come to mind: first and foremost, reading the procedural information during task execution takes time by itself that immediately adds to the time to vital interventions. Secondly, the supportive information may stimulate learners to go back to the standard approach they have learned, helping them back on track.

Regarding self-monitoring (hypothesis 3a), in contrast to our findings comparing the adaptive and nonadaptive group, we found significantly reduced self-monitoring with tailored supportive information. This contrasts with previous research in non-game environments, where increases in self-regulation have been observed with adaptive scaffolding, either provided by human tutors [ 8 , 78 ] or through rule-based artificial intelligence [ 38 ]. Visual inspection of our data and further exploratory post hoc analysis suggested that the presence of supportive information in itself increased the frequency of self-monitoring, while tailored scaffolding had no significant effects on self-monitoring frequency. This finding should be confirmed in an appropriately powered study, possibly combining interaction trace measures of SRL with other measures such as systematic observations [ 79 ], think-aloud protocols [ 80 ], micro-analytic questions [ 81 ], or eye-tracking data [ 82 ].

Help-seeking (hypothesis 3b) decreased with tailored procedural support. Participants who did not require procedural support and did not receive it, as well as those who did require procedural support and did receive it, sought help less often. Possibly, the tailored procedural information accurately provided the information the participants needed; hence the provision of help did not add much. We found no improvements in test performance (hypothesis 4) and learner engagement (hypothesis 5) with tailored scaffolding, likely because the analyses in the nonadaptive group had insufficient power for these single-timepoint outcomes.

Our study had several strengths. We included students from three different universities in a double-blinded randomized study design. The study intervention provided multiple scenarios and we measured performance on several dimensions, including transfer test performance and retention. To our knowledge, this study is the first one to investigate the effects of adaptive scaffolding on learning performance as well as transfer performance in the context of game-based learning. However, our findings must be interpreted in light of the following limitations.

The first limitation regards the occurrence of coincidental tailored scaffolding in the nonadaptive group. As described above, this reduced the study’s power in comparing adaptive and non-adaptive support. To avoid this, future research should attempt to increase the differences between the adaptive and nonadaptive groups. For example, a different sampling strategy aiming to increase heterogeneity would decrease the incidence of adaptive scaffolding. This could involve recruiting more expert learners (e.g. residents) as well as novices, and not matching the pairs by experience. Other options include implementing a larger number of unique input variables for the adaptive algorithm or applying a different research design. This design could incorporate an adaptive group, a control group that does not receive any scaffolding, and another group receiving random scaffolding. The second limitation concerns the application of the adaptive scaffolding in the next scenario, instead of providing the scaffolding in the scenario where the need for scaffolding was identified. The timing of scaffolding influences its effects. For example, study material provided before play has proven more effective than the other way around [ 63 ]. This may have attenuated the effects of the scaffolding provided in our study.

A final limitation in our study was the use of a single-item measure for cognitive load. We chose the Paas single item mental effort scale because it is sensitive to small changes [ 83 , 84 ], easy to use and barely interrupts gameplay. However, we failed to/did not find significant correlations between cognitive load and self-regulatory activities although we expected increases in germane load. A differentiated cognitive load measure could provide more insight into how adaptive scaffolding increases germane load, meaning the active resources invested by the learner, compared with the load produced by the task itself, consisting of intrinsic and extraneous load. Apart from the previously mentioned 10-item scale by Leppink et al. [ 16 ], the 8-item questionnaire by Klepsch and Seufert [ 85 ] and the 15-item scale developed by Krieglstein et al. [ 86 ] appear promising instruments that distinguish between active and passive mental load. Challenges in using these questionnaires involve the larger number of items, interrupting game flow, as well as the limited reliability for measuring germane cognitive load and sensitivity to changes in item formulation that may be necessary for translation. As germane cognitive load is dependent on intrinsic cognitive load [ 87 , 88 ], adding physiological measures (see Ayres et al. [ 21 ]) to non-intrusively provide insight into intrinsic cognitive load may help clarify the role of scaffolding in relation to task complexity.

Conclusions

We could not find evidence to support our hypothesis of improved performance and lower cognitive load in adaptive scaffolding in game-based learning. Exploratory analyses do suggest a possible effect of tailored scaffolding. To further build on these findings, we offer three recommendations for research in adaptive scaffolding in game-based learning/GBL?. First, researchers should choose their research design and adaptive algorithm carefully to prevent coincidental adaptive scaffolding in the control group, as described above. Secondly, we recommend a more granular approach to measuring cognitive load, combining multi-item subjective measurements with physiological measurements. Finally, the specific effects of adaptive scaffolding should be investigated, including different effects for various types of adaptive scaffolding. Options include incorporating eye tracking, think-aloud protocols, or cued recall interviews to elucidate the mechanisms through which adaptive scaffolding influenced self-regulation in the game.

Tailored scaffolding shows promise as a technique to optimize cognitive load in GBL. When designing an adaptive GBL or computer-based simulation environment, we recommend that educators and developers work towards adaptive scaffolding as a team from the start. This will facilitate the establishment of reliable indicators of performance, self-regulation, and learning, as well as the design of appropriate, preferably real-time, scaffolding. For educators or developers who are unable to implement adaptive scaffolding, supportive information may be provided as a static scaffold to improve self-monitoring.

To conclude, this study into the effects of scaffolding in a medical emergency simulation game suggests that implementing tailored scaffolding in GBL may optimize cognitive load. Tailored supportive and procedural information have different effects on self-regulation and learning performance, necessitating further research into the effects of adaptive support as well as the design of well-calibrated algorithms. Considering the pivotal role of cognitive load in learning, these findings should inform instructional design both in game-based learning as well as other educational formats.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Abbreviations

Airway, breathing, circulation, disability, exposure: a mnemonic used in emergency medicine

• Game-based learning

Self-regulated learning or self-regulation of learning

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Acknowledgements

The authors would like to acknowledge all students who participated in the study. We are grateful to Femke Jongen for enabling data collection, and to Laurens Bisschops, Hella Borggreve, Sven Crama, Ineke Dekker, Els Jansen, and Dewa Westerman for performing assessments. We extend our thanks to Kim van den Bosch, Josepha Kuhn, Joost Jan Pannebakker, and Robin de Vries for assisting with the data collection. The authors thank Tin de Zeeuw, P.D.Eng., for creating software to process the game log data. Finally, we wish to acknowledge IJsfontein for creating adaptive support and VirtualMedSchool for implementing the support algorithm and providing access to the study version of the game.

This work was supported by the Netherlands Organization for Scientific Research (NWO) [project number 055.16.117].

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Jeroen J. G. van Merriënboer passed away November 15, 2023.

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Department of Anesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Huispostnummer 717, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands

Tjitske J. E. Faber

Erasmus MC, University Medical Center Rotterdam, Institute for Medical Education Research Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands

Tjitske J. E. Faber, Mary E. W. Dankbaar & Walter W. van den Broek

Department of Neonatology, Radboud University Medical Center, Radboud Institute for Health Sciences, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands

Laura J. Bruinink & Marije Hogeveen

School of Health Professions Education, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands

Jeroen J. G. van Merriënboer

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TF, MD, JvM and WvdB conceptualized the study and developed the study protocol. TF oversaw the investigations, conducted analyses, and wrote the main manuscript text. MD, WvdB and MH provided resources for data collection. TF, LB and MH performed data collection. JvM passed away on November 15th, 2023 and reviewed the first version of the manuscript. All remaining authors reviewed the final manuscript.

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Faber, T.J.E., Dankbaar, M.E.W., van den Broek, W.W. et al. Effects of adaptive scaffolding on performance, cognitive load and engagement in game-based learning: a randomized controlled trial. BMC Med Educ 24 , 943 (2024). https://doi.org/10.1186/s12909-024-05698-3

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The digital transformation of the manufacturing industry is closely linked to green credit policies, which jointly promote the development of the manufacturing industry towards a more environmentally friendly, efficient and sustainable development. Based on the research sample of China’s manufacturing A-share listed companies from 2008 to 2022, this paper uses the difference-in- differences (DID) method to analyze the impact of green credit policies on the digital transformation of heavily polluting enterprises. The results show that green credit policies significantly inhibit the digital transformation of heavily polluting enterprises. In terms of the adjustment mechanism, the R&D investment of enterprises and the financial background of senior executives have weakened the inhibitory effect of green credit policies on the digital transformation of heavily polluting enterprises. When the R&D investment is low, the inhibitory effect of the policy is more significant, but with the increase of R&D investment, the inhibitory effect of the policy gradually weakens, indicating that there is a substitution relationship between the two. Enterprises with senior financial expertise have a deeper understanding of financial feasibility and benefit analysis, and are more receptive to the high-risk investment of digital transformation, while their financial network resources can help broaden financing channels, reduce financing constraints, and further reduce the financial difficulty of digital transformation. In addition, the green credit policy has a stronger inhibitory effect on the digital transformation of non-state-owned enterprises and enterprises that do not hold bank shares. The conclusions of this paper are expected to provide some policy implications for the subsequent green credit policies in promoting the digital transformation of the manufacturing industry.

Citation: Zhou X, Yuan D, Geng Z (2024) Can green credit policies improve the digital transformation of heavily polluting enterprises: A quasi-natural experiment based on difference-in-differences. PLoS ONE 19(8): e0307722. https://doi.org/10.1371/journal.pone.0307722

Editor: Juan E. Trinidad-Segovia, University of Almeria: Universidad de Almeria, SPAIN

Received: March 29, 2024; Accepted: July 10, 2024; Published: August 29, 2024

Copyright: © 2024 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Today, as the world experiences rapid digital technology and rising environmental issues, the challenges facing businesses are more complex and urgent. The frontier of digital technology has not only changed the business landscape, but also redefined the position of enterprises in global competition. At the same time, global environmental problems, such as climate change and resource depletion, are threatening the sustainable development of enterprises. As a result, digital transformation and environmental protection, as the two major themes that will lead the future development, are gradually becoming the core elements shaping the corporate strategy. On the one hand, driven by the current wave of digitization, the manufacturing industry is undergoing a profound change, and digital transformation has become a strategic choice for enterprises in meeting future challenges and seizing opportunities, as a strategic initiative integrating advanced technology and innovative thinking, which is leading the manufacturing industry into a new era. The digitalization of the manufacturing industry is a product of internal and external environmental factors [ 1 ], which has a significant impact on the production process and management process of enterprises, which not only changes the traditional production methods, but also leads to a major transformation of enterprise management, marketing, product innovation and other levels. On the other hand, environmental protection is of great importance in today’s global economy. Manufacturing companies must comply with increasingly stringent environmental regulations and standards, which is not only a social responsibility for enterprises, but also an important way to achieve sustainable development. Environmental requirements are driving companies to innovate in technology and business models, and to explore new development opportunities. Through R&D and application of environmental protection technologies, enterprises can develop new products and services, and open up new markets, which can not only enable the manufacturing industry to meet the regulatory requirements of the green environment and social expectations, but also improve resource utilization efficiency, reduce operational risks, enhance market competitiveness, and explore innovation and development opportunities. Driven by digitalization and environmental protection, manufacturing enterprises should integrate environmental protection into their strategic planning, promote green transformation, and achieve a win-win situation of economic and environmental benefits.

As a financial instrument to encourage environmental initiatives, green credit policies provide a new source of funding for companies, and by rewarding environmental measures, they may play a key role in driving companies to participate more actively in the process of digital transformation. From the perspective of capital, the digital transformation of the manufacturing industry requires huge financial support, and this policy may provide enterprises with a way of sustainable financing, which is expected to alleviate the huge financial pressure they may face in the digital transformation. From the perspective of incentive mechanism, green credit policies may also become a driving force for enterprises to take the initiative to move towards digital transformation. With the emphasis of the government and society on environmental responsibility, enterprises are expected to obtain more favorable green credit terms by adopting digital technologies to improve the efficiency of production processes, reduce resource waste, and reduce environmental emissions.

However, while green credit policies may encourage firms to invest more in environmentally friendly technologies in the short term, their specific impact on long-term technological innovation by firms, especially digital transformation, which requires a significant investment of capital and time to bear fruit, remains an area of challenge and unanswered questions. Firstly, the complexity of digital transformation is reflected in the fact that it is not just a technological update, but a comprehensive organizational change. It involves adjustments to company culture, employee training, and the integration of new technologies on a number of levels, all of which take time and effort to change. While short-term green credit policies incentives may push companies to make initial investments in environmentally friendly technologies, to achieve digital transformation in the true sense of the word, companies need longer-term plans and commitments. Second, investments in digital transformation are not permanent and require a continuous injection of capital at different stages. The incentives provided by green credit policies in the short term may not be able to meet the funding needs for the entire transformation cycle. Enterprises may receive some financial support in the initial stage, but the scale and frequency of financial investment may gradually increase as the project deepens and expands. In summary, enterprises must explore the relationship between green finance and digital transformation more actively while pursuing sustainable development. Especially for those heavily polluting enterprises, digital transformation is not only a need to enhance their competitiveness, but also an urgent requirement to fulfill their social responsibility. In this context, whether green credit policy can become a catalyst to promote the accelerated digital transformation of heavily polluting enterprises is a question that deserves in-depth exploration.

Currently, academics have conducted a lot of research on manufacturing digital transformation and green credit policies respectively. On the one hand, studies have shown that digital transformation helps alleviate the information asymmetry between investors and enterprises, and between enterprises and product supply and demand markets, enabling investors to more accurately assess the value and potential of enterprises [ 2 ]. At the same time, the information asymmetry between enterprises and product supply and demand markets has also been alleviated to a certain extent, which leads to more efficient operation of the market. Enterprise digital transformation through digital technology, enterprises can more easily access to financing channels and financing information, improve the flexibility and efficiency of financing, can ease the enterprise financing constraints and reduce the cost of financing, which provides a wider range of financial support for the development and expansion of enterprises, and helps to promote the innovation and upgrading of the manufacturing industry [ 3 ]. In addition, digital transformation can significantly improve the innovation efficiency of enterprises, especially green innovation [ 4 ]. Digital knowledge management (KM) has a significant positive impact on technological innovation, mainly through absorptive capacity, adaptive capacity and innovative capacity [ 5 ]. Meanwhile, the digital transformation of high-tech industries has a positive effect on both technological innovation and achievement transformation [ 6 ]. On the other hand, in terms of green credit policies, the introduction of the Green Credit Guidelines in 2012 marked the official implementation of green credit policies, which is the core of China’s green credit policies system and an important perspective for many scholars to study [ 7 ]. However, most current studies show that the implementation effect of green credit policies is not satisfactory [ 8 ]. On the one hand, green credit policies will inhibit bank loans and long-term financing of heavy polluting enterprises through financing constraint theory and financing cost theory [ 9 ], and significantly reduce long-term bank loans of heavy polluting enterprises [ 10 ]. On the other hand, the green credit policy significantly inhibits the level of technological innovation of heavy polluters [ 11 ]. Maybe the policy will improve the sustainable development of enterprises in the short term, but it has no long-term effect [ 12 ] and promotes poorly managed zombie enterprises [ 13 ].

In summary, digital transformation and green credit policies are key factors in the process of high-quality development of the manufacturing industry in terms of technological innovation, transformation and upgrading. At present, there is a large number of literatures on the digital transformation of the manufacturing industry and green credit policies, but few studies combine the two to explore the relationship between green credit policies and the digital transformation of the manufacturing industry. Therefore, the marginal contributions of this paper may be: Firstly, the uniqueness of the research: This paper may be the first time to deeply explore the relationship between digital transformation in the manufacturing industry and green credit policies, combining these two key areas for research. This research is unique in that it connects the two key themes of digital transformation and environmental policies, filling a gap in the existing literature and providing a new research perspective for the academic community. Secondly, the importance of research to academia and practice: This paper fills the gap in the academic understanding of the relationship between digital transformation and green development in the manufacturing industry, and provides new ideas and methods for solving problems in this field. At the same time, the research results of this paper are of great significance for practice, which can provide useful reference suggestions for China’s green credit policies formulation and digital transformation of the manufacturing industry, promote the sustainable development of the manufacturing industry, and promote the development of China’s economy in a greener and more innovative direction. Thirdly, the theoretical and empirical contributions of the research: By exploring the impact mechanism of green credit policies on the digital transformation of the manufacturing industry, this paper expands the existing theoretical framework and provides new ideas and perspectives for theoretical research. Besides, this paper provides new empirical evidence based on empirical data, deepens the understanding of the mechanism of green credit policies in the process of digital transformation, and provides strong support for practice in related fields. Fourthly, the potential impact of the research: The research results of this paper are expected to have a profound impact on policy-making and practice. By proposing more effective green credit policies to promote the sustainable development of the manufacturing industry, this paper will help guide the government and enterprises to better formulate policies and strategies, promote the development of China’s manufacturing industry in a more digital, green and sustainable direction, and contribute to the realization of high-quality economic development.

Materials and methods

Theoretical analysis and research hypothesis.

Digital transformation typically requires large-scale capital investments to meet the costs of building information technology infrastructure, procuring innovative technologies and training employees. Such investment is necessary to drive enterprises to achieve business process optimization, improve productivity, expand market share, and enhance innovation. However, the introduction of “the Green Credit Guidelines” tends to exacerbate the financing constraints of heavy polluters [ 14 ], which in turn may hinder their active participation in digital transformation. Firstly, from a financial perspective, the financial requirements for digital transformation are usually large, including but not limited to the updating of IT infrastructure, the construction of big data analytics platforms, the introduction of artificial intelligence technologies and related training costs. Heavily polluting enterprises usually face higher environmental risks, and from the "principal-agent cost theory" and "modern contract theory", it can be seen that the principal-agent cost between the bank, as a creditor, and the enterprise will increase with the increase in project risks, including the costs of identification, monitoring, management and auditing. The cost of identification, monitoring, management and auditing, etc. will lead banks to adopt a more conservative strategy when considering costs and benefits. Meanwhile, according to the "risk compensation theory", in order to compensate for the potential environmental risks and possible default risks in the future, banks and financial institutions may require heavy polluting enterprises to pay higher financing costs or put forward more stringent lending conditions [ 15 ], such as higher interest rates or additional collateral, in order to obtain the price of risk-bearing compensation. This will lead to higher financing costs for heavy polluters [ 16 ]. This means a tighter financial situation for heavy polluting enterprises who are already under pressure to make environmental improvements, reducing their ability to invest in digital transformation.

Secondly, from the perspective of environmental protection and governance costs, the environmental regulatory effect brought about by “the Green Credit Guidelines” will increase the rectification efforts of heavy polluting enterprises to reduce pollution and emissions, which will to some extent reduce the priority and capital investment in digital transformation projects, thus slowing down the process of digital transformation. On the one hand, heavy polluting enterprises may need to reallocate resources in order to comply with the requirements of “the Green Credit Guidelines”, which means that enterprises may need to invest more R&D funds and human resources into the end-of-pollution treatment [ 17 ], reducing the allocation of funds and resources in digital transformation. This not only makes digital transformation projects significantly less economically attractive within enterprises, but also further inhibits the pace of transformation in the digital field for heavily polluting enterprises. On the other hand, the process of environmental protection management may involve changes such as re-planning of production lines, optimization of production processes, and upgrading of environmental protection facilities. This not only requires the investment of a large amount of resources, but also may lead to disruptions and uncertainties in the production process, bringing additional disturbances to the normal operation of the enterprise. Accordingly, the author proposes the following hypothesis:

H1: “The Green Credit Guidelines” significantly inhibit the digital transformation of heavy polluters.

The amount and quality of an enterprise’s R&D investment is directly related to its innovative capacity and future development potential. In today’s competitive market, firms that are able to increase their R&D investment on a sustained basis are usually more likely to be able to adapt to market changes and meet future challenges. High levels of R&D investment may play a key role in the digital transformation of heavily polluting firms in weakening the disincentive effect of green credit policies. Firstly, increased R&D investment can make firms more technologically innovative [ 18 ], accelerate their digital transformation process, and promote the adoption of more advanced digital technologies. This not only improves productivity and product quality, but also helps to reduce environmental emissions, thus meeting the expectations of green credit policies on environmental requirements. Technological innovation makes enterprises more flexible in digital transformation and allows them to better respond to the environmental standards of the policy, thus weakening the inhibiting effect of the policy on digital transformation. Secondly, a high level of R&D investment helps to improve the productivity of enterprises, and through the application of digital technology, enterprises are able to manage and utilize resources more effectively. Initiatives such as optimizing the supply chain and implementing smart manufacturing can reduce the waste of energy and raw materials and lessen the burden on the environment. This efficient use of resources makes it easier for firms to adapt to the environmental requirements of the policy, diminishing the constraints of green credit policies on digital transformation. Once again, increased investment in R&D demonstrates a firm’s commitment to innovation and sustainability. This strategic shift makes firms more inclined to adopt digital technologies to improve productivity and reduce environmental impacts. For heavily polluting firms, digital transformation is not only a technological upgrade, but also a necessary tool to comply with the SDGs. Investments in research and development lead companies towards a digitalization path that is consistent with green credit policies, slowing down the disincentive effect of the policies.

At the same time, investment in R&D is not only about technical aspects, but also includes investment in training and culture. By improving employees’ understanding and ability to apply digital technologies, companies can better adapt to the level of technology required for digital transformation and more easily comply with green credit policies. Building green awareness and a culture of sustainability can help firms better integrate digital technologies and mitigate the disincentive effect of policies on digital transformation. In addition, the relationship between R&D investment intensity and enterprise survivability shows a "U" non-linear relationship, i.e., R&D investment intensity can greatly improve the survivability of enterprises after reaching a certain level [ 19 ]. This implies that a moderate increase in R&D investment by enterprises in the process of digital transformation can improve their competitive position in the market while increasing their innovation ability, and mitigate the potential inhibitory effect of green credit policy on their digital transformation. Overall, corporate R&D investment may affect corporate digital transformation on multiple levels by driving technological innovation, improving productivity, promoting sustainable development, and fostering corporate culture. Efforts in all these areas can help weaken the inhibitory effect of green credit policies on the digital transformation of heavy polluting enterprises and enable them to carry out their digital transformation more smoothly. Accordingly, the author proposes the following research hypothesis:

H2: Firms’ R&D investment weakens the dampening effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting firms.

The digital transformation of an enterprise is inherently a high-risk business investment, as it involves huge capital investment in new technologies, systems, training and human resources, and such high-cost, resource-intensive investment poses a greater financial challenge to the enterprise. Importantly, digital transformation is usually characterized by greater uncertainty, with technology risk being a key consideration. The introduction of new technologies may lead to technology integration issues and additional costs, and the results and rewards of digital transformation usually take longer to become apparent. In addition, digital transformation requires a cultural shift within the organization, including employee training and adaptation to new ways of working, and this cultural change can be a complex and time-consuming process. Top echelon theory suggests that executives with a financial background typically have a greater tolerance for risk. This trait may have a significant impact in the project decision-making process, making executives more willing to take risks and thus increasing the likelihood that firms will choose riskier projects [ 20 ]. Because executives with a financial background typically have a deeper understanding of national policies, market volatility, and financial instruments, they may be more responsive to financial incentives in green credit policies. Compared to their counterparts with non-financial backgrounds, they may be able to utilize green credit resources more effectively in digital transformation and reduce the cost of corporate finance, which in turn will make them more confident in dealing with potential risks, and thus more willing to choose higher-risk investments in corporate projects, leading to a smooth digital technology transition.

At the same time, as executives with a financial background usually have profound financial knowledge and risk management skills, they have a deeper understanding of financial feasibility and benefit analysis. Therefore, they pay more attention to the financial feasibility of enterprise digital transformation in the decision-making process, which helps to establish a more efficient financial review and decision-making process [ 21 ], and can more accurately assess the positive impact of green credit policies on the enterprise’s financial position compared to others. This financial sensitivity makes them more capable of reducing potential uncertainties through rational financial strategies, and more able to increase enterprises’ acceptance of digital transformation, thus more actively promoting enterprises to follow the path of green transformation. Additionally, executives with financial background can use their own financial network resources to establish bank-enterprise contacts, broaden financing channels, reduce the information asymmetry between the enterprise and the bank, so that the enterprise can obtain more funds to alleviate the degree of enterprise financing constraints [ 22 ], and further reduce the financial difficulty of digital transformation. Based on the above analysis, the author puts forward the following research hypotheses:

H3: Executive financial background weakens the dampening effect of “The Green Credit Guidelines” on digital transformation of heavily polluting firms.

Research design

Model building..

“The Green Credit Guidelines” issued in 2012 provide a good quasi-natural experiment to study the impact of green credit policies on the digital transformation of manufacturing industries. According to the characteristics of this policy, heavily polluting firms should be affected firstly because they face higher environmental risks. Therefore, this paper includes heavily polluting enterprises in the experimental group and non-heavily polluting enterprises in the control group.

Data sources.

This paper takes listed companies in China’s manufacturing industry from 2008 to 2022 as the initial sample, and in order to improve the data quality and ensure the validity of the empirical analysis, the initial sample [ 23 ] is screened in accordance with the following criteria: (1) exclude companies with financial anomalies during the sample period, such as ST,* ST, and PT; (2) exclude companies that change their industries between heavy polluting enterprises and non- heavy polluting enterprises during the sample period; (3) exclude key data companies with serious missing data; (4) to avoid extreme values interfering with the findings of this paper, all continuous variables are subjected to the upper and lower 1% shrinkage. Through the above screening, the final sample includes 660 companies with a total of 9,345 observations, of which heavy polluting enterprises contain 220 companies and non- heavy polluting enterprises contain 440 companies; the data used in the study come from the CSMAR database, the iFind database, the Wind database, the National Bureau of Statistics, and MarkData.com , among others.

Variable selection.

Explained variable . The explained variable in this paper is the level of digital transformation of the enterprise, referring to the research results of Chen et al. (2021) [ 24 ]: Based on the statistics of 99 digital-related word frequencies in four dimensions: digital technology application, Internet business model, intelligent manufacturing, and modern information system, the digital transformation index of manufacturing enterprises was constructed by using text analysis method and expert scoring method. First, use text analytics to construct Digit_text variables. The first step is to collect the annual reports of listed companies in the manufacturing industry from 2008 to 2022 and convert them into text format, and then extract the text of the business analysis part through Python. The second step is to extract a certain number of samples of enterprises that have been successful in digital transformation through manual judgment. In the third step, the selected samples were processed by word segmentation and word frequency statistics to screen out high-frequency words related to digital transformation, which can be divided into four dimensions: digital technology application, Internet business model, intelligent manufacturing and modern information system, which suggests that we can construct the digital transformation index of enterprises from four dimensions (see Table 1 ). In the fourth step, based on the words formed in the third step, the text before and after is extracted from the total sample of listed companies, and the text combinations with high frequency are found. The fifth step is to supplement the keywords on the basis of the existing literature to form the final word segmentation dictionary. In the sixth step, based on the self-built word segmentation dictionary, the Jieba function is used to segment all samples, and the number of keyword disclosures is counted from four aspects: digital technology application, Internet business model, intelligent manufacturing and modern information system, so as to reflect the degree of transformation of the enterprise in all aspects. On this basis, the word frequency data was standardized, and the entropy method was used to determine the weight of each index, and finally the Digit text index was obtained.

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https://doi.org/10.1371/journal.pone.0307722.t001

Secondly, according to the description of the above keywords in the annual report, the number of disclosures, and the production and operation of the enterprise, the expert scoring method is used to judge the degree of digital transformation of each company. Specifically, if "digitalization" is the main investment direction of the enterprise in the year, or "digitalization" has been integrated into the main business of the enterprise (including production, operation, R&D, sales and management, etc.), the Digit_score variable is scored with 3 points; If the enterprise’s relevant investment involves "digitalization", but "digitalization" is not the main investment direction at this stage, or the company’s main business has not yet achieved deep integration with "digitalization", 2 points will be scored for the Digit_score variable; If the company only touches on a small aspect of "digitalization", or only mentions it in its development strategy and business plan, the Digit_score is set at 1; If there is no mention of "digitalization" in the company’s annual report, or if the annual report reflects that the company has not implemented digital transformation, the Digit_score score is 0.

Finally, on the basis of the obtained Digit_text and Digit_score, the final total index Digit is synthesized according to the weight of 50% each, so as to fully reflect the degree of digital transformation of manufacturing enterprises.

Explanatory variable . Based on the principle of DID model, the explanatory variable is the interaction “Post*Treat” (DID) of the policy dummy variable (Post) and the industry dummy variable (Treat). Since “The Green Credit Guidelines” came into effect on 24 February 2012, 2012 is used as a time dummy variable in this article, and for 2012 and subsequent years, Post is equal to 1, otherwise it is equal to 0. Referring to previous studies [ 25 ], this paper selects the Catalogue of Classified Management Industries for Environmental Protection Verification of Listed Companies issued by the Ministry of Environmental Protection in 2008 to identify heavy polluting enterprises, and if they belong to the heavy polluting industries mentioned in the 2008 Ministry of Environmental Protection Notice, they are defined as heavy polluting enterprises. Treat is a grouping dummy variable, with 1 for heavily polluting enterprises and 0 for non-heavily polluting enterprises.

Control variables . In order to avoid the estimation bias caused by omitted variables, this paper refers to the results of previous research [ 26 ], and selects the following variables as the control variables in the empirical process: (1) Size, (2) Lev, (3) ROE, (4) Tobin Q, (5) Liquid, (6) Cashflow, (7) Loss, (8) Dual.

In summary, the specific definitions of the variables are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0307722.t002

Descriptive statistics and analysis.

After the data in this paper were analyzed by descriptive statistics, the results are shown in Table 3 . It can be seen that the level of digital transformation (Digit) of China’s heavy polluting enterprises has a maximum value of 757, a minimum value of 0, and a standard deviation of 42.2630, indicating that there is a large difference in the degree of digital transformation among enterprises. The current ratio (Liquid) has a maximum value of 204.7421, a minimum value of 0.1065, and a standard deviation of 4.4500, indicating that there are also large differences in current ratios among firms. A higher liquidity ratio may indicate a more flexible operation and liquidity, while a lower liquidity ratio may indicate that a company is facing a shortage of funds or assets that cannot be liquidated quickly. Taken together, the descriptive statistics of both the level of digital transformation and the current ratio reveal that there are large differences in the operational management of China’s heavy polluters, and that these differences may have an important impact on the competitiveness and long-term development of the enterprises.

https://doi.org/10.1371/journal.pone.0307722.t003

Results and discussion

Benchmark regression.

Table 4 shows the empirical results of the impact of green credit policies on the digital transformation of heavy polluting enterprises, columns (1) and (2) are the cases of regression alone and adding control variables and fixing the year and individual, respectively. It can be concluded that the DID coefficients are all significantly negative, and the implementation of green credit policies significantly inhibits the digital transformation of heavily polluting enterprises, and hypothesis 1 is verified. The possible explanation is that at present, bank credit is the main financing method for most enterprises in China, and the introduction of the “The Green Credit Guidelines” will make banks more inclined to provide financial support to environmental protection enterprises, while heavy polluting enterprises are difficult to obtain financial support from banks due to serious environmental risks, which will eventually lead to a lack of funds for heavy polluting enterprises, thereby inhibiting a series of technological research and development activities such as digital transformation.

https://doi.org/10.1371/journal.pone.0307722.t004

Robustness check

Parallel trend test..

To ensure that the results of this paper are not affected by other policies and events, referring to the study of Zhang and Hu (2022) [ 27 ], the event study method is used to introduce multiple time dummy variables to construct early and lagged policy variables, and regressions are added while keeping the control variables unchanged. The results of the four coefficients before the promulgation of the policy and the coefficients in the last nine periods are shown in Table 5 , and the parallel trend test chart is shown in Fig 1 , the DID coefficients in the first four periods of the policy are not significant, while the coefficients in the nine periods after the promulgation of the policy are significantly negative. Therefore, the experimental group and the control group are comparable before the implementation of the policy in 2012, and the difference-in-difference regression model in this paper conforms to the parallel trend hypothesis, indicating that the original regression results are robust.

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https://doi.org/10.1371/journal.pone.0307722.t005

Placebo testing.

In order to ensure that the impact of “the Green Credit Guidelines” on the digital transformation of heavy polluting enterprises can truly reflect the effect of the policy without being influenced by other factors, drawing on the research results of Guo and Yin (2023) [ 17 ], an experimental group is randomly generated to simulate a situation that is not affected by the green credit policy, in order to compare the differences between the experimental group and the control group before the implementation of the policy. This is done by randomly, year-by-year and no-putback sampling 2008–2022 enterprises as the experimental group and the rest of the enterprises as the control group, and substituting them into model (1) for regression respectively. The probability density distribution of the coefficient estimates in the placebo test was obtained after 500 random draws and regression tests (see Fig 2 ). As can be seen from Fig 2 , the coefficient estimates from the placebo test are mainly distributed around zero, indicating that the original regression results are robust.

https://doi.org/10.1371/journal.pone.0307722.g002

In order to eliminate the endogeneity problem caused by potential selection bias, ensure the robustness of the research results, and improve the comparability of the experimental and control groups in terms of digital transformation, the propensity score matching method was used to conduct the robustness test, drawing on the study of Li (2023) [ 28 ]. All control variables in model (1) are selected as matching indicators in the propensity score matching model, and a Logit model is selected to estimate the propensity score, and then nearest-neighbor matching is used to re-match the experimental and control groups to ensure that there is no difference in other factors between the matched experimental and control groups except for the policy differences, and then subsequently re-estimate the model (1). Fig 3 shows that there is a significant difference between the experimental and control groups before matching, and Fig 4 shows the same trend after matching; the DID coefficient is still significantly negative at the 1% level from column (1) of Table 6 , which further validates the robustness of the findings of this paper.

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https://doi.org/10.1371/journal.pone.0307722.t006

Replacement of core explanatory variables.

This paper replaces the explanatory variables with reference to the research results of Wu et al. (2021) [ 29 ], which are statistically derived from a total of 76 digitization-related word frequencies in five dimensions, namely, artificial intelligence technology, big data technology, cloud computing technology, blockchain technology, and the use of digital technology. The regression results are shown in column (2) of Table 6 , and the coefficient of DID is still significantly negative, which again verifies the robustness of the findings of this paper.

Treatment of endogenous problems.

Lagging the core explanatory variables by one period helps to alleviate the endogeneity problem and improves the model’s ability to explain time correlation and long-term causality. In this paper, by regressing the core explanatory variable DID with one period lag, the results are shown in column (3) of Table 6 , and the DID coefficient of is still significantly negative, which indicates that the findings of this paper are still robust after taking into account the time lag effect.

Mechanism of action tests

Moderating effects of r&d investment..

The results of the moderating effect test for R&D inputs reported in column (1) of Table 7 show a significantly negative coefficient for R&D inputs and a significantly positive coefficient for the interaction term between R&D inputs and green credit policies. This reflects that overall, enterprise R&D investment weakens the inhibitory effect of the Guidelines on the digital transformation of heavily polluting enterprises, and the inhibitory effect exerted by the policy is more obvious when R&D investment is low, but the inhibitory effect brought about by the policy gradually decreases with the increase of enterprise R&D investment, which suggests that there is a significant substitution relationship between R&D investment and the “the Green Credit Guidelines” in influencing the digital transformation of heavily polluting enterprises, and Hypothesis 2 can be verified. Firstly, the reason why R&D investment can attenuate the inhibitory effect of the green credit policy on the digital transformation of heavy polluting enterprises may be that by strengthening R&D investment, enterprises are more likely to improve their technological level, adopt more environmentally friendly technologies and production methods, and receive more support under the green credit policy, thus alleviating the policy’s restriction on the funds required for digital transformation. At the same time, it may indicate that policymakers recognize and encourage firms that meet their environmental goals through independent R&D, as these firms are more likely to succeed in digital transformation; second, the disincentive effect of the policy is relatively more pronounced when R&D inputs are low, which may be due to the fact that the policy puts more emphasis on promoting the digital transformation of firms through financial support, whereas, in the case of low R&D inputs, firms may be more rely on the green credit support provided by the government; finally, the inhibitory effect brought by the policy gradually decreases as the R&D investment of enterprises increases, which suggests that there is an obvious substitution relationship between the R&D investment and the green credit policy in influencing the digital transformation of heavily polluting enterprises, and the possible explanation is that enterprises may prefer to choose to meet the environmental protection requirements through independent R&D, instead of overly relying on the government’s green credit policies.

https://doi.org/10.1371/journal.pone.0307722.t007

Moderating effects of executive financial background.

The test results of the moderating effect of executive financial background reported in column (2) of Table 7 show that the coefficient of executive financial background is negative but insignificant and the coefficient of its interaction term with green credit policies is significantly positive, which suggests that executive financial background weakens the inhibitory effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting firms, and Hypothesis 3 is verified. The possible reasons for this are as follows, the advantage of executive financial background lies in its greater tolerance to the high-risk nature of digital transformation. This is mainly reflected in the fact that financial expertise makes them more sensitive to the financial incentives of green credit policies and more effective in utilizing green credit resources, thus reducing the cost of corporate financing and increasing the acceptance of digital transformation as a high-risk investment. At the same time, gold executives with financial backgrounds have a deeper understanding of financial feasibility and benefit analysis, which reduces uncertainty through rational financial strategies and pushes enterprises to follow the green transformation path more actively. In addition, their financial contacts help broaden financing channels and reduce financing constraints, further easing the financial difficulty of digital transformation.

Heterogeneity analysis

Whether the enterprise is a state-owned enterprise..

In this paper, state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs) are regressed separately, and the results, as shown in columns (1) and (2) of Table 8 , indicate that the inhibitory effect of green credit policy on digital transformation is significantly higher for NSOEs than for SOEs. The possible explanations are as follows: firstly, SOEs and non-SOEs play different roles in China’s economic environment, with SOEs usually having easier access to government support and financing, while non-SOEs may be more dependent on indirect financing such as bank loans. Green credit policies may lead banks to be more prudent in approving loans and may place greater constraints on the financing needs of non-SOEs, thus inhibiting their digital transformation process; secondly, green credit policies usually require companies to take more steps in environmental compliance to qualify for loans. Non-state-owned enterprises may need more time and resources to meet these requirements, and thus may face greater resistance in the digital transformation process; finally, state-owned enterprises may enjoy market monopolies or more government support in some cases, which may make them more able to bear the costs of digital transformation. In contrast, non-State-owned enterprises may operate in more competitive market environments and be more vulnerable to green credit policies, as digital transformation requires greater capital investment.

https://doi.org/10.1371/journal.pone.0307722.t008

Whether the enterprise holds shares in the bank.

In this paper, firms holding bank shares and firms not holding bank shares are regressed separately. The results show that the inhibition effect of green credit policies on the digital transformation of non-state-owned enterprises is significantly higher than that of state-owned enterprises. The possible explanations are as follows: firstly, that the green credit policy may impose stricter environmental requirements on heavily polluting firms that do not hold bank shares by strengthening loan approval criteria, thereby limiting their access to funds for digital transformation. In contrast, firms that hold bank shares may be more likely to fulfill the conditions of green credit policies due to closer relationships with financial institutions such as banks. Secondly, firms with different shareholding structures may adopt different strategies in responding to green credit policies. Firms that do not hold bank shares may be more inclined to adopt a strategy of directly confronting environmental requirements by adapting their production and management practices to reduce environmental impacts, while relatively slowing down the pace of digital transformation. In contrast, firms with bank holdings may be more likely to obtain funding through green credits and thus invest more aggressively in digital transformation in order to adapt to environmental trends.

Conclusions and policy recommendations

Based on “the Green Credit Guidelines” issued in 2012, this paper selects China’s manufacturing A-share listed companies from 2008 to 2022 as the research sample. Based on the existing research, this paper uses the DID method to investigate and evaluate the impact of green credit policies on the digital transformation of heavily polluting enterprises. The research results show that: Firstly, the green credit policy, represented by “the Green Credit Guidelines”, has a significant inhibitory effect on the digital transformation of heavily polluting enterprises. Secondly, from the perspective of the adjustment mechanism, the R&D investment and the financial background of senior executives weaken the inhibition effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting enterprises, and when the R&D investment is low, the inhibitory effect of the policy is more obvious, but with the increase of enterprise R&D investment, the inhibitory effect of the policy gradually decreases, that is, the R&D investment of enterprises and the Guidelines have an obvious substitution relationship in affecting the digital transformation of heavily polluting enterprises. Thirdly, “the Green Credit Guidelines” has a significantly stronger inhibitory effect on the digital transformation of non-SOE heavy polluting enterprises than that of SOEs; it has a significant inhibitory effect on the digital transformation of heavy polluting enterprises that do not hold shares in a bank, while the effect on heavy polluting enterprises that hold shares in a bank is insignificant.

Based on the above conclusions, this paper puts forward the following policy recommendations from the perspectives of government and enterprises.

On the one hand, the government should launch a special digital transformation loan program to provide heavily polluting enterprises with preferential conditions such as low interest rates and extended repayment periods, so as to ensure that they receive adequate financial support in the process of digital transformation. At the same time, the government should encourage enterprises to increase R&D investment, such as through tax incentives and scientific research funding support, to encourage enterprises to increase R&D investment in the field of digitalization. Flexibly adjust the green credit conditions according to the level of enterprise R&D investment, and provide more flexible credit support for enterprises with low R&D investment. In addition, the government should implement differentiated green credit policies. Formulate differentiated policies according to the nature and shareholding of enterprises, and promote close cooperation between non-state-owned enterprises and non-bank shares and financial institutions to ensure that these enterprises can obtain favorable financial support. On the other hand, enterprises should actively apply for the government’s digital transformation loan program to take advantage of low interest rates and flexible repayment terms to reduce financing pressure and ensure the funds needed for digital upgrading. At the same time, enterprises should increase R&D investment and increase digital technology R&D and innovation activities to improve their competitiveness. In addition, enterprises should pay attention to financial literacy training such as digital literacy of senior executives, and encourage enterprises to participate in training programs to enhance their understanding and support for digital transformation. Finally, companies should optimize their financing structures and strengthen financial cooperation. Specifically, non-state-owned enterprises should explore flexible financing methods and establish close cooperation with financial institutions to obtain favorable financial support. Companies with bank stakes should optimize their financing structures and leverage their banking relationships to obtain better financing conditions to support digital transformation.

Supporting information

https://doi.org/10.1371/journal.pone.0307722.s001

Acknowledgments

We would like to express my sincere thanks to the editors and reviewers of the magazine. Thank you for your meticulous review of my manuscript and your valuable comments during your busy schedule. Your professional insights and constructive suggestions have greatly improved the quality and scientific of this paper, and provided important guidance for the refinement and improvement of this study. We have benefited greatly from your hard work and patience in the course of my research. We know that your valuable time and energy play an important role in advancing academic research and knowledge. Therefore, we would like to express my heartfelt respect and gratitude to you for your selfless dedication.

Thank you again for your attention and support to my manuscript, and look forward to your continued guidance and help in the future.

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