PONE-D-20-32904

Predicting Student Satisfaction Scores after Emergency Remote Learning in Higher Education during COVID-19, with Selected Machine Learning and Statistical Methods

PLOS ONE

Dear Dr. Indy Man Kit Ho,

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Zaher Mundher Yaseen

Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

Author should pay attention very much for the reviewers comments. The manuscript is required as extensive revision.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: * The title is very long and not informative.

* The abstract is misleading and does not convey the main purpose of the study.

* The logical stance of the research problem and its corresponding research objective is not clearly designated in the introduction section.

* Some grammatical mistakes and language logical flow were spotted in the manuscript, and therefore, it should be sent out for proofreading.

* Sections 2 to 4 can be merged under one main section (e.g., background or literature review).

* Collecting data through surveys are sometimes exposed to bias. How did you handle this issue? This needs to be clarified briefly in the manuscript.

* The discussion section has mainly tackled the statistical analysis of the findings, which sounds good. However, I would expect to discuss the results of your research and link them to the prior research. Why they are important?

* What are the theoretical contributions and practical implications of your research? This should be clearly specified in a separate section before the conclusion.

* The figures are not clear. Please provide high-resolution figures.

* The following relevant references were missed in your manuscript. You can refer to them and include them in your literature review section:

- Saa, A. A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques. Technology, Knowledge and Learning, 24(4), 567-598.

- Saa, A. A., Al-Emran, M., & Shaalan, K. (2019, March). Mining student information system records to predict students’ academic performance. In International conference on advanced machine learning technologies and applications (pp. 229-239). Springer, Cham.

Reviewer #2: Reviewer Comments to Author:

The study is quite interesting, and the outcomes of this paper are a valuable addition to literature (after major changes/revision). This study investigates the feature selection technique of random forest feature recursive elimination and checking overfitting issues with training and testing data sets and identified improved accuracy for all selected models. The author allegedly argues that The most important predictors to determine student satisfaction scores during emergency remote learning includes: 1) preference of face-to-face learning; 2) instructors' efforts to enhance students learning; 3) adjusted assessment was appropriate, and 4) online course activities were well delivered. This paper contributes to the literature by showing evidence of distance learning; social distancing practices are limited to different countries' data (The author should disclose country names). Overall, this article is not written clearly, but it should not be granted a publication in PLOS ONE. There are problems with English, i.e., Punctuations, grammar, and propositions. The paper needs copy editing. The introduction section, critical literature, and discussion of findings are major and significant concerns of this article. See comments below for details.

The below are major concerns and areas of improvement with suggestions.

1. Originality: The paper aims to investigate the feature selection technique of random forest feature recursive elimination, checking overfitting issues with training and testing data sets, and identifying improved accuracy for all selected models. The document did not contain significant new information appropriate to justify the publication. There is a need to revise the paper title, and the current title does not portray the paper's actual meaning.

2. Abstract: The abstract is not well written. There is a need to revise the abstract with explicit contents, i.e., the main issue, sampling, a statistical tool, methods, results, and implication. The author’s should provide a precisely and focused abstract.

• What is the practical and theoretical contribution of this article to literature?

• The sampling criteria, population, and unit of analysis for the selection of students are missing. The author should highlight the sampling criteria for more clarity to readers.

• As a suggestion for improvement, the author should not use the same Keywords as Paper Title. It is encouraged to used different keywords that are not in the Paper title. It will enhance paper searchability after the publication.

3. Introduction: The introduction section is not well written. There are ambiguous statements and no clarity in the introduction section.

• The introduction section is not started with a broader area and issue or in a global context. There is no synthesis in writing an introduction section.

• There is less debate on the targeted country problem, i.e., countries problems. It will add more value to the paper if the author explains some statistics and recent issues.

• In the Introduction section, the brief discussions of methods, tools, sampling, and findings are missing.

• An important question to answer is, “Why should PLOS ONE readers be interested in the results of this paper, which scrutinized only some countries' data?” The reason given is not supportive. Are the findings generalizable to other developing countries like India and Indonesia? The author(s) needs to improve underwritten motivation.

• Author(s) did not follow the academic paper's standard content, i.e., Introduction, Literature Review; follow as per journal requirements and guidelines.

• There is no roadmap at the end of the introduction that conveys the rest of the paper's structure.

4. Relationship to Literature: The paper incorporated major literature of learning culture, but the paper does not sufficiently cover recent research in the area. Helpful in this regard would be to include relevant research recently covered in top journals of similar scope. Further, work needs to be done to support the findings based on the current literature, as a recent theory in the area is directly counter to what was found.

• Author(s) did not use any underpinning theory to justify this research, i.e., resource dependency, agency theory etc. This is the major concern of this paper; the author(s) should highlight how this research contributes to the theory or the contribution of the theory. The authors did not discuss much with the use of developed theories in this area.

• There is a need to add more critical recent literature and based on theoretical argumentation.

5. Methodology: The author did not develop its argument from the appropriate theory and explored models previously studied in the same area. However, the data is focused on students. The article represents primary data from the survey technique as the sample and methodology and relevant to present the proposed theme's context. However, the empirical sample was fair enough to illustrate significant empirical results of this paper, and the methods employed are appropriate. In addition, there are major concerns with sampling design and data nature.

(i) What is the population of students (Sampling frame, total population, etc.)?

(ii) What are the criteria for the selection of firms for data collection? (sampling techniques, i.e., random. Cluster or judgmental sampling)?

(iii) Regarding the methodology, more details and justification of why Machine Learning analysis is used?

(iv) Author (s) did not define the data collection and sampling in a clear way.

6. Results: The analysis is provided to tie up with the findings.

• The author should provide complete statistical analysis, i.e., descriptive analysis, including skewness and kurtosis.

7. Discussion and findings: As results are clearly provided. However, there is a substantial discussion on results.

• Author(s) should discuss the limitations of this study and future research direction in a constructive way. Hence, authors should write in prices and in a constructive way under a subsection of discussion.

• Author(s) did not discuss the theoretical and practical contribution of this study. The author (s) should discuss this study's theoretical and practical contribution in the separate subsection under discussion for more clarity.

8- Conclusion: The author should provide concluding statements rather than repetitive statements in the conclusion portion.

9. Citation and End References

• The in-text citations and end list of references do not sufficiently correspond.

• The author did cite the latest literature relevant to the target issue in this paper. The reviewer found that the author has cited only eight (8) recently published papers in this article (Most of the cited articles ten years old). As a suggestion, the author must cite new articles (latest literature) to make holistic discussion and sturdy paper with high readability

11. Quality of Communication: The paper needs further proofreading. I have tried to read the paper constructively, but I felt it suffers from poor writing. I, therefore, request the author to pass the manuscript for professional proofreading. I suggest that a more careful investigation of prior literature can make this paper distinguishable. Linking this article with prior studies does not seem sufficient, which weakens the justification of incremental contributions.

Suggested Revisions

• The manuscript needs a professional proofreading.

• Abstract revision (complete / Major concern)

• Sampling and Data (Need to explain in detail / Major concern)

• Introduction Section (Please re-write holistically)

• There is a need to discuss the theoretical and practical contributions of this study in separate subsections.

• Conclusion section need to add and revise

I hope that the comments provided can help in this regard.

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Reviewer #1: No

Reviewer #2: No

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Predicting Student Satisfaction of Emergency Remote Learning in Higher Education during COVID-19 using Machine Learning Techniques

PONE-D-20-32904R1

Dear Dr. Ho,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Zaher Mundher Yaseen

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all the requested comments. Therefore, I recommend the manuscript for publication.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No