The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

Round 1

Reviewer 1 Report

My concerns are as follows.

1.      The study analyzes a data set of more than 550 students from 100 different schools.  It’s about 5 or 6 students from each school.  In this sense the sample size is very small.  It’s important to know how did the students are selected.

2.      Page 2-3 may be shortened.  You may just say which factors or measurements are included in the study.  No need to repeatedly say how important they are before the data analysis as the numerical analysis should show it.

3.      The manuscript is long.  Many statements are repeated multiple times, e.g. it mentioned “18 features” are used many times.  Authors can also make Section 2 brief. 

Author Response

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

---------------------------------------------------------------------------------------------------------------------

Response File

We would like to thank the editors (lead and guest editors) and reviewers for their valuable suggestions. We have addressed all of the questions raised by the reviewers and made all the necessary changes suggested by the reviewers. All of the concerns of the reviewer such as; related work, references, grammar, and formatting have been addressed properly. The changes that we made in the revised manuscript are highlighted as well with a different color.

Reviewer 1:

My concerns are as follows.

1. The study analyzes a data set of more than 550 students from 100 different schools.  It’s about 5 or 6 students from each school.  In this sense the sample size is very small.  It’s important to know how did the students are selected.

Answer: Thank you very much for your valuable comment. Actually, it is 5550, not 550. It is a typing mistake. We visited different schools and collected the data from the mature students of each school, who possess good thinking and understanding.

 

2. Page 2-3 may be shortened.  You may just say which factors or measurements are included in the study.  No need to repeatedly say how important they are before the data analysis as the numerical analysis should show it.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have shortened pages 2–3 and made all the necessary changes in the revised manuscript. Please see the revised manuscript.

 

3. The manuscript is long.  Many statements are repeated multiple times, e.g. it mentioned “18 features” are used many times.  Authors can also make Section 2 brief. 

Answer: Thank you very much for your valuable comment. According to your suggestion, we have removed the repeated sentences from the manuscript. Further, we made all the necessary changes in Section 2 of the revised manuscript. Please see the revised manuscript.

 

 

Reviewer 2 Report

The study is interesting, however, it can be improved further. I request the authors consider the following:

1. Improve the figures. Most of the figures are not clear.

2. Please justify the reasons for using the mentioned ML techniques.

3. Please provide references for these packages " The main packages we used for the implementation of ML models and 417
graphics generation are Scikit-Learn (Sklearn), Pandas, Numpy, Seaborn, Matplotlib, etc. "

4. SMOTE is only mentioned in the conclusion. The way how SOMOTE has been applied should be explained in the experimental section. Please add some details for that.

5. A separate experimental setup section should be added to explain how all models have been set up, their parameters etc.

6. There is no comparison with other works in this space. Please provide some comparisons with other works.

 

Author Response

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

----------------------------------------------------------------------------------------------------------------

Response File

We would like to thank the editors (lead and guest editors) and reviewers for their valuable suggestions. We have addressed all of the questions raised by the reviewers and made all the necessary changes suggested by the reviewers. All of the concerns of the reviewer such as; related work, references, grammar, and formatting have been addressed properly. The changes that we made in the revised manuscript are highlighted as well with a different color.

Reviewer 2:

The study is interesting; however, it can be improved further. I request the authors consider the following:

1. Improve the figures. Most of the figures are not clear.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have improved the quality of the figures and used good-quality figures in the revised manuscript. Please see the revised manuscript.

 

2. Please justify the reasons for using the mentioned ML techniques.

Answer: Thank you very much for your valuable comment. Actually, our main concern of using multiple classifiers is to identify which model best suits the nature of the problem. The mentioned ML techniques are vital in different fields of life, and we found them beneficial in our study as well.

3. Please provide references for these packages "The main packages we used for the implementation of ML models and 417 graphics generation are Scikit-Learn (Sklearn), Pandas, Numpy, Seaborn, Matplotlib, etc."

Answer: Thank you very much for your valuable comment. According to your suggestion, we have provided the references (Reference 36-40) for the mentioned packages in the revised manuscript. Please see Section 4.1 in the revised manuscript.

 

4. SMOTE is only mentioned in the conclusion. The way how SOMOTE has been applied should be explained in the experimental section. Please add some details for that.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have added a brief explanation of SMOTE in Section 4 of the revised manuscript.  Please see the first paragraph of Section 4 in the revised manuscript.

 

5. A separate experimental setup section should be added to explain how all models have been set up, their parameters etc.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have added a separate Section 4.1 in Section 4 of the revised manuscript.  Please see Section 4.1 in the revised manuscript.

 

6. There is no comparison with other works in this space. Please provide some comparisons with other works.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have provided a comparison of our work with the others work. Please see Table 12 in Section 4.5 of the revised manuscript.

 

Reviewer 3 Report

This study investigates the most critical socioeconomic factors and their impact on the educational achievements and performance of a student. This study mainly focuses on how the SES of a student affects his or her performance. Overall, the article is quite interesting. However, the article has big problems in literature review and innovation. I only agree to the publication of this paper after the authors have revised it according to my comments. Here are some specific suggestions:

1. The authors claim that they proposed a novel approach using different ML algorithms to identify the impact of a student's SES on his or her educational achievements. The proposed system worked on full features and on selected features as well, using the two important feature it m selection, algorithm FCBF and relief. Furthermore, the feature selectors present the significant and correlated features to the ML models and play an important role in enhancing the performance of the models. However, the ML-based methods in this paper are very traditional or maturely used , where is the innovation of this article?

2. The main method of the article is to use ML-based methods to solve problems, but the literature review lacks discussions on advanced ML or DL, and some recent work about ML or DL needs to be analyzed, such as A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition, An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition, A parallel hybrid neural network with integration of spatial and temporal features for remaining useful Angioinfectious pred dual sparse self-attention network for remaining useful life prediction

3. Figure 1 of the article is not beautiful enough, the proportion of the picture is not coordinated, and the clarity of the picture is low, which needs to be corrected

4.Various ML algorithms are used in this study such as DT, KNN, RF, NB, XGBoost, LR, etc. These methods are very traditional, the authors only use the method and lack of algorithm improvement, and the theoretical innovation needs to be in-depth Research

5. The formula on page 12 lacks a formula number

6. Figure 2-4 is not clear enough and needs to be corrected

7. Tables 6 and 8 have carried out parameter adjustment experiments for KNN. Why didn't the authors conduct detailed parameter adjustment experiments on other methods and list the tables

8. From the current point of view, the innovation of the article cannot be seen, and the existing methods are used. It is recommended that the authors discuss in depth

Author Response

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

---------------------------------------------------------------------------------------------------------------------

Response File

We would like to thank the editors (lead and guest editors) and reviewers for their valuable suggestions. We have addressed all of the questions raised by the reviewers and made all the necessary changes suggested by the reviewers. All of the concerns of the reviewer such as; related work, references, grammar, and formatting have been addressed properly. The changes that we made in the revised manuscript are highlighted as well with a different color.

 

Reviewer 3:

This study investigates the most critical socioeconomic factors and their impact on the educational achievements and performance of a student. This study mainly focuses on how the SES of a student affects his or her performance. Overall, the article is quite interesting. However, the article has big problems in literature review and innovation. I only agree to the publication of this paper after the authors have revised it according to my comments. Here are some specific suggestions:

1. The authors claim that they proposed a novel approach using different ML algorithms to identify the impact of a student's SES on his or her educational achievements. The proposed system worked on full features and on selected features as well, using the two important feature selection algorithms, FCBF and relief. Furthermore, the feature selectors present the significant and correlated features to the ML models and play an important role in enhancing the performance of the models. However, the ML-based methods in this paper are very traditional or maturely used, where is the innovation of this article?

Answer: Thank you very much for your valuable comment. You are correct that different academics have used these models. We have tried it in a different way by developing our own dataset and then using the fine-tuned versions of the mentioned ML models along with the feature extractors (Relief and FCBF). According to our knowledge, this is a new and prominent approach. The experimental results show the importance of the proposed system.

2. The main method of the article is to use ML-based methods to solve problems, but the literature review lacks discussions on advanced ML or DL, and some recent work about ML or DL needs to be analyzed, such as “A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition”, “An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition”, “A parallel hybrid neural network with integration of spatial and temporal features for remaining useful Angioinfectious pred dual sparse self-attention network for remaining useful life prediction”.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have added more literature based on ML and DL techniques in Section 2 of the revised manuscript. Further, we have discussed the mentioned papers (Reference 24-26) as well in the revised manuscript. Please see Section 2 of the revised manuscript.

 

3. Figure 1 of the article is not beautiful enough, the proportion of the picture is not coordinated, and the clarity of the picture is low, which needs to be corrected.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have improved the quality and contents of Figure 1. Please see Figure 1 in Section 3 of the revised manuscript.

4. Various ML algorithms are used in this study such as DT, KNN, RF, NB, XGBoost, LR, etc. These methods are very traditional, the authors only use the method and lack of algorithm improvement, and the theoretical innovation needs to be in-depth Research.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have deeply discussed the main theme of the paper in more detail. Further, we have added all of the necessary information to the revised manuscript. Please see the revised manuscript.

 

5. The formula on page 12 lacks a formula number.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have given a number to each formula in the revised manuscript. Please see Section 3.2.5 of the revised manuscript.

 

6. Figure 2-4 is not clear enough and needs to be corrected.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have improved the quality of the figures and used good-quality figures in the revised manuscript. Please see the revised manuscript.

 

7. Tables 6 and 8 have carried out parameter adjustment experiments for KNN. Why didn't the authors conduct detailed parameter adjustment experiments on other methods and list the tables.

Answer: Thank you very much for your valuable comment. Actually, the case of KNN is different from the other models, where the value of K has a great impact on the performance of the model. KNN shows more variance in performance when the value of K is changed as compared to the other models. Therefore, we carried out multiple experiments for different values of K. Further, we have added all of the necessary parameter settings for other models in the performance tables in Section 4 of the revised manuscript.

 

Round 2

Reviewer 1 Report

Comments on revision

For my concern 1 in the original review, the authors’ response says “collected the data from the mature students of each school”.  It is not clear to me how can it be done.  The revision (page 11) is not clear either.  It’s important to provide detail information and procedure on how the students are selected in the paper, such that readers can see what the population of interest is.

Author Response

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

Response File

We would like to thank the editors (lead and guest editors) and reviewers for their valuable suggestions. We have addressed all of the questions raised by the reviewers and made all the necessary changes suggested by the reviewers. All of the concerns of the reviewer such as; related work, references, grammar, and formatting have been addressed properly. The changes that we made in the revised manuscript are highlighted as well with a different color.

Reviewer 1:

For my concern 1 in the original review, the authors’ response says “collected the data from the mature students of each school”. It is not clear to me how can it be done. The revision (page 11) is not clear either.  It’s important to provide detail information and procedure on how the students are selected in the paper, such that readers can see what the population of interest is.

Answer: Thank you very much for your valuable comment. According to your suggestion, we have mentioned the details of student selection criteria and dataset creation in Section 3.1 (Dataset acquisition) on page 6 of the revised manuscript. Further, we have removed the duplicate and unrelated information from Section 4 (Page 11) of the revised manuscript. Kindly see pages 6 and 11 of the revised manuscript. Thank you once again for your valuable suggestions and for helping us improve the quality of our manuscript.

Author Response File: Author Response.pdf

Reviewer 2 Report

Thank you for making the suggested changes. All the best with the paper.

Author Response

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

-----------------------------------------------------------------------------------------

Response File

We would like to thank the editors (lead and guest editors) and reviewers for their valuable suggestions. We have addressed all the questions raised by the reviewers and made all the necessary changes suggested by the reviewers. All of the concerns of the reviewer such as related work, references, grammar, and formatting have been addressed properly. The changes that we made in the revised manuscript are highlighted as well with a different color.

Reviewer-2: Many Thanks for your time and valuable suggestions.

Author Response File: Author Response.pdf

Reviewer 3 Report

Thanks to the authors' revision. I accept its publication. 

Author Response

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

---------------------------------------------------------------------------------------------------------------------

Response File

We would like to thank the editors (lead and guest editors) and reviewers for their valuable suggestions. We have addressed all the questions raised by the reviewers and made all the necessary changes suggested by the reviewers. All of the concerns of the reviewer such as related work, references, grammar, and formatting have been addressed properly. The changes that we made in the revised manuscript are highlighted as well with a different color.

Reviewer-3: Many Thanks for your time and valuable suggestions.

Author Response File: Author Response.pdf