Re: Rebuttal Letter for Manuscript 535-1515 "A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction"
Reference is made to the above mentioned manuscript submitted to your journal and valuable reviewers comments that we received.
All the comments have been addressed and detailed on this rebuttal letter.
The corresponding modified manuscript in response to the reviewers’ comments is attached.
The reviewers’ comments have been addressed as follows:
1. The writing for this work is in need of proofing. There are numerous grammatical errors as well as latex formatting errors. This often stands as an obstacle in understanding what the writers are presenting.
The grammatical are corrected and latex formatting errors are addressed on the whole manuscript in line with the reviewer’s comments.
2. The introduction section discusses numerous topics that are not discussed in greater detail later in the writing (namely, gender disparities with respect to dropout)
Gender is discussed in subsections 4.6 and 5.1 of the paper, the feature importance experiment indicated the dropout rate association with gender. Student gender is considered a feature on the experiment, indicated on Table 3.
3. The paper initially presents itself as a review/survey but does not present much more than a typical literature review. Furthermore, the authors seems caught between attempting to present a standalone review/survey of prior work and presenting their own machine learning experiment(s). I'd suggest the authors stick to one or the other.
We agree with reviewer’s comment and present the manuscript as survey, we have rephrased all sections and describe the experimental part as case study.
4. An additional discussion of the methods in the ML experiments is needed. What does the dataset look like? Which features are present? Why were those specific ML algorithms selected for use? What was the train/test split?
In response to this comment we reconstruct the experimental framework section 4 and in line with reviewer’s comment. Furthermore, we present Table 3 that clearly describe features presented with additional data type details.
5. The authors only glance over their findings/results from both their review and their experiments. I think an additional discussion of either/both is needed.
We agree with reviewer’s comment and provide additional discussion on experiments in section 4.
6. The authors should make a better survey of Machine Learning Approaches for Student Dropout Prediction. e.g. Carlos Márquez-Vera, Alberto Cano, Cristóbal Romero, Amin Y. Noaman, Habib M. Fardoun, Sebastián Ventura: Early dropout prediction using data mining: a case study with high school students. Expert Systems 33(1): 107-124 (2016) as well as about the procedure for handling imbalance datasets e.g Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221-232.
We surveyed these two publications and incorporated in subsections 4.3 and 5.1 of our manuscript.
7. The experimental framework is not well designed. The results are based on only one dataset. Overfitting should be included.
We agree with reviewer that the study based only on one dataset, we elaborate the experimental procedure in subsection 4.2 and present the experiment as case study.
8. Section 3.3 is not really useful.
The results are based on only one dataset.
If the authors want to propose an algorithm should present it with pseudocode.
A statistical test should be used for the comparison of the examined methods.
Some information about the time efficiency of the proposed method should be included.
We in line with reviewer’s comment and included additional details in subsection 5.1. The survey doesn’t propose any algorithm but evaluating the best algorithms based on the conducted experiment. Therefore, we didn’t include pseudocode for any algorithm presented. The additional details provide more clarification on experimental procedure.
We look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.