Reviewer has chosen to be AnonymousOverall Impression:
UndecidedTechnical Quality of the paper:
Incomplete or inappropriateNovelty:
Limited noveltyData availability:
With exceptions that are admissible according to the data availability guidelines, all used and produced data are FAIR and openly available in established data repositoriesLength of the manuscript:
The length of this manuscript is about right
Summary of paper in a few sentences:
The paper discusses the state of student dropout in developing countries and several performance metrics used by researchers to evaluate machine learning techniques in the context of education with experimental examples
Reasons to accept:
In this article, a review of how machine-learning techniques have been used in the fight against dropouts is presented for the purpose of providing a stepping-stone for students, researchers and developers who aspire to apply the techniques
Reasons to reject:
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.
The experimental framework is not well designed. The results are based on only one dataset. Overfitting should be included.
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.