Development of an Early Warning System to Support Educational Planning Process by Identifying At-Risk Students

Abstract

The development of data analysis techniques and intelligent systems has had a considerable impact on education, and has seen the emergence of the field of educational data mining (EDM). The Early Warning System (EWS) has been of great use in predicting at-risk students or analyzing learners' performance. Our project concerns the development of an early warning system that takes into account a number of socio-cultural, structural and educational factors that have a direct impact on a student's decision to drop out of school. We have worked on an original database dedicated to this issue, which reflects our approach of seeking exhaustiveness and precision in the choice of dropout indicators. The model we built performed very well, particularly with the K-Nearest Neighbor (KNN) algorithm, with an accuracy rate of over 99.5% for the training set and over 99.3% for the test set. The results are visualized using a Django application we developed for this purpose, and we show how this can be useful for educational planning.

Feature selection techniques and classification algorithms for student performance classification: a review

“…Working on the development of an early warning system, involving various socio-cultural, structural, and educational factors that directly influence a student's choice to discontinue their education [63], several classification algorithms, namely SVM, RF, stochastic gradient descent (SGD) and KNN, were employed as predictive models for the dataset. According to their findings, the KNN algorithm demonstrated superior performance by achieving the lowest losses mean absolute error (MAE) and root mean square error (RMSE) and consequently securing the highest accuracy score (R2).…”