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.

Keywords

• Early warning system
• machine learning
• KNN
• educational planning
• dropout