Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models

• Autores: Balqis Albreiki
• Localización: International Journal of Educational Technology in Higher Education, ISSN 2365-9440, Nº. 19, 2022
• Idioma: inglés
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• Resumen

  • Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students’ academic performance early using advanced computational techniques. Several research eforts have focused on developing systems that can predict student performance. However, there is a need for a solution that can predict student performance and identify the factors that directly infuence it. This paper aims to develop a model that accurately identifes students who are at risk of low performance, while also delineating the factors that contribute to this phenomenon. The model employs explainable machine learning (ML) techniques to delineate the factors that are associated with low performance and integrates rule-based model risk fags with the developed prediction system to improve the accuracy of performance predictions. This helps low-performing students to improve their academic metrics by implementing remedial actions that address the factors of concern. The model suggests proper remedial actions by mapping the students’ performance in each identifed checkpoint with the course learning outcomes (CLOs) and topics taught in the course. The list of possible actions is mapped to this checkpoint. The developed model can accurately distinguish students at risk (total grade < 70%) from students with good performance. The Area under the ROC Curve (AUC ROC) of binary classifcation model fed with four checkpoints reached 1.0.

    Proposed framework may aid the student to perform better, increase the institution’s efectiveness and improve their reputations and rankings