Automatic and Intelligent Recommendations to Support Students’ Self-Regulation

Abstract

In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student’s self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students’ performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student’s performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.