Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

TLDR

Formative feedback is known to enhance learning, yet its implementation is hampered by the time teachers must spend analysing student behaviour and delivering tailored feedback, and prior learning‑analytics studies have predicted performance without explaining the underlying causes. This study introduces a learning‑analytics and explainable‑machine‑learning framework that automatically generates data‑driven feedback and actionable recommendations to support students’ self‑regulation and improve course performance. Using LMS data from a university course, the framework explains prediction root causes and delivers them through a dashboard that provides real‑time, data‑driven feedback and intelligent action suggestions. Evaluation of the dashboard showed it enhanced students’ learning outcomes, facilitated self‑regulation, and boosted motivation, while also revealing its practical limitations.

Abstract

Formative feedback has long been recognised as an effective tool for student learning, and researchers have investigated the subject for decades. However, the actual implementation of formative feedback practices is associated with significant challenges because it is highly time-consuming for teachers to analyse students' behaviours and to formulate and deliver effective feedback and action recommendations to support students' regulation of learning. This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent feedback and action recommendations that support student's self-regulation in a data-driven manner, aiming to improve their performance in courses. Prior studies within the field of learning analytics have predicted students' performance and have used the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and by automatically providing data-driven intelligent recommendations for action. Based on the proposed explainable machine learning-based approach, a dashboard that provides data-driven feedback and intelligent course action recommendations to students is developed, tested and evaluated. Based on such an evaluation, we identify and discuss the utility and limitations of the developed dashboard. According to the findings of the conducted evaluation, the dashboard improved students' learning outcomes, assisted them in self-regulation and had a positive effect on their motivation.