Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

Abstract

Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students’ learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students’ performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students’ academic performance. The results revealed that the dashboard significantly enhanced students’ learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students’ performance and assisted them in self-regulation

The Impact of Teacher Feedback on Non-cognitive Aspects of Student's Performance in Higher Education: A Review of Research

“…The results of the effect of e-feedback through artificial intelligence are of interest since its provision enhanced learners' self-regulated learning skills, motivation, critical thinking, and self-reflection skills (Porter & Grippa, 2020;Afzaal et al, 2021;Afzaal et al, 2023). Furthermore, the combination of providing feedback from teachers and fellow learners had significant positive effects on enhancing learners' motivation and self-confidence, reducing their anxiety and increasing their self-regulation (Nafi & Nazari, 2020;Weng et al, 2023;Turner, 2023), in enhancing collaboration and self-reflection (Zhang et al, 2014) as well as promoting critical thinking (Chakarvarti, 2022).…”

Artificial intelligence in university education: Systematic review

“…(Rezapour & Elmshaeuser, 2022) Universities should focus on providing face-to-face and synchronous classes supported with asynchronous components to facilitate healthy learning for students, as well as working on the nancial aspect of the students to avoid economic stress for them. (Afzaal et al, 2023) The algorithm-based approach (dashboard) provided advance information on students' progress and performance, which allowed planning and monitoring students' studies, providing information on learning achievements, self-regulated learning skills and positively affected students' performance. (Subirats et al, 2023) It was possible to determine student pro les, those who study continuously, at the end and low performance, with arti cial intelligence; in all of them different learning strategies applied before and after con nement were used, they were very relevant to change the habits of the students, proposing gami cation strategies for being motivating and of continuous use.…”