Explainable AI for data-driven feedback and intelligent action recommendations to support students self-regulation
Open accessAbstract
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.
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Afzaal et al. (2021) showed that explanations of student performance predictions can be used to recommend actions to students in self-regulated learning.
“For instance, Afzaal et al. (2021) showed that explanations of student performance predictions can be used to recommend actions to students in self-regulated learning”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Afzaal et al., 2021
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“For instance, Afzaal et al. [2021] showed that explanations of student performance predictions can be used to recommend actions to students in self-regulated learning, Albreiki [2022] studied how explanations from ML can be used to recommend remedial actions to low performing students with the goal of improving learni…”
BackgroundA Comprehensive Systematic Review of AI-Driven Approaches to Self-Directed Learning
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- Authors
- Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., & Weegar, R.
- Affiliations
- Stockholm University (SE)
- Corresponding author
- Afzaal, Muhammad
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2021-11-12
- Volume
- 4
- Pages
- 723447-723447
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC8636130
- OpenAlex
- W3213654060
- Cited by
- 125
- References
- 55
- Citations by year
- 2026: 30 · 2025: 31 · 2024: 34 · 2023: 17 · 2022: 13
- FWCI
- 13.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vetenskapsrådet
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- explainable machine learning, formative feedback, student self-regulation, learning analytics, learning management system data, student performance prediction, learning analytics dashboards, student motivation, learning outcomes
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