Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation
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 teach...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:4df5d3a547954061aca9e00fc533c2832021-11-12T06:28:21ZExplainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation2624-821210.3389/frai.2021.723447https://doaj.org/article/4df5d3a547954061aca9e00fc533c2832021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frai.2021.723447/fullhttps://doaj.org/toc/2624-8212Formative 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.Muhammad AfzaalJalal NouriAayesha ZiaPanagiotis PapapetrouUno ForsYongchao WuXiu LiRebecka WeegarFrontiers Media S.A.articleself-regulated learningrecommender systemautomatic data-driven feedbackexplainable machine learning-based approachdashboardlearning analyticsElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Artificial Intelligence, Vol 4 (2021) |
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| collection |
DOAJ |
| language |
EN |
| topic |
self-regulated learning recommender system automatic data-driven feedback explainable machine learning-based approach dashboard learning analytics Electronic computers. Computer science QA75.5-76.95 |
| spellingShingle |
self-regulated learning recommender system automatic data-driven feedback explainable machine learning-based approach dashboard learning analytics Electronic computers. Computer science QA75.5-76.95 Muhammad Afzaal Jalal Nouri Aayesha Zia Panagiotis Papapetrou Uno Fors Yongchao Wu Xiu Li Rebecka Weegar Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation |
| description |
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. |
| format |
article |
| author |
Muhammad Afzaal Jalal Nouri Aayesha Zia Panagiotis Papapetrou Uno Fors Yongchao Wu Xiu Li Rebecka Weegar |
| author_facet |
Muhammad Afzaal Jalal Nouri Aayesha Zia Panagiotis Papapetrou Uno Fors Yongchao Wu Xiu Li Rebecka Weegar |
| author_sort |
Muhammad Afzaal |
| title |
Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation |
| title_short |
Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation |
| title_full |
Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation |
| title_fullStr |
Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation |
| title_full_unstemmed |
Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation |
| title_sort |
explainable ai for data-driven feedback and intelligent action recommendations to support students self-regulation |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/4df5d3a547954061aca9e00fc533c283 |
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