Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Afzaal, Muhammad, Nouri, Jalal, Zia, Aayesha, Papapetrou, Panagiotis, Fors, Uno, Wu, Yongchao, Li, Xiu, Weegar, Rebecka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636130/
https://www.ncbi.nlm.nih.gov/pubmed/34870183
http://dx.doi.org/10.3389/frai.2021.723447
_version_ 1784608473021939712
author Afzaal, Muhammad
Nouri, Jalal
Zia, Aayesha
Papapetrou, Panagiotis
Fors, Uno
Wu, Yongchao
Li, Xiu
Weegar, Rebecka
author_facet Afzaal, Muhammad
Nouri, Jalal
Zia, Aayesha
Papapetrou, Panagiotis
Fors, Uno
Wu, Yongchao
Li, Xiu
Weegar, Rebecka
author_sort Afzaal, Muhammad
collection PubMed
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 Online
Article
Text
id pubmed-8636130
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-86361302021-12-02 Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation Afzaal, Muhammad Nouri, Jalal Zia, Aayesha Papapetrou, Panagiotis Fors, Uno Wu, Yongchao Li, Xiu Weegar, Rebecka Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2021-11-12 /pmc/articles/PMC8636130/ /pubmed/34870183 http://dx.doi.org/10.3389/frai.2021.723447 Text en Copyright © 2021 Afzaal, Nouri, Zia, Papapetrou, Fors, Wu, Li and Weegar. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Afzaal, Muhammad
Nouri, Jalal
Zia, Aayesha
Papapetrou, Panagiotis
Fors, Uno
Wu, Yongchao
Li, Xiu
Weegar, Rebecka
Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation
title 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_short 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
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636130/
https://www.ncbi.nlm.nih.gov/pubmed/34870183
http://dx.doi.org/10.3389/frai.2021.723447
work_keys_str_mv AT afzaalmuhammad explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT nourijalal explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT ziaaayesha explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT papapetroupanagiotis explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT forsuno explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT wuyongchao explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT lixiu explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation
AT weegarrebecka explainableaifordatadrivenfeedbackandintelligentactionrecommendationstosupportstudentsselfregulation