PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems

Advancements in adaptive educational technologies, specifically the adaptive learning system, have made it possible to automatically optimize the sequencing of the pedagogical instructions according to the needs of individual learners. The crux of such systems lies in the instructional sequencing po...

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Autores principales:	Muhammad Zubair Islam, Rashid Ali, Amir Haider, Md Zahidul Islam, Hyung Seok Kim
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Adaptability recommendation adaptive learning system knowledge acquisition Markov model general diagnostic models educational technology Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2f454cf8b29a45a0a4cc17a90e10a218
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spelling	oai:doaj.org-article:2f454cf8b29a45a0a4cc17a90e10a2182021-11-26T00:02:00ZPAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems2169-353610.1109/ACCESS.2021.3128578https://doaj.org/article/2f454cf8b29a45a0a4cc17a90e10a2182021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617651/https://doaj.org/toc/2169-3536Advancements in adaptive educational technologies, specifically the adaptive learning system, have made it possible to automatically optimize the sequencing of the pedagogical instructions according to the needs of individual learners. The crux of such systems lies in the instructional sequencing policy, which recommends personalized learning material based on the learning experiences of the learner to maximize their learning outcomes. However, limited available information such as cognitive, affective states, and competence levels of the learners ongoing knowledge points servers critical challenges to optimizing individual-specific pedagogical instructions in real-time. Moreover, making such decisions policy for every learner with a unique knowledge profile demands a trade-off between learner current knowledge and curiosity to learn next knowledge point. To address these challenges, this paper proposes a personalized adaptability knowledge extraction strategy (PAKES) using cognitive diagnosis and reinforcement learning (RL). We apply the general diagnostic model to track the current knowledge state of the learners. Subsequently, an RL-based Q-learning algorithm is employed to recommend optimal pedagogical instructions for individuals to meet their learning objectives while maintaining equilibrium among the learner-control and teaching trajectories. The results indicate that the learning analytics of the proposed framework can fairly deliver the optimal pedagogical paths for the learners based upon their learning profiles. A 62% learning progress score was achieved with the pedagogical paths recommended by the PAKES, showing a 20% improvement compared to the baseline model.Muhammad Zubair IslamRashid AliAmir HaiderMd Zahidul IslamHyung Seok KimIEEEarticleAdaptability recommendationadaptive learning systemknowledge acquisitionMarkov modelgeneral diagnostic modelseducational technologyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155123-155137 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Adaptability recommendation adaptive learning system knowledge acquisition Markov model general diagnostic models educational technology Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Adaptability recommendation adaptive learning system knowledge acquisition Markov model general diagnostic models educational technology Electrical engineering. Electronics. Nuclear engineering TK1-9971 Muhammad Zubair Islam Rashid Ali Amir Haider Md Zahidul Islam Hyung Seok Kim PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems
description	Advancements in adaptive educational technologies, specifically the adaptive learning system, have made it possible to automatically optimize the sequencing of the pedagogical instructions according to the needs of individual learners. The crux of such systems lies in the instructional sequencing policy, which recommends personalized learning material based on the learning experiences of the learner to maximize their learning outcomes. However, limited available information such as cognitive, affective states, and competence levels of the learners ongoing knowledge points servers critical challenges to optimizing individual-specific pedagogical instructions in real-time. Moreover, making such decisions policy for every learner with a unique knowledge profile demands a trade-off between learner current knowledge and curiosity to learn next knowledge point. To address these challenges, this paper proposes a personalized adaptability knowledge extraction strategy (PAKES) using cognitive diagnosis and reinforcement learning (RL). We apply the general diagnostic model to track the current knowledge state of the learners. Subsequently, an RL-based Q-learning algorithm is employed to recommend optimal pedagogical instructions for individuals to meet their learning objectives while maintaining equilibrium among the learner-control and teaching trajectories. The results indicate that the learning analytics of the proposed framework can fairly deliver the optimal pedagogical paths for the learners based upon their learning profiles. A 62% learning progress score was achieved with the pedagogical paths recommended by the PAKES, showing a 20% improvement compared to the baseline model.
format	article
author	Muhammad Zubair Islam Rashid Ali Amir Haider Md Zahidul Islam Hyung Seok Kim
author_facet	Muhammad Zubair Islam Rashid Ali Amir Haider Md Zahidul Islam Hyung Seok Kim
author_sort	Muhammad Zubair Islam
title	PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems
title_short	PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems
title_full	PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems
title_fullStr	PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems
title_full_unstemmed	PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems
title_sort	pakes: a reinforcement learning-based personalized adaptability knowledge extraction strategy for adaptive learning systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2f454cf8b29a45a0a4cc17a90e10a218
work_keys_str_mv	AT muhammadzubairislam pakesareinforcementlearningbasedpersonalizedadaptabilityknowledgeextractionstrategyforadaptivelearningsystems AT rashidali pakesareinforcementlearningbasedpersonalizedadaptabilityknowledgeextractionstrategyforadaptivelearningsystems AT amirhaider pakesareinforcementlearningbasedpersonalizedadaptabilityknowledgeextractionstrategyforadaptivelearningsystems AT mdzahidulislam pakesareinforcementlearningbasedpersonalizedadaptabilityknowledgeextractionstrategyforadaptivelearningsystems AT hyungseokkim pakesareinforcementlearningbasedpersonalizedadaptabilityknowledgeextractionstrategyforadaptivelearningsystems
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PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems

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