Deep Reinforcement Learning for Adaptive Learning Systems

Autor/inn/en	Li, Xiao; Xu, Hanchen; Zhang, Jinming; Chang, Hua-hua
Titel	Deep Reinforcement Learning for Adaptive Learning Systems
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 2, S.220-243 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Li, Xiao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221129847
Schlagwörter	Learning Processes; Models; Algorithms; Individualized Instruction; Instructional Materials; Instructional Design; Student Characteristics; Markov Processes; Decision Making; Artificial Intelligence; Intelligent Tutoring Systems; Item Response Theory; Probability; Reinforcement; Measurement Techniques; Teaching Methods + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Analogiemodell; Algorithm; Algorithmus; Individualisierender Unterricht; Lehrmaterial; Lehrmittel; Unterrichtsmedien; Lesson concept; Lessonplan; Unterrichtsentwurf; Markowscher Prozess; Decision-making; Entscheidungsfindung; Künstliche Intelligenz; Intelligentes Tutorsystem; Item-Response-Theorie; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Positive Verstärkung; Messtechnik; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner's latent traits. In this article, we study an important yet less-addressed adaptive learning problem--one that assumes continuous latent traits. Specifically, we formulate the adaptive learning problem as a Markov decision process. We assume latent traits to be continuous with an unknown transition model and apply a model-free deep reinforcement learning algorithm--the deep Q-learning algorithm--that can effectively find the optimal learning policy from data on learners' learning process without knowing the actual transition model of the learners' continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner's learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy. Especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01