Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Vassoyan, Jean; Vie, Jill-Jênn
Titel	Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Reinforcement; Networks; Simulation; Educational Technology; Teaching Methods; Intelligent Tutoring Systems; Outcomes of Education; Educational Resources; Individualized Instruction; Graphs; Algorithms; Programming Languages + Suchen Sie Ihr Suchwort? Positive Verstärkung; Simulation program; Simulationsprogramm; Unterrichtsmedien; Teaching method; Lehrmethode; Unterrichtsmethode; Intelligentes Tutorsystem; Lernleistung; Schulerfolg; Bildungsmittel; Individualisierender Unterricht; Grafische Darstellung; Algorithm; Algorithmus
Abstract	Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfield of adaptive learning is learning path personalization: it aims at designing systems that recommend sequences of educational activities to maximize students' learning outcomes. Many machine learning approaches have already demonstrated significant results in a variety of contexts related to learning path personalization. However, most of them were designed for very specific settings and are not very reusable. This is accentuated by the fact that they often rely on non-scalable models, which are unable to integrate new elements after being trained on a specific set of educational resources. In this paper, we introduce a flexible and scalable approach towards the problem of learning path personalization, which we formalize as a reinforcement learning problem. Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners. Our results demonstrate that it can learn to make good recommendations in the small-data regime. [For the complete proceedings, see ED630829.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01