Abstract
This paper views the learning path recommendation task as a sequential decision problem and considers Partially Observable Markov Decision Process (POMDP) as an adequate approach. This work proposes M-POMDP, a POMDP-based recommendation model that manages learners’ memory strength, while limiting the increase in complexity and data required. M-POMDP has been evaluated on two real datasets.
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Zhang, Z., Brun, A., Boyer, A. (2022). Managing Learners’ Memory Strength in a POMDP-Based Learning Path Recommender System. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_53
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DOI: https://doi.org/10.1007/978-3-031-11647-6_53
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