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@article{2395999, author = {Pelánek, Radek and Effenberger, Tomáš and Jarušek, Petr}, article_location = {DORDRECHT}, doi = {http://dx.doi.org/10.1007/s11257-024-09396-z}, keywords = {Recommender system; Education; Learning environment; Adaptive practice; Domain modeling}, language = {eng}, issn = {0924-1868}, journal = {User Modeling and User-Adapted Interaction}, title = {Personalized recommendations for learning activities in online environments: a modular rule-based approach}, year = {2024} }
TY - JOUR ID - 2395999 AU - Pelánek, Radek - Effenberger, Tomáš - Jarušek, Petr PY - 2024 TI - Personalized recommendations for learning activities in online environments: a modular rule-based approach JF - User Modeling and User-Adapted Interaction PB - Springer Netherlands SN - 09241868 KW - Recommender system KW - Education KW - Learning environment KW - Adaptive practice KW - Domain modeling N2 - Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework. ER -
PELÁNEK, Radek, Tomáš EFFENBERGER and Petr JARUŠEK. Personalized recommendations for learning activities in online environments: a modular rule-based approach. \textit{User Modeling and User-Adapted Interaction}. DORDRECHT: Springer Netherlands, 2024, 32 pp. ISSN~0924-1868. Available from: https://dx.doi.org/10.1007/s11257-024-09396-z.
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