Detailed Information on Publication Record
2024
Personalized recommendations for learning activities in online environments: a modular rule-based approach
PELÁNEK, Radek, Tomáš EFFENBERGER and Petr JARUŠEKBasic information
Original name
Personalized recommendations for learning activities in online environments: a modular rule-based approach
Authors
PELÁNEK, Radek, Tomáš EFFENBERGER and Petr JARUŠEK
Edition
User Modeling and User-Adapted Interaction, DORDRECHT, Springer Netherlands, 2024, 0924-1868
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Confidentiality degree
není předmětem státního či obchodního tajemství
Impact factor
Impact factor: 3.600 in 2022
Organization unit
Faculty of Informatics
UT WoS
001197691700001
Keywords in English
Recommender system; Education; Learning environment; Adaptive practice; Domain modeling
Tags
International impact, Reviewed
Změněno: 22/4/2024 10:39, doc. Mgr. Radek Pelánek, Ph.D.
Abstract
V originále
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.
