Personalized recommendations for learning activities in online environments: a modular rule-based approach

PELÁNEK, Radek, Tomáš EFFENBERGER and Petr JARUŠEK. Personalized recommendations for learning activities in online environments: a modular rule-based approach. 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.

Basic 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

• Original language: English
• Type of outcome: Article in a journal
• Confidentiality degree: is not subject to a state or trade secret
• Impact factor: Impact factor: 3.600 in 2022
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1007/s11257-024-09396-z
• UT WoS: 001197691700001
• Keywords in English: Recommender system; Education; Learning environment; Adaptive practice; Domain modeling
• Tags: International impact, Reviewed

Abstract

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.