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

J 2024

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

PELÁNEK, Radek, Tomáš EFFENBERGER and Petr JARUŠEK

Basic information

Original name

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

Authors

PELÁNEK, Radek (203 Czech Republic, guarantor, belonging to the institution), Tomáš EFFENBERGER (203 Czech Republic) and Petr JARUŠEK (203 Czech Republic)

Edition

User Modeling and User-Adapted Interaction, DORDRECHT, Springer Netherlands, 2024, 0924-1868

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Impact factor

Impact factor: 3.000 in 2023

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

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.

Files attached

s11257-024-09396-z.pdf

Citovat

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, vol. 34, No 4, p. 1399-1430. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-024-09396-z.

@article{2395999,
   author = {Pelánek, Radek and Effenberger, Tomáš and Jarušek, Petr},
   article_location = {DORDRECHT},
   article_number = {4},
   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},
   url = {https://link.springer.com/article/10.1007/s11257-024-09396-z},
   volume = {34},
   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
VL  - 34
IS  - 4
SP  - 1399-1430
EP  - 1399-1430
PB  - Springer Netherlands
SN  - 09241868
KW  - Recommender system
KW  - Education
KW  - Learning environment
KW  - Adaptive practice
KW  - Domain modeling
UR  - https://link.springer.com/article/10.1007/s11257-024-09396-z
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, vol.~34, No~4, p.~1399-1430. ISSN~0924-1868. Available from: https://dx.doi.org/10.1007/s11257-024-09396-z.

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