research-article

Learner Modeling and Recommendation of Learning Resources using Personal Knowledge Graphs

Authors:

Mohamed Amine Chatti,

Paul Arthur Meteng Kamdem,

Rawaa Alatrash,

Clara SiepmannAuthors Info & Claims

LAK '24: Proceedings of the 14th Learning Analytics and Knowledge Conference

Pages 273 - 283

https://doi.org/10.1145/3636555.3636881

Published: 18 March 2024 Publication History

Abstract

Educational recommender systems (ERS) are playing a pivotal role in providing recommendations of personalized resources and activities to students, tailored to their individual learning needs. A fundamental part of generating recommendations is the learner modeling process that identifies students’ knowledge state. Current ERSs, however, have limitations mainly related to the lack of transparency and scrutability of the learner models as well as capturing the semantics of learner models and learning materials. To address these limitations, in this paper we empower students to control the construction of their personal knowledge graphs (PKGs) based on the knowledge concepts that they actively mark as ’did not understand (DNU)’ while interacting with learning materials. We then use these PKGs to build semantically-enriched learner models and provide personalized recommendations of external learning resources. We conducted offline experiments and an online user study (N=31), demonstrating the benefits of a PKG-based recommendation approach compared to a traditional content-based one, in terms of several important user-centric aspects including perceived accuracy, novelty, diversity, usefulness, user satisfaction, and use intentions. In particular, our results indicate that the degree of control students are able to exert over the learner modeling process, has positive consequences on their satisfaction with the ERS and their intention to accept its recommendations.

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Cited By

Alatrash RChatti MAin QJoarder SBoratto LGena CMarras MGermanakos PPopescus E(2024)Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural NetworksAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665230(591-596)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665230
Dong YQin KLiang S(2024)Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue SystemsNatural Language Processing and Chinese Computing10.1007/978-981-97-9431-7_3(29-42)Online publication date: 1-Nov-2024
https://doi.org/10.1007/978-981-97-9431-7_3

Index Terms

Learner Modeling and Recommendation of Learning Resources using Personal Knowledge Graphs

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LAK '24: Proceedings of the 14th Learning Analytics and Knowledge Conference

March 2024

962 pages

ISBN:9798400716188

DOI:10.1145/3636555

Copyright © 2024 ACM.

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Published: 18 March 2024

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LAK '24: The 14th Learning Analytics and Knowledge Conference

March 18 - 22, 2024

Kyoto, Japan

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Cited By

Alatrash RChatti MAin QJoarder SBoratto LGena CMarras MGermanakos PPopescus E(2024)Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural NetworksAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665230(591-596)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665230
Dong YQin KLiang S(2024)Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue SystemsNatural Language Processing and Chinese Computing10.1007/978-981-97-9431-7_3(29-42)Online publication date: 1-Nov-2024
https://doi.org/10.1007/978-981-97-9431-7_3

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