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MOOC Recommendation Using Heterogeneous Graph Neural Network and Attention Mechanism

Published: 14 June 2024 Publication History

Abstract

Massive Open Online Courses (MOOCs) are a contemporary approach to education, providing a large number of open courses to facilitate students' access to knowledge. However, the absence of personalized recommendations based on specific knowledge concepts has decreased students' enthusiasm because of the many courses with different emphases. To address this issue, this paper proposes a catechism resource recommendation model, HGNNRec, which is based on a heterogeneous graph neural network combined with an attention mechanism. The model captures learners' interest preferences by constructing a heterogeneous information network, extracting rich semantic information using meta-paths, performing node feature extraction and weight assignment via graph convolutional networks and attention mechanisms, and finally incorporating matrix decomposition methods for recommendation. The experimental results demonstrate that the proposed method outperforms various baseline and existing methods in predicting and recommending concepts of interest to users. The model can effectively address the problem of personalized recommendations for learners and improve the overall learning experience in MOOCs.

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AIPR '23: Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 14 June 2024

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