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Understanding User Perspectives for MOOC Quality Evaluation with Hypergraph Learning
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AI
DOI：10.1145/3749845.png)
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Abstract
En 中文
Evaluation of Massive Open Online Course (MOOC) quality is crucial to enhance the educational resources, benefiting user services, and enhancing students’ learning efficiency. Despite achieving encouraging results, current efforts are hindered by complex relationships between entities and individual varies. To address the above problem, in this article, we frame the issue as a task of learning course representations and proceed to develop an User-Centric Hypergraph Representation Learning (UHRL) for online course quality evaluation. In particular, we initially construct a MOOC hypergraph to depict the interactions and connections between the entities and use cross-hyperedge alignment to reveal the semantics of courses. And then we incorporate an attention mechanism in the information transmission process to ensure semantic integrity. Furthermore, to tackle the bias of users’ preference, our framework exploits mutual information for preserving the fairness of representation learning. Finally, our comprehensive experiments on three real-world datasets confirm the effectiveness of our approach compared to cutting-edge methods in evaluating online course quality across various performance metrics.
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