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Article overview
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Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning | Lu Jiang
; Yibin Wang
; Jianan Wang
; Pengyang Wang
; Minghao Yin
; | Date: |
1 Jan 2023 | Abstract: | In this paper, we study the problem of MOOC quality evaluation which is
essential for improving the course materials, promoting students’ learning
efficiency, and benefiting user services. While achieving promising
performances, current works still suffer from the complicated interactions and
relationships of entities in MOOC platforms. To tackle the challenges, we
formulate the problem as a course representation learning task-based and
develop an Information-aware Graph Representation Learning(IaGRL) for
multi-view MOOC quality evaluation. Specifically, We first build a MOOC
Heterogeneous Network (HIN) to represent the interactions and relationships
among entities in MOOC platforms. And then we decompose the MOOC HIN into
multiple single-relation graphs based on meta-paths to depict the multi-view
semantics of courses. The course representation learning can be further
converted to a multi-view graph representation task. Different from traditional
graph representation learning, the learned course representations are expected
to match the following three types of validity: (1) the agreement on
expressiveness between the raw course portfolio and the learned course
representations; (2) the consistency between the representations in each view
and the unified representations; (3) the alignment between the course and MOOC
platform representations. Therefore, we propose to exploit mutual information
for preserving the validity of course representations. We conduct extensive
experiments over real-world MOOC datasets to demonstrate the effectiveness of
our proposed method. | Source: | arXiv, 2301.01593 | Services: | Forum | Review | PDF | Favorites |
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