Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning

TL;DR

This paper introduces a novel information-aware graph representation learning method for multi-view MOOC quality evaluation, effectively capturing complex platform interactions to improve course assessment accuracy.

Contribution

It develops a multi-view graph learning framework using mutual information to enhance MOOC quality evaluation, addressing complex entity relationships in MOOC platforms.

Findings

Outperforms existing methods on real-world datasets

Effectively captures multi-view semantics of courses

Improves the validity and expressiveness of course representations

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.