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Enhancing Academic Performance Prediction with Temporal Graph Networks for Massive Open Online Courses by Qionghao Huang; Jili Chen is a Computer Science article available to read on EtoBox.

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## Abstract Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized

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Author

Qionghao Huang; Jili Chen

Publisher

Springer Science and Business Media LLC

Published

2024

Language

EN

Field

Computer Science (Physical Sciences)