Summary of Disengcd: a Meta Multigraph-assisted Disentangled Graph Learning Framework For Cognitive Diagnosis, by Shangshang Yang et al.
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
by Shangshang Yang, Mingyang Chen, Ziwen Wang, Xiaoshan Yu, Panpan Zhang, Haiping Ma, Xingyi Zhang
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for cognitive diagnosis (CD) that learns three types of representations on disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency. The framework, called DisenGCD, uses a meta multigraph learning module to learn student representations that access lower-order exercise latent representations, leading to more effective and robust student representations. The authors also devise a novel diagnostic function that handles the three disentangled representations for prediction. Experimental results show better performance and robustness of DisenGCD compared to state-of-the-art CD methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand how students learn by using special types of graphs. It’s like a puzzle where you have different pieces that fit together in different ways. The authors use three types of graphs: one for student-exercise interactions, one for exercise-concept connections, and one for concept dependencies. They then use these graphs to learn more about the student’s understanding, which helps them make better predictions. The results show that this new method is better than others at predicting how students will do on a test. |
