Summary of End-to-end Graph Learning Approach For Cognitive Diagnosis Of Student Tutorial, by Fulai Yang et al.
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial
by Fulai Yang, Di Wu, Yi He, Li Tao, Xin Luo
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed End-to-end Graph Neural Networks-based Cognitive Diagnosis (EGNN-CD) model addresses the challenge of accurate cognitive diagnosis by leveraging existing studying records. The model consists of three components: knowledge concept network (KCN), graph neural networks-based feature extraction (GNNFE), and cognitive ability prediction (CAP). KCN constructs a CD-related interaction by extracting physical information from students, exercises, and knowledge concepts. GNNFE extracts high-order and individual features from the constructed KCN, while CAP employs a multi-layer perceptron to fuse these features for predicting learning abilities. The EGNN-CD model outperforms state-of-the-art models in cognitive diagnosis tasks on three real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The EGNN-CD model is a new approach to cognitive diagnosis that uses graph neural networks and end-to-end learning. It starts by creating a network of knowledge concepts and how they relate to each other and to students’ studying records. Then, it uses this network to extract features that can be used to predict students’ learning abilities. The model shows great promise in accurately diagnosing student mastery of unknown knowledge concepts. |
Keywords
» Artificial intelligence » Feature extraction
