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Neural Cognitive Diagnosis for Intelligent Education Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Refine the Q-matrix with exercise text | NeuralCDM · Math | Accuracy: 0.792 -> 0.804 | Helped |
| Refine the Q-matrix with exercise text | NeuralCDM · Math | RMSE: 0.378 -> 0.371 | Helped |
| Refine the Q-matrix with exercise text | NeuralCDM · Math | AUC: 0.820 -> 0.835 | Helped |
| Use Q-matrix knowledge relevancy vectors rather than estimating them during unsupervised training (ablation) | NeuralCDM · Math and ASSIST | DOA | Helped |
| Apply the monotonicity assumption by constraining network weights to be positive (ablation) | NeuralCDM · Math and ASSIST | DOA | Helped |
Closest papers by meaning
- Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems (AAAI 2024 · similarity 0.91)
- Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation (NeurIPS 2024 · similarity 0.88)
- Boosting Neural Cognitive Diagnosis with Student’s Affective State Modeling (AAAI 2024 · similarity 0.88)
- Enhancing Cognitive Diagnosis Using Un-interacted Exercises: A Collaboration-Aware Mixed Sampling Approach (AAAI 2024 · similarity 0.87)
- AD4CD: Causal-Guided Anomaly Detection for Enhancing Cognitive Diagnosis (AAAI 2025 · similarity 0.87)
- Zero-1-to-3: Domain-Level Zero-Shot Cognitive Diagnosis via One Batch of Early-Bird Students towards Three Diagnostic Objectives (AAAI 2024 · similarity 0.86)
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