Research map / Label-efficient and robust learning / Knowledge distillation
Exercise-Enhanced Sequential Modeling for Student Performance Prediction
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add exercise text embedding (BiLSTM) instead of knowledge-concept inputs (ablation) | LSTM student model (LSTMA/LSTMM) · Zhixue math | MAE/RMSE/ACC/AUC | Helped |
| add attention over historical student states (vs Markov last state) (ablation) | EERNN · Zhixue math | MAE/RMSE/ACC/AUC | Helped |
| add attention for long fitting sequences (>60) (ablation) | EERNN · Zhixue math, 90% train | ACC/AUC | Mixed |
| use exercise text embeddings for cold-start new exercises | EERNN · Zhixue math cold-start exercises | MAE/RMSE/ACC/AUC | Helped |
Closest papers by meaning
- EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction (ICML 2025 · similarity 0.86)
- Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing (NeurIPS 2025 · similarity 0.86)
- An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling (NeurIPS 2024 · similarity 0.85)
- Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy (AAAI 2024 · similarity 0.84)
- Toward Student-oriented Teacher Network Training for Knowledge Distillation (ICLR 2024 · similarity 0.83)
- Multi-type MOOCs Recommendation: Leveraging Deep Multi-Relational Representation and Hierarchical Reasoning (AAAI 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.