Intensify Knowledge Tracing for Learning Performance Prediction via Tensor-Based Self-Attention
Bibliographic record
Abstract
The purpose of Knowledge Tracing (KT) is to model students’ mastery of Knowledge Concepts (KCs) and predict their future learning performance. Nevertheless, current attention-based methods focus exclusively on direct relationships among KCs and analyze these relationships using low-order attention weights. These methods neglect the indirect relationships among KCs and fail to incorporate multidimensional associations across different subspaces, ultimately leading to poor prediction performance. To address these challenges, we propose a novel tensorbased self-attention (TBSA) mechanism for better learning performance prediction in KT. We first propose TBSA, which captures both direct and indirect relationships among KCs. Then, we design tensor-based attention weights by utilizing tensor’s advantages to incorporate the multidimensional associations. Furthermore, we propose a TBSA-based KT model (i.e., TBKT) to enhance learning performance prediction. Finally, we conduct a series of experiments on four benchmark datasets to validate the effectiveness and innovation of the proposed TBKT. Extensive experimental results demonstrate that TBKT outperforms SOTA methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".