Graph Convolutional Neural Network Knowledge Tracking Based on Response Time Feature
Why this work is in the frame
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Bibliographic record
Abstract
The GCKT model is proposed based on the following two feature optimization ideas. Firstly, Graph Convolutional Neural Network (GCN) is applied to knowledge tracking in order to enhance local features, improve the effect of the model, and reduce the risk of overfitting. In addition, in order to solve the problem that the current GKT model only depends on the relevant content of the learner's answer and input few features, which leads to low prediction accuracy, the model in this paper uses the time features obtained by incorporating the learner's answer time of each exercise, and gives the learner each answer record as the model input.To improve the accuracy of prediction. Finally, the effectiveness and rationality of the proposed method are proved by experiments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it