On-Task BERT
This model classifies a student's classroom utterance as off-task (0) or
on-task (1). It is a BERT-base-uncased sequence classifier fine-tuned on
English student utterances from mathematics classroom transcripts.
Evaluation
The final model was selected using five-fold cross-validation on the training and validation data, retrained on 1,878 examples, and evaluated once on a held-out test set of 470 examples.
| Metric | Result |
|---|---|
| Accuracy | 0.900 |
| Macro F1 | 0.813 |
| On-task precision | 0.935 |
| On-task recall | 0.947 |
| On-task F1 | 0.941 |
The test confusion counts were 372 true positives, 26 false positives, 21 false negatives, and 51 true negatives. On the same test split, a math-vocabulary baseline achieved 0.849 accuracy and 0.559 macro F1.
Training details
- Base model:
bert-base-uncased - Maximum sequence length: 256
- Epochs: 5
- Learning rate: 3e-5
- Input: student utterance only