---
language:
- en
license: mit
library_name: transformers
pipeline_tag: text-classification
base_model: google-bert/bert-base-uncased
tags:
- education
---

# 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