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https://github.com/qqpann/knowledgetracing
Customizable Deep Knowledge Tracing models and reproducible experimental scripts in one place.
https://github.com/qqpann/knowledgetracing
Last synced: about 1 month ago
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Customizable Deep Knowledge Tracing models and reproducible experimental scripts in one place.
- Host: GitHub
- URL: https://github.com/qqpann/knowledgetracing
- Owner: qqpann
- License: mit
- Created: 2019-10-21T07:53:17.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2023-07-28T05:34:05.000Z (over 1 year ago)
- Last Synced: 2024-05-01T17:23:26.586Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 23.2 MB
- Stars: 6
- Watchers: 4
- Forks: 2
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
> **Warning**: This code is provided as-is for reproductivity. No updates and security patches are planned so far.
# Knowledge Tracing
Customizable Deep Knowledge Tracing models and reproducible experimental scripts in one place.
## Quick start
### Install and run an experiment
```terminal
$ poetry install
$ poetry shell
$ python main.py config/debug/debug.json
```
Alternatively, you can use pip instead of poetry:
`pip install -r requirements.txt`.
Create your own config JSON file and you can start your experiment.
## Datasets
We used prepared data by Zhang et al.
| Dataset name | KC size | Link |
| --------------------------------------- | ------- | -------------------------------------------------------------------------------------- |
| ASSISTments Skill builder 2009-2010 | 110 | https://sites.google.com/site/assistmentsdata/home/assistment-2009-2010-data |
| ASSISTments Skill builder 2015 | 100 | https://sites.google.com/site/assistmentsdata/home/2015-assistments-skill-builder-data |
| ASSISTments Datamining competition 2017 | | https://sites.google.com/view/assistmentsdatamining/home?authuser=0 |
| statics | 1223 | |
| synthetic | 50 | |
## Environments
| requirements | version |
| ------------ | ------- |
| Python | 3.7 |
| CUDA | 10.1 |
| PyTorch | 1.5.0 |
## BibTex
Please cite our paper if you use the code.
### Pre-training
[Proceeding p.265](https://icce2021.apsce.net/proceedings/volume1/) | [Slide](https://speakerdeck.com/qqhann/icce2021-prior-knowledge-on-the-dynamics-of-skill-acquisition-improves-deep-knowledge-tracing)
To reproduce, read `notebook/Results_ICCE2021.ipynb` and run the same experiment.
```bibtex
# To appear
@article{panaccuracy,
title={Prior knowledge on the dynamics of skill acquisition improves deep knowledge tracing},
author={Qiushi Pan and Taro Tezuka},
booktitle={Proceedings of the 29th International Conference on Computers in Education},
year={2021}
}
```
```text
Qiushi Pan and Taro Tezuka, Prior knowledge on the dynamics of skill acquisition improves deep knowledge tracing, Proceedings of the 29th International Conference on Computers in Education, November 2021 (ICCE2021). (to appear)
```
### Knowledge State Vector Loss
To reproduce, read `notebook/Results_ICCE2020.ipynb` and run the same experiment.
```bibtex
@article{panaccuracy,
title={Accuracy-aware Deep Knowledge Tracing with Knowledge State Vector Loss},
author={Qiushi Pan and Taro Tezuka},
booktitle={Proceedings of the 28th International Conference on Computers in Education (ICCE2020)},
year={2020}
}
```
```text
Qiushi Pan and Taro Tezuka, Accuracy-aware deep knowledge tracing with knowledge state vectors and an encoder-decoder architecture, Proceedings of the 28th International Conference on Computers in Education (ICCE2020), Online, November 23-27, 2020.
```
## References
- DKT by author Piech
- DKVMN by author Zhang
- DeepIRT by author Yeung