https://github.com/yxonic/dtransformer

Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer (WWW '23)
https://github.com/yxonic/dtransformer

dtransformer knowledge-tracing pytorch transformer www2023

Last synced: 2 months ago
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Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer (WWW '23)

• Host: GitHub
• URL: https://github.com/yxonic/dtransformer
• Owner: yxonic
• Created: 2022-09-11T14:47:30.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-21T08:06:23.000Z (about 1 year ago)
• Last Synced: 2023-11-21T09:27:41.660Z (about 1 year ago)
• Topics: dtransformer, knowledge-tracing, pytorch, transformer, www2023
• Language: Python
• Homepage:
• Size: 4.26 MB
• Stars: 19
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DTransformer

Code for _Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer_ (accepted at WWW '23).

Cite this work:

```bibtex

@inproceedings{yin2023tracing,

  author = {Yin, Yu and Dai, Le and Huang, Zhenya and Shen, Shuanghong and Wang, Fei and Liu, Qi and Chen, Enhong and Li, Xin},

  title = {Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer},

  year = {2023},

  isbn = {9781450394161},

  publisher = {Association for Computing Machinery},

  address = {New York, NY, USA},

  url = {https://doi.org/10.1145/3543507.3583255},

  doi = {10.1145/3543507.3583255},

  booktitle = {Proceedings of the ACM Web Conference 2023},

  pages = {855–864},

  numpages = {10},

  keywords = {contrastive learning, knowledge tracing, DTransformer},

  location = {Austin, TX, USA},

  series = {WWW '23}

}

```

## Installation

```bash

poetry install

```

## Usage

### Train

Train DTransformer with CL loss:

```bash

python scripts/train.py -m DTransformer -d [assist09,assist17,algebra05,statics] -bs 32 -tbs 32 -p -cl --proj [-o output/DTransformer_assist09] [--device cuda]

```

For more options, run:

```bash

python scripts/train.py -h

```

### Evaluate

Evaluate DTransformer:

```bash

python scripts/test.py -m DTransformer -d [assist09,assist17,algebra05,statics] -bs 32 -p -f [output/best_model.pt] [--device cuda]

```