This is our implementation for the paper:
Chenyang Wang, Weizhi Ma, Min Zhang, Chuancheng Lv, Fengyuan Wan, Huijie Lin, Taoran Tang, Yiqun Liu, and Shaoping Ma. Temporal Cross-effects in Knowledge Tracing. In WSDM'21.
- Install Anaconda with Python >= 3.5
- Clone the repository and install requirements
git clone https://github.com/THUwangcy/HawkesKT- Prepare datasets according to README in data directory
- Install requirements and step into the
srcfolder
cd HawkesKT
pip install -r requirements.txt
cd src- Run model
python main.py --model_name HawkesKT --emb_size 64 --max_step 50 --lr 5e-3 --l2 1e-5 --time_log 5 --gpu 1 --dataset ASSISTments_09-10Example training log can be found here.
The main arguments of HawkesKT are listed below.
| Args | Default | Help |
|---|---|---|
| emb_size | 64 | Size of embedding vectors |
| time_log | e | Base of log transformation on time intervals |
| max_step | 50 | Consider the first max_step interactions in each sequence |
| fold | 0 | Fold to run |
| lr | 1e-3 | Learning rate |
| l2 | 0 | Weight decay of the optimizer |
| batch_size | 100 | Batch size |
| regenerate | 0 | Whether to read data again and regenerate intermediate files |
The table below lists the results of some representative models in ASSISTments 12-13 dataset.
| Model | AUC | Time/iter | Time-aware | Temporal cross |
|---|---|---|---|---|
| DKT | 0.7308 | 3.8s | ||
| DKT-Forgetting | 0.7462 | 6.2s | √ | |
| KTM | 0.7535 | 49.8s | √ | |
| AKT-R | 0.7555 | 13.8s | √ | |
| HawkesKT | 0.7676 | 3.2s | √ | √ |
Current running commands are listed in run.sh. We adopt 5-fold cross validation and report the average score (see run_exp.py). All experiments are conducted with a single GTX-1080Ti GPU.
Please cite our paper if you use our codes. Thanks!
@inproceedings{wang2021temporal,
title={Temporal cross-effects in knowledge tracing},
author={Wang, Chenyang and Ma, Weizhi and Zhang, Min and Lv, Chuancheng and Wan, Fengyuan and Lin, Huijie and Tang, Taoran and Liu, Yiqun and Ma, Shaoping},
booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
pages={517--525},
year={2021}
}
Chenyang Wang ([email protected])
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