/HawkesKT

WSDM'2021: Temporal Cross-Effects in Knowledge Tracing

Primary LanguageJupyter NotebookMIT LicenseMIT

HawkesKT

illustration

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.

Usage

  1. Install Anaconda with Python >= 3.5
  2. Clone the repository and install requirements
git clone https://github.com/THUwangcy/HawkesKT
  1. Prepare datasets according to README in data directory
  2. Install requirements and step into the src folder
cd HawkesKT
pip install -r requirements.txt
cd src
  1. 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_09-10

Example training log can be found here.

Arguments

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

Performance

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.

Citation

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}
}

Contact

Chenyang Wang (THUwangcy@gmail.com)