Enhancing Knowledge Tracing via Adversarial Training

This repository contains source code for the paper "Enhancing Knowledge Tracing via Adversarial Training" to be presented at ACM MM 2021 (Oral).

Requirements

PyTorch==1.7.0
Python==3.8.0

Usage

Cloning the repository

git clone [email protected]:xiaopengguo/ATKT.git
cd ATKT

Running

We evaluate our method on four datasets including Statics2011, ASSISTments2009, ASSISTments2015 and ASSISTments2017.

Statics2011

python main.py --dataset 'statics'

ASSISTments2009

python main.py --dataset 'assist2009_pid'

ASSISTments2015

python main.py --dataset 'assist2015'

ASSISTments2017

python main.py --dataset 'assist2017_pid'

Evaluated results (AUC scores) will be saved in statics_test_result.txt, assist2009_pid_test_result.txt, assist2015_test_result.txt, and assist2017_pid_test_result.txt, respectively.

Acknowledgments

Code and datasets are borrowed from AKT. Adversarial training implementation is inspired by adversarial_training. Early stopping implementation is modified from early-stopping-pytorch.

Reference

@inproceedings{guo2021enhancing,
  title={Enhancing Knowledge Tracing via Adversarial Training},
  author={Guo, Xiaopeng and Huang, Zhijie and Gao, Jie and Shang, Mingyu and Shu, Maojing and Sun, Jun},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={367--375},
  year={2021}
}