dumpmemory / atkt

"Enhancing Knowledge Tracing via Adversarial Training", ACM MM 2021 (Oral).

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}
}