With more deep learning techniques being introduced into the knowledge tracing domain, the interpretability issue of the knowledge tracing models has aroused researchers' attention. Our previous study(Lu et al. 2020) on building and interpreting the KT model mainly adopts the ASSISTment dataset(Feng, Heffernan, and Koedinger 2009),, whose size is relatively small. In this work, we perform the similar tasks but on a large and newly available dataset, called EdNet(Choi et al. 2020). The preliminary experiment results show the effectiveness of the interpreting techniques, while more questions and tasks are worthy to be further explored and accomplished.
@article{arxiv.2111.00419,
title = {Interpreting Deep Knowledge Tracing Model on EdNet Dataset},
author = {Deliang Wang and Yu Lu and Qinggang Meng and Penghe Chen},
journal= {arXiv preprint arXiv:2111.00419},
year = {2021}
}
Comments
This paper has been accepted and presented in AAAI 2021 Workshop on AI Education