Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu 0001. Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022. pages 12810-12818, AAAI Press, 2022. [doi]

@inproceedings{MinnVTK022,
  title = {Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations},
  author = {Sein Minn and Jill-Jênn Vie and Koh Takeuchi and Hisashi Kashima and Feida Zhu 0001},
  year = {2022},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/21560},
  researchr = {https://researchr.org/publication/MinnVTK022},
  cites = {0},
  citedby = {0},
  pages = {12810-12818},
  booktitle = {Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022},
  publisher = {AAAI Press},
  isbn = {978-1-57735-876-3},
}