Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Authors

  • Jiahao Chen TAL Education Group
  • Zitao Liu Jinan University
  • Shuyan Huang TAL Education Group
  • Qiongqiong Liu TAL Education Group
  • Weiqi Luo Jinan University

DOI:

https://doi.org/10.1609/aaai.v37i12.26661

Keywords:

General

Abstract

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students’ knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.

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Published

2023-06-26

How to Cite

Chen, J., Liu, Z., Huang, S., Liu, Q., & Luo, W. (2023). Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14196-14204. https://doi.org/10.1609/aaai.v37i12.26661

Issue

Section

AAAI Special Track on AI for Social Impact