Debiased Cognition Representation Learning for Knowledge Tracing

Xiangwei Lv, Guifeng Wang, Jingyuan Chen, Hejian Su, Zhiang Dong, Yumeng Zhu, Beishui Liao, Fei Wu

Abstract: Knowledge tracing (KT) is a fundamental task in intelligent education aimed at tracking students’ knowledge status and predicting their performance on new questions. The primary challenge in KT is accurately inferring a high-quality representation of students’ knowledge state that effectively captures their understanding of questions. However, existing methods are typically developed under the assumption that students’ behaviors directly reflect their knowledge state, which may not hold true especially in online learning scenarios. Abnormal behaviors exhibited by students, such as guessing and plagiarism, can introduce biases into the data, making it difficult to accurately assess students’ true knowledge state. To address this limitation, we propose a novel DebiAsed Cognition rEpresentation (DACE) modeling approach. This approach introduces a novel adversarial training strategy based on information bottleneck theory to obtain a debiased knowledge state representation that retains only the most reliable information for accurately predicting students’ performance on new questions. Moreover, we design a novel contrastive learning module through embedding-based augmentation to further enhance the robustness and generalizability of the learned knowledge state representation. We conduct extensive experiments on three public KT datasets and the newly released dataset BaiPy to demonstrate the superiority of our model over strong baselines, particularly when confronted with biased data. Our code and datasets are available at https://github.com/lvXiangwei/DACE.git.

External IDs:dblp:journals/tois/LvWCSDZLW25