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<p>With the rapid development of intelligent tutoring systems (ITSs) in the past decade, tracing students’ knowledge state has become increasingly important for providing individualized learning guidance; this is the main idea of <i>knowledge tracing (KT)</i>, which models students’ mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Many KT models have been proposed and have recently shown remarkable performance. However, the majority of these models use concepts to index questions, which implies that the predefined skill tags for each question are required in advance to indicate the specific KCs needed for answering the question correctly; this makes it difficult to apply to large-scale online education platforms where questions are often not well organized by skill tags. In this paper, we propose Q-matrix-based attentive knowledge tracing (QAKT), an end-to-end KT model that uses the attentive approach in situations where predefined skill tags are not available. With a novel hybrid embedding method based on the Q-matrix and Rasch model, the QAKT model is capable of modeling problems hierarchically and learning the Q-matrix efficiently based on students’ sequences. Moreover, the architecture of the QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically verified that even without predefined skill tags, our model performs similarly to or even better than the state-of-the-art KT methods, by up to 2% in AUC in some cases. Moreover, our model outperforms existing models that do not require skill tags (by up to 7% in AUC) in predicting future learner responses. The results of further experiments suggest that the Q-matrix learned by the QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.</p>

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Attentive Q-matrix learning for knowledge tracing

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Zhongfeng Jia,
  • Wei Su,
  • Jiamin Liu,
  • Wenli Yue

摘要

With the rapid development of intelligent tutoring systems (ITSs) in the past decade, tracing students’ knowledge state has become increasingly important for providing individualized learning guidance; this is the main idea of knowledge tracing (KT), which models students’ mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Many KT models have been proposed and have recently shown remarkable performance. However, the majority of these models use concepts to index questions, which implies that the predefined skill tags for each question are required in advance to indicate the specific KCs needed for answering the question correctly; this makes it difficult to apply to large-scale online education platforms where questions are often not well organized by skill tags. In this paper, we propose Q-matrix-based attentive knowledge tracing (QAKT), an end-to-end KT model that uses the attentive approach in situations where predefined skill tags are not available. With a novel hybrid embedding method based on the Q-matrix and Rasch model, the QAKT model is capable of modeling problems hierarchically and learning the Q-matrix efficiently based on students’ sequences. Moreover, the architecture of the QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically verified that even without predefined skill tags, our model performs similarly to or even better than the state-of-the-art KT methods, by up to 2% in AUC in some cases. Moreover, our model outperforms existing models that do not require skill tags (by up to 7% in AUC) in predicting future learner responses. The results of further experiments suggest that the Q-matrix learned by the QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.