<p>Knowledge Tracing (KT) estimates a learner’s knowledge state within a given domain based on their response data. Although approaches such as Bayesian modeling and neural-based models have achieved remarkable estimation accuracy, conventional models update the learner’s knowledge state only at the time response data are obtained, thereby preventing the consideration of real-time state changes between responses. In this study, we introduce the concept of continuous time and propose a knowledge tracing method grounded in mechanical dynamics. Experimental results demonstrate that incorporating “indeterminate” evaluations–cases where it cannot be determined whether constraints are considered–enhances the overall validity of state estimation. Despite accounting for such ambiguous states, the proposed model achieves accuracy comparable to Bayesian knowledge tracing methods in predicting learners’ correct response rates.</p>

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Dynamic computational tracking of learners’ knowledge states based on the cognitive diagnostic model

  • Yoshimasa Tawatsuji,
  • Sho Yamamoto

摘要

Knowledge Tracing (KT) estimates a learner’s knowledge state within a given domain based on their response data. Although approaches such as Bayesian modeling and neural-based models have achieved remarkable estimation accuracy, conventional models update the learner’s knowledge state only at the time response data are obtained, thereby preventing the consideration of real-time state changes between responses. In this study, we introduce the concept of continuous time and propose a knowledge tracing method grounded in mechanical dynamics. Experimental results demonstrate that incorporating “indeterminate” evaluations–cases where it cannot be determined whether constraints are considered–enhances the overall validity of state estimation. Despite accounting for such ambiguous states, the proposed model achieves accuracy comparable to Bayesian knowledge tracing methods in predicting learners’ correct response rates.