DCKT: A Novel Dual-Centric Learning Model for Knowledge Tracing

Yixuan Chen, Shuang Wang, Fan Jiang, Yaxin Tu and Qionghao Huang ()
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Yixuan Chen: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
Shuang Wang: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
Fan Jiang: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
Yaxin Tu: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
Qionghao Huang: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China

Sustainability, 2022, vol. 14, issue 23, 1-20

Abstract: Knowledge tracing (KT), aiming to model learners’ mastery of a concept based on their historical learning records, has received extensive attention due to its great potential in realizing personalized learning in intelligent tutoring systems. However, most existing KT methods focus on a single aspect of knowledge or learner, not paying careful attention to the coupling influence of knowledge and learner characteristics. To fill this gap, in this paper, we explore a new paradigm for the KT task by exploiting the coupling influence of knowledge and learner. A novel model called Dual-Centric Knowledge Tracing (DCKT) is proposed to model knowledge states through two joint tasks of knowledge modeling and learner modeling. In particular, we first generate concept embeddings in abundant knowledge structure information via a pretext task (knowledge-centric): unsupervised graph representation learning. Then, we deeply measure learners’ prior knowledge the knowledge-enhanced representations and three predefined educational priors for discriminative feature enhancement. Furthermore, we design a forgetting-fusion transformer (learner-centric) to simulate the declining trend of learners’ knowledge proficiency over time, representing the common forgetting phenomenon. Extensive experiments were conducted on four public datasets, and the results demonstrate that DCKT could achieve better knowledge tracing results over all datasets via a dual-centric modeling process. Additionally, DCKT can learn meaningful question embeddings automatically without manual annotations. Our work indicates a potential future research direction for personalized learner modeling, which is of both accuracy and high interpretability.

Keywords: knowledge tracing; learner modeling; prerequisite inferences; forgetting behaviors; transformer; personalized learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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