Abstract
Knowledge tracing models the cognitive process of skill acquisition of a student to predict the current knowledge state. Based on cognitive processing theory, we regard student knowledge state in dichotomous view in alignment with Performance Factor Analysis (PFA). Assuming that a student’s correct and incorrect responses are fundamentally different for modeling a student’s knowledge state, we propose a Dichotomous Knowledge Tracing (DiKT), a novel knowledge tracing network with a dichotomous perspective on a student’s knowledge state. We modify the network’s value memory by dividing it into two memories, each encoding recallable and unrecallable knowledge to precisely capture the student knowledge state. With the proposed architecture, our model generates a knowledge trajectory that instantly and accurately portrays a student’s knowledge level based on learning history. Empirical evaluations demonstrate that our proposed model achieves comparable performance on benchmark educational datasets.
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Acknowledgements
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).
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Kim, S., Kim, W., Jung, H., Kim, H. (2021). DiKT: Dichotomous Knowledge Tracing. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_5
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DOI: https://doi.org/10.1007/978-3-030-80421-3_5
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