Abstract
Cognitive diagnostic model (CDM) aims to estimate learners’ cognitive states utilizing different techniques so that personalized educational interventions can be provided. The deterministic inputs noisy and gate (DINA) model is a fundamental CDM that estimates learners’ cognitive states based on response data. However, the response time that learners used to answer test items provides rich information for cognitive diagnosis and influences the accuracy of the responses. In this work, we propose to introduce the response time into guessing and slipping of DINA model, which could better differentiate individual learner’s dynamic cognitive states.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (No. 62177009, 62077006, 62007025), the Fundamental Research Funds for the Central Universities, BNU Interdisciplinary Research Foundation for the First-Year Doctoral Candidates (Grant BNUXKJC2002), and Tencent Cloud Xiaowei.
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Chen, P., Lu, Y., Pian, Y., Li, Y., Cao, Y. (2022). Introducing Response Time into Guessing and Slipping for Cognitive Diagnosis. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_61
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