Abstract
Knowledge tracing(KT) refers to the task of modeling students’ evolving knowledge state according to their historical learning trajectories. Although many methods have been proposed to solve KT task, most of them ignore the difference of students and questions, i.e., the students’ learning ability are different from each other. To this end, in this paper, a learning ability estimation module is proposed to extract students’ learning ability according to their learning history and a novel method to obtain questions’ representation is designed. Besides, a knowledge state estimation module is proposed to estimation students’ knowledge state which takes both students’ learning ability and their learning interaction into consideration when modeling. Extensive experiments demonstrate that the proposed model could improve thr results of knowledge tracing through modeling individualized students’ learning ability and questions’ difficulty in learning process.
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Acknowledgements
This research was funded in part by the National Natural Science Foundation of China (No. 62177032), Public Course Reform Project of Shaanxi Normal University (No. 21GGK-JG02) and the Fundamental Research Funds for the Central Universities (No. GK202205020).
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Xiao, B., Jiang, H., Ma, J., Zhang, R. (2023). Tracing Knowledge State with Individualized Ability and Question Difficulty. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1811. Springer, Singapore. https://doi.org/10.1007/978-981-99-2443-1_40
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