Abstract
Recently, working memory models have been used in knowledge tracing to improve prediction performance, which is a cognitive system with limited processing capacity enabling short-term storage of information. However, existing versions do not model limited capacity adequately or consider individual differences in processing capacity. To resolve these problems, we simulate working memory for knowledge tracking (MCKT) by utilizing a Convolutional Neural Network (CNN). To this end, multiple CNNs with convolution kernels of different scales are used to process a single interaction sequence, where each scale of convolution kernels represents a certain processing capacity of the current learner. MCKT utilizes a multi-scale convolution kernel approach to achieve personalized simulation of processing capacities of different learners. Moreover, long-term memory is simulated to predict future learner responses. The results of experiments on several benchmark datasets from the real world reveals that MCKT performs better than multiple classical models.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 61977033), the State Key Program of National Natural Science of China (Grant No. U20A20229), and the Central China Normal University National Teacher Development Collaborative Innovation Experimental Base Construction Research Project (Grant No. CCNUTEIII 2021-03).
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Yang, H., Chen, B., Hu, J., Huang, T., Geng, J., Tang, L. (2023). Modeling Working Memory Using Convolutional Neural Networks for Knowledge Tracing. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_11
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