Abstract
Knowledge Tracing (KT) aims to accurately trace the states of evolving knowledge of students and reliably predict students’ performances on future exercises. This task has been widely studied, leading to fast promotion on the development of online education. However, KT still faces two problems. First, most of previous work directly assigned an embedding for each question, which ignores semantic information contained in the questions. Secondly, students may learn differently from correct and incorrect answers to a question. Therefore, the embedding of a question should change based on the correctness of a student’s answer. In this paper, we propose a positive-negative dual-view model named PDNV for knowledge tracing. Firstly, we leverage two Graph Convolutional Networks to learn question embeddings from both positive and negative perspectives. Secondly, an information filtering module is designed based on students’ answers to selectively enhance positive or negative information in question embeddings. Experiment results based on three widely-used datasets demonstrate that our model outperforms state-of-the-art baseline models.
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Xu, Q., Chen, G., Shuai, L., Pu, S., Liu, H., Hao, T. (2023). A Positive-Negative Dual-View Model for Knowledge Tracing. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_35
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DOI: https://doi.org/10.1007/978-981-99-5847-4_35
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