MPSKT: Multi-head ProbSparse Self-Attention for Knowledge Tracing
Article No.: 33, Pages 1 - 5
Abstract
Over the past two years, COVID-19 has led to a widespread rise in online education, and knowledge tracing has been used on various educational platforms. However, most existing knowledge tracing models still suffer from long-term dependence. To address this problem, we propose a Multi-head ProbSparse Self-Attention for Knowledge Tracing(MPSKT). Firstly, the temporal convolutional network is used to encode the position information of the input sequence. Then, the Multi-head ProbSparse Self-Attention in the encoder and decoder blocks is used to capture the relationship between the input sequences, and the convolution and pooling layers in the encoder block are used to shorten the length of the input sequence, which greatly reduces the time complexity of the model and better solves the problem of long-term dependence of the model. Finally, experimental results on three public online education datasets demonstrate the effectiveness of our proposed model.
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Index Terms
- MPSKT: Multi-head ProbSparse Self-Attention for Knowledge Tracing
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Publication History
Published: 13 December 2022
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- Research-article
- Research
- Refereed limited
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- National Natural Science Foundation of China
- ?Shaanxi Province 2021 Provincial First-Class Undergraduate Course Construction Project ?Computer Network?
- Shaanxi Normal University Teacher Teaching Model Innovation and Practice Research Special Fund Project in 2021
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CSAE 2022
CSAE 2022: The 6th International Conference on Computer Science and Application Engineering
October 21 - 23, 2022
Virtual Event, China
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