Application of Deep Self-Attention in Knowledge Tracing
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Abstract (arXiv)
The development of intelligent tutoring system has greatly influenced the way students learn and practice, which increases their learning efficiency. The intelligent tutoring system must model learners' mastery of the knowledge before providing feedback and advices to learners, so one class of algorithm called "knowledge tracing" is surely important. This paper proposed Deep Self-Attentive Knowledge Tracing (DSAKT) based on the data of PTA, an online assessment system used by students in many universities in China, to help these students learn more efficiently. Experimentation on the data of PTA shows that DSAKT outperforms the other models for knowledge tracing an improvement of AUC by 2.1% on average, and this model also has a good performance on the ASSIST dataset.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 17 May 2021.
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References · 11
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2016Layer Normalizationcited by 5,669
- 2018Self-Attentive Sequential Recommendationcited by 1,184
- 2017Convolutional Sequence to Sequence Learningcited by 552
- 2015Deep Knowledge Tracingcited by 332
- 2019A Self-Attentive model for Knowledge Tracingcited by 172
- 2016Dynamic Key-Value Memory Networks for Knowledge Tracingcited by 171
- 2016One-shot Learning with Memory-Augmented Neural Networkscited by 60
- 2018Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularizationcited by 55
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