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Papers

Application of Deep Self-Attention in Knowledge Tracing

2021-05-17 · Junhao Zeng, Qingchun Zhang, Ning Xie, Bochun Yang

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.

📄 PDF Abstract BibTeX arXiv:2105.07909

Code (3)

Fusion4233919/DSAKT pytorch
Fusion4233919/SAKT pytorch
pwc-1/Paper-9/tree/main/1/xmod mindspore

Tasks

Knowledge Tracing

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