@article{M37E0BB32,
title = "Improvement of Deep Learning Models to Predict the Knowledge Level of Learners based on the EdNet Data",
journal = "Journal of KIISE, JOK",
year = "2021",
issn = "2383-630X",
doi = "10.5626/JOK.2021.48.12.1335",
author = "Seulgi Choi,Youngpyo Kim,Sojung Hwang,Heeyoul Choi",
keywords = "education,personalized learning,knowledge tracing,deep learning,self-attention",
abstract = "As online education increases, the field of AI in Education (AIEd), where artificial intelligence is used for education, is being actively studied. Knowledge Tracing (KT), which predicts a student"s knowledge level based on each student"s learning record, is a basic task in the AIEd field. However, there is a lack of utilization of the dataset and research on the KT model architecture. In this paper, we propose to use a total of 11 features, after trying various features related to the problems, and present a new model based on the self-attention mechanism with new query, key, and values, Self-Attentive Knowledge Tracking Extended (SANTE). In experiments, we confirm that the proposed method with the selected features outperforms the previous KT models in terms of the AUC value."
}