Improvement of Deep Learning Models to Predict the Knowledge Level of Learners based on the EdNet Data

Seulgi Choi, Youngpyo Kim, Sojung Hwang, Heeyoul Choi

http://doi.org/10.5626/JOK.2021.48.12.1335

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.