MACHINE PERCEPTION AND ROBOTICS GROUP

機械知覚&ロボティクスグループ
中部大学

The growth of online education has made it easier to capture learner activity. It is expected that detailed feedback to learners will lead to better performance. For this purpose, it is important to predict the performance of learners. Methods using classical machine learning and RNNs that take time series information into account have been proposed. In this paper, we propose a Transformer-based performance prediction method that aims to improve accuracy and extract important activity. The proposed method achieves more accurate performance prediction than conventional methods. In addition, we found that NEXT, SEARCH_JUMP and LINK_CLICK are important behaviors by analyzing the rationale of the Transformer.