Performance Prediction and Importance Analysis Using Transformer

Akiyoshi Satake, Hironobu Fujiyoshi, Takayoshi Yamashita, Tsubasa Hirakawa, Atsushi Shimada

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
編集者Maria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang
出版社Asia-Pacific Society for Computers in Education
ページ538-543
ページ数6
ISBN(電子版)9789869721486
出版ステータス出版済み - 11月 22 2021
イベント29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online
継続期間: 11月 22 202111月 26 2021

出版物シリーズ

名前29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
2

会議

会議29th International Conference on Computers in Education Conference, ICCE 2021
CityVirtual, Online
Period11/22/2111/26/21

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(その他)
  • 教育

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