How Does Analysis of Handwritten Notes Provide Better Insights for Learning Behavior?

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Handwritten notes are one important component of students' learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students' learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.

本文言語英語
ホスト出版物のタイトルLAK 2022 - Conference Proceedings
ホスト出版物のサブタイトルLearning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
出版社Association for Computing Machinery
ページ549-555
ページ数7
ISBN(電子版)9781450395731
DOI
出版ステータス出版済み - 3月 21 2022
イベント12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, 米国
継続期間: 3月 21 20223月 25 2022

出版物シリーズ

名前ACM International Conference Proceeding Series

会議

会議12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
国/地域米国
CityVirtual, Online
Period3/21/223/25/22

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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