Exploring the Correlation between Students' Attention and Learning Performance

Xin Ping Huang, Chung Kai Yu, Stephen J.H. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the field of education, the use of big data to improve students' learning performance has been a very popular topic recently. The attention of students in the curriculum is obviously a key factor affecting learning performance. In past studies, the attention of students is often measured through questionnaires. The results obtained through the questionnaire are not objective enough, and there are problems of those who feeling good or lack of confidence. Therefore, the measurement of attention was gradually changed to a hardware method, such as brain waves or heart rhythm, etc. Although, the hardware measurement method is more accurate, the cost of the equipment greatly increased, and allowing students to wear these hardware devices may cause the students to perform measurements in an unnatural learning environment. As a result, this study attempts to use a software method to measure and analyzes students' attention in class through e-book reading log, and to explore whether attention can be used as an important indicator of predicting learning performance, and design a set of training to improve student's attention. The research results show that both the e-book reading log and the student's question-posing score can be used to measure attention, and student's attention in class is a key factor affecting their learning performance, that is students with high attention tend to achieve better learning performance.

Original languageEnglish
Title of host publication29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
EditorsMaria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang
PublisherAsia-Pacific Society for Computers in Education
Pages516-521
Number of pages6
ISBN (Electronic)9789869721486
StatePublished - 22 Nov 2021
Event29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online
Duration: 22 Nov 202126 Nov 2021

Publication series

Name29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
Volume2

Conference

Conference29th International Conference on Computers in Education Conference, ICCE 2021
CityVirtual, Online
Period22/11/2126/11/21

Keywords

  • Attention
  • Learning performance
  • Linear regression
  • Question-posing

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