Facial micro-expression states as an indicator for conceptual change in students' understanding of air pressure and boiling points

Mei Hung Chiu, Hongming Leonard Liaw, Yuh Ru Yu, Chin Cheng Chou

Research output: Contribution to journalArticle

Abstract

Utilizing facial recognition technology, the current study has attempted to predict the likelihood of student conceptual change with decision tree models based on the facial micro-expression states (FMES) students exhibited when they experience conceptual conflict. While conceptual change through conceptual conflicts in science education is a well-studied field, there is little research done on conceptual change through conceptual conflict in terms of students' facial expressions. As facial expressions are one of the most direct and immediate responses one can get during instruction and that facial expressions are often representations student's emotions, a link between students' FMES and learning was explored. Facial data was collected from 90 tenth graders. Only data from the 72 students who made incorrect predictions were analyzed in this study. The concept taught was the relationship between boiling point and air pressure. Through facial recognition software analysis and decision tree models, the current study found Surprised, Sad and Disgusted to be key FMES that could be used to predict student conceptual change in a conceptual conflict-based scenario.

Original languageEnglish
Pages (from-to)469-480
Number of pages12
JournalBritish Journal of Educational Technology
Volume50
Issue number1
DOIs
Publication statusPublished - 2019 Jan

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air
facial expression
student
students' representation
emotion
scenario
instruction
science
learning
education
experience

ASJC Scopus subject areas

  • Education

Cite this

Facial micro-expression states as an indicator for conceptual change in students' understanding of air pressure and boiling points. / Chiu, Mei Hung; Liaw, Hongming Leonard; Yu, Yuh Ru; Chou, Chin Cheng.

In: British Journal of Educational Technology, Vol. 50, No. 1, 01.2019, p. 469-480.

Research output: Contribution to journalArticle

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