Developing a plugged-in class observation protocol in high-school blended STEM classes: Student engagement, teacher behaviors and student-teacher interaction patterns

Jo Chi Hsiao, Ssu Kuang Chen, Wei Chen, Sunny S.J. Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

To investigate how high school students engaged in blended STEM courses, this study developed a new digital observation protocol, Classroom Observation Protocol for Interactive Engagement in STEM (COPIE-STEM) to observe patterns of behaviors between teachers and students. We also distinguished the data according to two common types of STEM programs (i.e., mainly hands-on versus mainly lecture). In the present study, a case study design was used to validate this COPIE-STEM. It was able to code eight teacher behaviors classified into three categories: Student-Centered, Teacher-Centered, and Transitional activities; and 23 student behaviors of Interactive, Constructive, Active, and Passive engagement based on the ICAP framework (Chi, 2009). Interrater reliability was satisfactory. Observational data collected from 25 classes showed that students were most engaged in Active activities and less in Constructive and Interactive activities. When students engaged in Interactive, Constructive and Active activities, teachers' activities were mostly Student-Centered. In contrast, when students showed Passive Engagement behaviors, teachers were mostly engaged in Teacher-Centered educational activities. Using social network analysis to process each student's data, we created graphs of student-teacher associations and found four types of student-teacher behavior associations: Constructive with Extensive Interaction (C-EI), Constructive with Few Interactions (C-FI), Constructive Engagement (CE) and Active Engagement (AE). In hands-on courses, there were more C-EI patterns, while in lecture courses, CE and C-FI patterns dominated. The results of social network analysis showed there was more and diverse co-occurrence of teacher-student behaviors in the C-EI patterns, and students' behavioral nodes were more closely related to teachers' behavioral nodes in the C-EI patterns in both hands-on and lecture courses. Finally, student interaction was limited and constrained in the lecture courses, whereas in the hands-on courses, student interaction was more frequent and less constrained by seating arrangement. The findings suggest that high school teachers should design tasks that allow students to stay engaged in deeper learning.

Original languageEnglish
Article number104403
JournalComputers and Education
Volume178
DOIs
StatePublished - Mar 2022

Keywords

  • Data science applications in education
  • Evaluation methodologies
  • Improving classroom teaching
  • Interdisciplinary projects
  • Secondary education

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