Effects of Sharing Data With Teachers on Student Physical Activity and Sedentary Behavior in the Classroom

Abstract

Background: Data-driven decision making is an accepted best practice in education, but teachers seldom reflect on data to drive their physical activity (PA) integration efforts. The purpose of this study was to explore the impact of a data-sharing intervention with classroom teachers on teacher-directed movement integration and students’ PA and sedentary behavior. Methods: Teacher-directed movement behaviors from 8 classroom teachers in 1 primary school were systematically observed during four 1-hour class periods before (pre) and after (post) an intervention in which teachers individually discussed student movement data with a trained interviewer. Teachers’ K–2 students (N = 132) wore accelerometers for 10 school days both preintervention and postintervention. Results: Multilevel mixed effects regression indicated a nonsignificant increase in teacher-directed movement from preintervention to postintervention (+7.42%, P = .48). Students’ classroom time spent in moderate to vigorous PA increased (males: +2.41 min, P < .001; females: +0.84 min, P = .04) and sedentary time decreased (males: −9.90 min, P < .001; females: −7.98 min, P < .001) postintervention. Interview data inductively analyzed revealed teachers’ perspectives, including their surprise at low student PA during the school day. Conclusions: Findings suggest that sharing data with classroom teachers can improve student PA and decrease sedentary behavior at school.

A Protocol for a Local Community Monitoring and Feedback System for Physical Activity in Organized Group Settings for Children

“…Previous setting-based studies have used monitoring and feedback as intervention components to improve PA in children. 15,[29][30][31][32] However, these primarily have been single-setting efforts focused on the school and after-school program settings, and a recent scoping review found that a majority of existing whole-of-community population health improvement interventions that are focused on improving cancer risk factors in youth, including PA, do not include monitoring and feedback systems. 33 The present protocol extends setting-based efforts by identifying both in-school and out-of-school organized group settings available across the whole-of-community, collecting data from these settings, and feeding this data back to a community hub.…”

A Convolutional Neural Network (CNN) Based Approach for the Recognition and Evaluation of Classroom Teaching Behavior

“…Teachers' classroom teaching behavior refers to all the interactions between teachers and students in the classroom and the behavior related to teaching and learning. Since teachers and students are the composite subjects of educational activities (including teaching), based on the above definitions of experts, classroom teaching behavior is the sum of teachers' teaching, students' learning, and other related behaviors in the classroom environment to achieve common mathematical goals [8]. Based on the above classification results, its characteristics are extracted first to realize the recognition and evaluation of classroom teaching behavior.…”

“…In the formula, E i represents the Lagrange multiplier. Furthermore, the classroom teaching behavior characteristic data exist in the SVM hyperplane, and the characteristic sample data are expressed as [8]…”