The Impact of Teachers' Classroom Behavior Management Strategies on Learning Behavior among Chinese Art Students

Previous studies have examined teachers' classroom behavior management strategies (TCBMS) and students' learning behavior (SLB) from a static perspective, particularly outside the realm of art education. This mixed-methods study aimed to comprehensively explore the detailed correlation between each dimension and the dynamic elements of TCBMS and SLB variables. Eighty-seven teachers and 453 students from Chinese art universities completed an online survey on TCBMS and SLB. Additionally, in-depth interviews with five art teachers were conducted to examine the dynamic aspects of learning behavior. Quantitative data were analyzed using structural equation modeling (SEM) to explain the impact of TCBMS on SLB. The research findings reveal that all aspects of TCBMS positively contribute to SLB. Three components of TCBMS were particularly dominant in improving SLB: feedback, the student-teacher relationship, and the use of traditional writing on the whiteboard. Teachers' feedback emerged as the most effective method for encouraging students' attention in art classroom learning. The study also examined how teachers provided input to their art students and the subsequent impact on their learning.

Authors

Institutions

Publication Details

Journal
Journal of Curriculum Studies Research
Published
2024-10-12
DOI
https://doi.org/10.46303/jcsr.2024.16
Citations
2
Primary Topic
Educational Environments and Student Outcomes
Type
article
Field-Weighted Citation Impact
4.19
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Impact of Teachers' Classroom Behavior Management Strategies on Learning Behavior among Chinese Art Students

Yudhi Arifani, Disi He, Yaoping Liu, Sri Suryanti et al.
2 citations
Journal of Curriculum Studies Research
Educational Environments and Student Outcomes
4.19
article

The Impact of Teachers' Classroom Behavior Management Strategies on Learning Behavior among Chinese Art Students

Yudhi Arifani, Disi He, Yaoping Liu, Sri Suryanti, Saifon Songsiengchai, Wannaporn Siripala
article en
2 citations

Abstract

Previous studies have examined teachers' classroom behavior management strategies (TCBMS) and students' learning behavior (SLB) from a static perspective, particularly outside the realm of art education. This mixed-methods study aimed to comprehensively explore the detailed correlation between each dimension and the dynamic elements of TCBMS and SLB variables. Eighty-seven teachers and 453 students from Chinese art universities completed an online survey on TCBMS and SLB. Additionally, in-depth interviews with five art teachers were conducted to examine the dynamic aspects of learning behavior. Quantitative data were analyzed using structural equation modeling (SEM) to explain the impact of TCBMS on SLB. The research findings reveal that all aspects of TCBMS positively contribute to SLB. Three components of TCBMS were particularly dominant in improving SLB: feedback, the student-teacher relationship, and the use of traditional writing on the whiteboard. Teachers' feedback emerged as the most effective method for encouraging students' attention in art classroom learning. The study also examined how teachers provided input to their art students and the subsequent impact on their learning.

Journal of Curriculum Studies ResearchVol. 6(2)
State University of Semarang (ID), Universitas Negeri Surabaya (ID), Rajamangala University of Technology Krungthep (TH)
Quality Education
Openalex Percentile: Top 6%
Educational Environments and Student Outcomes
4.19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.