Analyzing theories related to managing crowded classes and presenting an optimal model with a fuzzy approach

Document Type : Original Article (Mixed)

Authors

1 PhD student in Educational Management, Department of Educational Sciences, Urmia Branch, Islamic Azad University. Urmia. Iran

2 Assistant Professor, Department of Educational Sciences, Urmia Branch, Islamic Azad University, Urmia, Iran

3 Professor of Educational Management, Urmia University, Urmia, Iran

Abstract
The aim of the present study is to analyze theories related to the management of large classes and to present an optimal model with a fuzzy approach. This research is of applied research type in terms of purpose and nature and mixed (qualitative-quantitative) in terms of implementation. In the first part of the study, in order to develop a comprehensive model of analyzing theories related to the management of large classes, the method of comparative content analysis was used to review domestic and foreign studies over the past two decades. The samples were selected purposefully and based on theoretical saturation, and the intra-subject agreement rate was 0.92. The data were processed and analyzed using the content analysis method. In the second part of the study, which is a survey, the mathematical modeling method (fuzzy expert system) was used to present the optimal combination of model dimensions so that large classes can be managed more effectively and optimally. The results of this study showed that the proposed model of large class management, by emphasizing key factors such as creating a supportive environment, positive and constructive interactions, shaping a self-regulatory environment, developing metacognitive skills, goal setting, and accountability, can help create more effective and sustainable learning environments. The findings of this study can be used as a guide for educational policies and designing classroom management improvement programs, and in particular, help teachers and educational administrators to effectively implement appropriate strategies for optimal management of large classes.

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  • Receive Date 30 July 2025
  • Revise Date 26 September 2025
  • Accept Date 08 November 2025