Analysis and Prediction of Classroom Silence Reasons Using Bayesian Networks
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Abstract
Classroom silence is a prevalent but seldom studied in depth phenomenon in educational environments, and the reasons behind it are complex and varied, directly affecting teaching effectiveness and students' motivation. This study aims to systematically analyze and predict the main reasons for students' silence in different teaching environments, including individual psychological factors, the quality of classroom interactions, and the difficulty of teaching content, by constructing and applying a Bayesian network model. In this study, firstly, based on the theory of educational psychology, key variables affecting students' classroom participation were selected, data were collected through questionnaires, and then data were analyzed using Bayesian networks, presenting causality and probabilistic inference, and finally realizing dynamic prediction of the phenomenon of silence in the classroom. At T2, the model predicted the probability of silence was 0.85, and after the teacher changed the topic, the probability of silence decreased to 0.60, and the actual length of silence was reduced from 10 to 7 minutes, indicating that changing the topic was an effective strategy. The findings of the study not only provide teachers with practical tools to identify and reduce silence in the classroom, but also provide data support for educational policy makers to optimize teaching strategies and improve the quality of education in order to motivate and engage students.
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