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Exploration of Variables Predicting Sense of School Belonging Using the Machine Learning Method-Group Mnet.
Hyo Jin Lim1, Jin Eun Yoo2, Minjeong Rho2
1Seoul National University of Education, Seoul, Korea.
Psychological Reports
|October 11, 2022
Summary
This study identified key factors influencing school belonging using machine learning. Student motivation, parental support, and school satisfaction significantly predict a positive sense of belonging in students.
Area of Science:
- Educational Psychology
- Machine Learning in Social Sciences
Background:
- Traditional methods often analyze limited variables for school belonging.
- A holistic approach is needed to understand the multifaceted nature of school belonging.
Purpose of the Study:
- To explore a comprehensive set of variables associated with school belonging.
- To apply machine learning for identifying predictors in a single, complex model.
Main Methods:
- Utilized 2015 Program for International Student Assessment (PISA) data.
- Employed the group Mnet machine learning technique to analyze 504 potential predictors.
- Conducted 100 rounds of model building with random data splitting.
Main Results:
- Identified 32 significant variables related to school belonging.
- Key predictors include individual/parent factors (motivation, cooperative learning, parental support) and school factors (satisfaction, peer/teacher relationships, activities).
Conclusions:
- Machine learning offers a powerful, holistic approach to understanding school belonging.
- Findings highlight the interplay of individual, familial, and school environmental factors.
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