Exploration of Variables Predicting Sense of School Belonging Using the Machine Learning Method—Group Mnet

Abstract

The purpose of this study was to explore variables related to school belonging from a holistic perspective, including a large number of variables in one model, different to the traditional analytical method. Using 2015 data from the Program for International Student Assessment (PISA), we sought to identify variables related to school belonging by searching for hundreds of predictors in one model using the group Mnet machine learning technique. The study repeated 100 rounds of model building after random data splitting. After exploring 504 variables (384 student and 99 parent), 32 variables were finally selected after selection counts. Variables predicting a sense of school belonging were categorized as individual/parent variables (e.g. motivation to achieve, tendency to cooperative learning, parental support) and school-related variables (e.g. school satisfaction, peer/teacher relationship, learning/physical activities). The significance and implications of the study as well as future research topics were discussed.

Exploring Predictors of School Belonging in Senior High School Students

“…When interactions among students are of poor quality, such as being ignored or even bullied, it can make students feel like they do not belong to their school (Han et al, 2021). Conversely, students feel more belonging to school when peers can provide support, are often involved in conversations among friends, are invited to play games, help when they are in trouble, and share their complaints (Lim et al, 2022). Thus, this study supports the statement of Allen, et al (2018) that the quality of friendship relationships with peers can affect how students feel a part of their school.…”