An Analysis of Annual Changes on the Determining Factors for Teacher Attachment with Random Forest

Abstract

Teacher attachment is one of the most important factors for students' mental health, adaptation, and life satisfaction in their school life. Therefore, a lot of studies have been conducted to find affective variables in teacher attachment, but they have suffered to explain data consistently due to the limitation of the cross-sectional data and the conventional statistical methods. In the research, to resolve these issues, we adopt a data mining method named Random Forest on the longitudinal data spanning over five years. From the results, the variables related studying are the most affective ones in teacher attachment as students grow.