Abstract

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees and structural equation model forests were applied to the Program for International Student Assessment 2015 dataset. By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources, home possessions, mother's education, and gender were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation and sense of belonging at school were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.