Predicting Students' Attitudes Toward Collaboration: Evidence From Structural Equation Model Trees and Forests

Frontiers in Psychology

Jialing Li¹  Feifei Huang¹  Wei Shao¹  Yixing Li¹  Minqiang Zhang² 

[1] School of Psychology, South China Normal University, Guangzhou, China;School of Psychology, South China Normal University, Guangzhou, China;Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China;Center for Studies of Psychological Application, South China Normal University, Guangzhou, China;Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China;

关键词: attitudes toward collaboration;    person-centered;    most significant factors;    PISA 2015;    structural equation model tree;    structural equation model forest;    data mining;   

DOI  :  10.3389/fpsyg.2021.604291

来源: Frontiers

【 摘 要 】

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 (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). 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 (split by “above-average or not”), home possessions (split by “disadvantaged or not”), mother's education (split by “below high school or not”), and gender (split by “male or female”) were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by “above-average or not”) and sense of belonging at school (split by “above-average or not” and “disadvantaged or not”) 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.

【 授权许可】

CC BY