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 (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.
Highlights
Collaboration is one of the most important twenty-first-century skills
Before using SEM trees to determine the most significant predictors, we compared the fits of the two confirmatory factor analysis models (Figure 1) to determine the temperate model of the SEM trees and forests
Demographic SEM Tree Seventeen demographic variables were included in the SEM tree analysis with the two-factor model as the template model to determine their priority in terms of the impact on students’ attitudes toward collaboration and to explore the heterogeneity of students
Summary
Collaboration is one of the most important twenty-first-century skills. A series of studies revealed that collaboration could enhance team cohesion, generate positive evaluations of partners, and improve performance (Mishra et al, 2015; Chen and Agrawal, 2017; Aldieri et al, 2018). To better improve people’s collaborative performance, it is worth noting that collaboration involves much more than physically gathering together to discuss issues or share information among team participants. It relies heavily on team members’ involvement, attitude, and commitment regarding interacting with each other (Wu et al, 2013). Team members’ attitudes toward collaboration are important for successful collaboration (OECD, 2017a)
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