Aligning Measurement of Personal Attributes for Predicting STEM Education Outcomes

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Scholars in personality psychology, economics, and child development report a variety of empirical relationships connecting personality traits, executive functions (EF), and economic preferences to enrollment and performance in science, technology, engineering, and math (STEM) courses in elementary and secondary education and choice of STEM majors in college. The similarities and differences in how these attributes are measured and their relationships to STEM education outcomes have not been rigorously examined. This interdisciplinary multi-country study will investigate the evolution and co-evolution of traits, EF skills, and preferences in children to determine how those attributes predict STEM achievement and the choice of STEM majors and careers. In particular, it will use a common measurement framework across samples from three sites in the United States and China, as well as data from the Australian Household, Income and Labour Dynamics Survey, to find out how personal attributes are related to STEM education achievement, college attainment and STEM major choice. It will provide a better understanding of age-specific windows of opportunity for interventions promoting STEM achievement and the pursuit of STEM majors. This study will produce core sets of distinct attributes to use in assessing students and schools, which will be useful as a diagnostic tool for teachers and school administrators to counsel students and to facilitate personalized learning. Students may reap the academic and social benefits of teaching interventions focused on the impact of traits, skills, and preferences. The main goal of this study is to identify the combination of traits, EF skills, and preferences for predicting STEM achievement (grades, test scores, teacher ratings, as well as choice of college major and career). To do this, first the study will generate a parsimonious measurement system of personal attributes by conducting linear and nonlinear factor analysis to determine the relationship and dimensionality of traits, EF skills, and preferences. Second, it will collect data on these personal attributes in different experimental conditions to quantify how incentives, frames and contextual factors affect their measurement, in order to propose standardized measures for personal attributes that predict STEM outcomes. Third, it will examine trait, skill, and preference growth longitudinally, spanning cohorts from 4th to 12th grade, for both mean change and change in the prediction of STEM outcomes. Finally, it will examine how background factors including geo-cultural settings moderate the relationship between STEM outcomes and traits, EF skills, and preferences. This study makes five major contributions to STEM education research: (1) The longitudinal measurement furthers our understanding of age-specific windows of opportunity for interventions promoting attributes that drive interest in STEM; (2) Examination of relationships across the elicited measures enables determination of which measurement strategies capture the same constructs, and which add genuine new dimensions to the study of individual differences. This study will use a common set of measures across countries; (3) For each construct, use of experimental manipulations enables investigation of the quantitative importance of standardizing for incentives and environments; (4) By studying the power of the various elicited, standardized measures for predicting STEM achievement, college attainment, and STEM major and career choices, this study will support schools and teachers to promote personalized education and improve student outcomes; and (5) The study of similarities and differences in measures across cultural, race, ethnicity, gender, and family background, allows for optimal prediction of STEM outcomes across distinct contexts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.