STEM-R: Modeling STEM Retention and Departure across Physics, Mathematics, and Engineering

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Researchers at West Virginia University will develop a new theoretical framework for STEM departure that will detail the reasons why students leave STEM majors. The research extends Tinto's university departure model to include the career exploration process where a student leaves STEM but remains in college. The framework will be developed and tested by extensive measurement of demographic, social, academic, affective (self-efficacy, self-esteem, belonging), career exploration/aspirations and psychological variables at four longitudinal points in physics and mathematics introductory class sequences required for many STEM majors. The longitudinal measurement monitors evolution of a student's STEM career commitment and vocational identity, and determines factors that influence a decision to begin the process of STEM departure or lead to a well-investigated resolution to pursue a STEM career. The research will explore crucial questions influencing university STEM departure, including to what extent departure is preventable by modifying advising protocols, to what extent performance in these gate-keeper classes and its effect on self-efficacy influence the decision to change major, what psychological/social factors mark students beginning to explore non-STEM careers, and to what extent belonging influences retention, particularly of underrepresented women and rural students. The research will contribute to a deeper understanding of this important phenomenon. It also will inform the national discussion of STEM retention by producing a theoretical framework of STEM departure validated across a large, economically diverse pool of students. The research design will implement a multi-stage analysis and measurement with the goal of modeling the process of STEM departure and quantifying the reasons for and markers of the departure process. Researchers will investigate in depth: (1) what modifications are needed in theoretical frameworks for college departure to explain intra-university departure from STEM disciplines, (2) the minimum information required to predict STEM departure and where STEM departure originates, (3) what fraction of STEM departure could be prevented by interventions, (4) how STEM departure markers differ for underserved populations, and (5) how to identify subpopulations of students where interventions will be most effective. Researchers will use institutional class-taking and outcome data to build a set of "survival" probabilities for individual classes and class-taking sequences. Linear regression analysis then be used to analyze how these probabilities are affected by (1) the student's intrinsic characteristics (ability measured by ACT/SAT, race and ethnicity, socioeconomic status, first generation status, and personality profile), (2) the student's academic preparation (high school GPA and high school course-taking patterns), (3) the student's academic performance in his or her current class (measured by attendance patterns and assignment grades), (4) the students current social connectedness to both family and university structures and communities, and (5) the student's current affective state, his or her self-efficacy, sense of belonging, self-esteem, and STEM identity. Latent growth modeling will track changes in these factors over time. In parallel, the student's vocational identity status, his or her state of career exploration and career decision making, will be monitored. Using the four-point longitudinal measurement, transitions in the student's vocational identity state (particularly changes that indicate a renewed career exportation process and therefore the threat of STEM departure) will be correlated with previous changes in survival probabilities and with changes in the student's affective, academic, or social state. Signatures to inform advisers of at risk students and general changes to advising protocols will be developed. Campus social/academic structures that promote retention will be identified. At all levels, differential results for underrepresented students will be investigated with the goal of designing interventions that target these populations.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in three areas: STEM learning and learning environments, broadening participation, and workforce development.