A Multi-Method Approach to Understanding Dropout from STEM Gateway Courses
The research reported here was supported by the US National Science Foundation, through award #0814901 to the University of Illinois, Urbana-Champaign. The opinions expressed are those of the authors and do not represent views of the National Science Foundation.
Why do undergraduate students drop out of STEM majors during or after “gateway” courses? We have used a novel set of cognitive/motivational variables and a multimethod research design to study the process of undergraduate students’ dropout from these courses at Temple University, where such gateway courses enroll 60% or more non-White students. First, much of the prior research on loss from the STEM pipeline considered either cognitive predictors (e.g., science knowledge) or motivational predictors (e. g., self-efficacy), but the combination of cognitive and motivational variables has only rarely been investigated. Second, we have used several different motivational theories: Dweck’s (1999) entity/incremental theory of achievement motivation, Steele and colleagues’ stereotype threat model, and Hofer’s epistemological beliefs model. Third, we have interviewed a purposive sample of students early and late in the semester in order to better understand students’ own reasons for dropout/retention decisions and their relationship to entity/incremental beliefs. Fourth, we have followed up with students as they continue in (or drop out of) STEM majors, and to will follow these students’ dropout status over the course of four years, allowing for a longitudinal examination (using the statistical technique of survival analysis) of the effect of cognitive and motivational variables at intake on students’ persistence.
Initial Data Collection. Over the course of three years of research, we have collected data from three annual cohorts of first-year STEM students in Temple fall chemistry (N = 600 per year) and spring biology (N = 300 per year) courses. We have collected paper-and-pencil data on cognitive (e.g., background knowledge, inference/reasoning) and motivational (entity/incremental) predictors of dropout/persistence. We then selected—based on those predictors—20 students likely to persist and 20 students likely to drop out from each course for each of their first two semesters, and conducted semi-structured 40 min interviews with a purposive sample from each group. The second interviews occur within one week after students learn their grades on a mid-semester exam. The focus of the interviews is on students’ perceptions of how they are doing in the course, reasons why they believe they are performing at that level (including contrasts with demands of high school courses), and participants’ own prediction of how likely they are to pursue a STEM major through to graduation.
Follow-up Longitudinal Data Collection. We have administered follow-up questionnaires to determine, at the end of each of 6 semesters, whether participants are still a STEM major and also to collect information on GPA and entity/incremental beliefs. (For all cohorts we will also request permission to check university records through the end of the grant period; for our Fall, 2011 cohort we only followed up through semester 2). Quantitative analyses use the statistical technique of survival analysis to assess the strength of effects of cognitive and motivational predictors at the beginning of semester 1 to STEM dropout (or retention). Previous research has treated dropout as a one-time event and has mostly focused on motivation or cognition; by combining quantitative and qualitative data and modeling dropout over time we have gained a much better and richer understanding of loss from the STEM pipeline.
Follow-up “Exit” Interviews. We are interviewing January, 2012 and may, 2012 graduates who participated in the questionnaire portion of the study--both those who are graduating in STEM majors and those who switched out of STEM. Interviews will focus on the decision-making process regarding staying in or leaving a STEM major.