Predicting community college astronomy performance through logistic regression

The present study examined demographic and academic predictors of astronomy performance among a cohort of $N=1909$ community college students enrolled in astronomy courses in a large suburban community college during a four-year time frame, 2015–2019. The theoretical framework was based upon a deconstructive approach for predicting community college performance, whereby students’ academic pathways through higher education institutions are examined to understand their dynamic interaction with institutional integration and progress toward academic goals. Transcript data analysis was employed to elicit student demographics and longitudinal academic coursework and performance. A logistic regression model was generated to identify significant predictors of astronomy performance, which included mathematics achievement, enrollment in remedial mathematics, and enrollment in multiple astronomy courses. The results imply a greater focus on mathematics preparation and performance may mediate astronomy outcomes for community college students. Notably, demographic variables including ethnicity, socioeconomic status, gender, and age were not significant predictors of astronomy performance in the multivariable model, suggesting the course is a potential gateway for diversifying science, technology, engineering, and mathematics access. Also, astronomy interest, as measured by enrollment in multiple astronomy courses, was related to performance. Further implications for practice are discussed.

Figure 1

Grade distributions for astronomy courses.