Predicting community college astronomy performance through logistic regression

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