Assessing college student interest in math and/or computer science in a cross-national sample using classification and regression trees

Abstract

The purpose of this exploratory study was to assess the relative importance of a number of variables in predicting students’ interest in math and/or computer science. Classification and regression trees (CART) were employed in the analysis of survey data collected from 276 college students enrolled in two U.S. and Greek universities. The results revealed that American students reporting high levels of barrier coping self-efficacy tended to show more interest in these fields. American students, however, with low barrier coping self-efficacy, low social or family influences, and low levels of self-efficacy for learning showed the least interest in math and/or computer science. In Greek students, the highest interest in math and/or computer science was observed among those whose parents had high expectations, expressed high barrier coping self-efficacy, and found mathematics to be useful. Overall, lower parental expectations and limited access to role models or mentors decreased their interest in these fields of study. Educational implications are discussed.