Interactions of gender with predictors of academic achievement

Abstract

Predictive models of academic achievement are used in various (often high stakes) applications, including selection and study orientation procedures for higher education. Considering the far-reaching consequences of their outcomes, these models should show as little bias for irrelevant factors as possible. While numerous studies have researched the impact of gender on the isolated individual predictors of academic achievement, no studies yet have explored how gender affects program-specific prediction models of academic achievement. As such, the present study examined whether prediction models exhibit gender differences in the accuracy of their predictions, and how such differences relate to the gender balance within a study program. Besides that, we developed gender-specific prediction models of academic achievement in order to examine how these models differ in terms of which predictors are included, and whether they make more accurate predictions. Data was examined from a large sample of first year students across 16 programs in an open access higher education system (N = 5,016). Results revealed interactions between gender and several predictors of academic achievement. While the models exhibited little difference in the accuracy of their predictions for male and female students, analyses showed that using gender-specific models substantially improved our predictions. We also found that male and female models of academic achievement differ greatly in terms of the predictors included in their composition, irrespective of the gender balance in a study program.