Predicting success in first-year university accounting using gender-based learning analysis

Abstract

This study investigates whether secondary school accounting, mathematics and Tertiary Entrance score have any effect on the deep learning and surface learning components of the first-year university accounting examination. In addition, it examines whether these school factors have same relative predictive value in explaining the performance of male and female accounting students. Results show that while they were relevant in explaining success in the surface learning component, none of the perdictors had any significant predictive power for the deep learning component. Results also indicate that the performance of males was best explained by school accounting. On the other hand, the effect of general academic ability on female performance was greater than that of specific cognate subjects.