Understanding academic performance and progression of first-year accounting and business economics undergraduates: the role of approaches to learning and prior academic achievement

Abstract

Previous research indicates that age, gender, and prior academic achievement have direct effects on students' approaches to learning and their academic performance and progression. Here an investigation is reported which explores the relationships between 60 first-year undergraduate accounting and business economics students' approaches to learning, their age, gender, prior academic achievement, and their subsequent academic performance and progression. Linear regression analyses identified the strongest predictor of first-year academic performance and progression as prior academic achievement (i.e. performance in school examinations). Cluster analysis of the six dimensions of the RASI, academic performance and progression revealed two clusters. The first cluster (labelled ‘effective learner’) had a 75.0% rate of progression; the second (labelled ‘ineffective learner’) had only an 11.7% rate of progression. ‘Effective learner’ scores high on Deep Approach and low on Surface Approach, while the ‘ineffective learner’ scores low on Deep Approach and high on Surface Approach.

Keywords:

• students' approaches to learning (SAL)
• Revised Approaches to Studying Inventory (RASI)
• academic performance
• progression
• prior academic achievement

Notes

Eta² is a measure of effect size. In analysis of variance, Cohen (1988) indicates 1% is ‘small’; 6% is ‘medium’; and 14% ‘large’.

Logistic regression is similar to linear regression, except that the dependent variable is dichotomous. It requires far fewer assumptions than discriminant analysis and performs well even when the assumptions for discriminant analysis are met (Hosmer and Lemeshow, 1989).