Learning approaches as predictors of academic performance in first year health and science students

Abstract

Aims: To compare health and science students' demographic characteristics and learning approaches across different disciplines, and to examine the relationship between learning approaches and academic performance.

Background: While there is increasing recognition of a need to foster learning approaches that improve the quality of student learning, little is known about students' learning approaches across different disciplines, and their relationships with academic performance.

Design: Prospective, correlational design.

Methods: Using a survey design, a total of 919 first year health and science students studying in a university located in the western region of Sydney from the following disciplines were recruited to participate in the study - i) Nursing: n = 476, ii) Engineering: n = 75, iii) Medicine: n = 77, iv) Health Sciences: n = 204, and v) Medicinal Chemistry: n = 87.

Results: Although there was no statistically significant difference in the use of surface learning among the five discipline groups, there were wide variations in the use of deep learning approach. Furthermore, older students and those with English as an additional language were more likely to use deep learning approach. Controlling for hours spent in paid work during term-time and English language usage, both surface learning approach (β = -0.13, p = 0.001) and deep learning approach (β = 0.11, p = 0.009) emerged as independent and significant predictors of academic performance.

Conclusions: Findings from this study provide further empirical evidence that underscore the importance for faculty to use teaching methods that foster deep instead of surface learning approaches, to improve the quality of student learning and academic performance.

Keywords: Academic achievement; First year undergraduate students; Health and science education; Higher education; Learning approaches.