Older Women, Deeper Learning: Age and Gender Interact to Predict Learning Approach and Academic Achievement at University

Older students have reported a series of barriers to starting their higher education experience. However, quantitative evidence suggests that older students, particularly older women, have unique approaches to learning that enhance their satisfaction with their higher education studies. The current study sought to extend these findings by quantitatively examining an interaction between age and gender in predicting approaches to learning and subsequent academic achievement. The research provided an original analysis from two previous studies. Participants consisted of Australian and United Kingdom undergraduates. The Australian samples were 367 undergraduates at an Australian public regional university. The United Kingdom samples were 2,163 undergraduates enrolled with a distance higher education provider. Participants completed a research survey either online or on paper. Consistent with previous research, age moderated the effect of gender on deep learning, such that gender predicted deep learning more strongly among older students than younger students in both samples. Furthermore, gender predicted achievement in both samples, such that women out-performed men. Finally, deep learning only explained the relationship between gender and academic achievement when students were older. Based on this evidence, higher education institutions should consider and address the barriers that adult students, particularly older women, experience in order to enhance the social mobility benefits from a university degree that this non-traditional higher education group accrues.

Abstract licence: CC BY

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