- Title
- Older women, deeper learning: age and gender interact to predict learning approach and academic achievement at university
- Creator
- Douglas, Heather E.; Rubin, Mark; Scevak, Jill; Southgate, Erica; MacQueen, Suzanne; Richardson, John T. E.
- Relation
- Frontiers in Education Vol. 5, no. 158
- Publisher Link
- http://dx.doi.org/10.3389/feduc.2020.00158
- Publisher
- Frontiers Research Foundation
- Resource Type
- journal article
- Date
- 2020
- Description
- 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 U.K. undergraduates. The Australian sample were 367 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 older 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.
- Subject
- academic achievement; age; conditional process analysis; gender differences; learning approach
- Identifier
- http://hdl.handle.net/1959.13/1426112
- Identifier
- uon:38367
- Identifier
- ISSN:2504-284X
- Rights
- © 2020 Douglas, Rubin, Scevak, Southgate, Macqueen and Richardson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Language
- eng
- Full Text
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