Older women, deeper learning, and greater satisfaction at university

age and gender predict university students' learning approach and degree satisfaction

Mark Rubin*, Jill Scevak, Erica Southgate, Suzanne Macqueen, Paul Williams, Heather Douglas

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

The present study explored the interactive effect of age and gender in predicting surface and deep learning approaches. It also investigated how these variables related to degree satisfaction. Participants were 983 undergraduate students at a large public Australian university. They completed a research survey either online or on paper. Consistent with previous research, age was a positive predictor of both surface and deep learning. However, gender moderated this age effect in the case of deep learning: Age predicted deep learning more strongly among women and not among men. Furthermore, age positively predicted degree satisfaction among women but not among men, and deep learning mediated this moderation effect. Hence, older female students showed the greatest deep learning in the present sample, and this effect explained their greater satisfaction with their degree. The implications of these findings for pedagogical practices and institutional policy are considered.

Original languageEnglish
Pages (from-to)82-96
Number of pages15
JournalJournal of Diversity in Higher Education
Volume11
Issue number1
Early online date5 Sep 2016
DOIs
Publication statusPublished - Mar 2018

Keywords

  • Deep learning
  • Degree satisfaction
  • Gender differences
  • Learning approach
  • Surface learning

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