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.

中文翻译：

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老年女性、更深入的学习和更大的大学满意度：年龄和性别预测大学生的学习方法和学位满意度。

本研究探讨了年龄和性别在预测表面和深度学习方法中的交互作用。它还调查了这些变量如何与学位满意度相关。参与者是澳大利亚一所大型公立大学的 983 名本科生。他们在网上或纸上完成了一项研究调查。与之前的研究一致，年龄是表面学习和深度学习的积极预测因素。然而，在深度学习的情况下，性别缓和了这种年龄效应：年龄对女性而非男性的深度学习预测更强烈。此外，年龄对女性的学位满意度有积极的预测，但在男性中则不然，而深度学习调节了这种调节效应。因此，在本样本中，年龄较大的女学生表现出最大的深度学习，这种影响解释了他们对学位的更大满意度。考虑了这些发现对教学实践和制度政策的影响。