Predictive models of academic achievement are used in various (often high stakes) applications, including selection and study orientation procedures for higher education. Considering the far-reaching consequences of their outcomes, these models should show as little bias for irrelevant factors as possible. While numerous studies have researched the impact of gender on the isolated individual predictors of academic achievement, no studies yet have explored how gender affects program-specific prediction models of academic achievement. As such, the present study examined whether prediction models exhibit gender differences in the accuracy of their predictions, and how such differences relate to the gender balance within a study program. Besides that, we developed gender-specific prediction models of academic achievement in order to examine how these models differ in terms of which predictors are included, and whether they make more accurate predictions. Data was examined from a large sample of first year students across 16 programs in an open access higher education system ( = 5,016). Results revealed interactions between gender and several predictors of academic achievement. While the models exhibited little difference in the accuracy of their predictions for male and female students, analyses showed that using gender-specific models substantially improved our predictions. We also found that male and female models of academic achievement differ greatly in terms of the predictors included in their composition, irrespective of the gender balance in a study program.

中文翻译：

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性别与学业成绩预测因素的相互作用

学业成绩的预测模型用于各种（通常是高风险）应用，包括高等教育的选拔和学习定向程序。考虑到其结果的深远影响，这些模型应该尽可能减少对不相关因素的偏见。尽管大量研究研究了性别对学业成绩的孤立个体预测因素的影响，但尚无研究探讨性别如何影响特定于学业成绩的预测模型。因此，本研究检查了预测模型在预测准确性方面是否表现出性别差异，以及这些差异与研究计划中的性别平衡有何关系。除此之外，我们还开发了针对学业成绩的性别预测模型，以研究这些模型在包​​含哪些预测因素方面有何不同，以及它们是否做出更准确的预测。数据来自开放式高等教育系统 16 个项目的大一年级学生样本 (= 5,016)。结果揭示了性别与学业成绩的几个预测因素之间的相互作用。虽然这些模型对男女学生的预测准确性几乎没有差异，但分析表明，使用特定性别的模型大大改善了我们的预测。我们还发现，无论研究项目中的性别平衡如何，男性和女性的学业成就模型在其构成中包含的预测因素方面存在很大差异。