Academic performance prediction in a gender-imbalanced environment

Individual characteristics and informal social processes are among the factors that contribute to a student’s performance in an academic context. Universities can leverage this knowledge to limit drop-out rates and increase performance through interventions targeting at-risk students. Data-driven recommendation systems have been proposed to identify such students for early interventions. However, as we show in this paper, it is possible to identify certain groups of students whose performance is best predicted using indicators that differ from those predictive for the majority. Naïve approaches that do not account for this fact might favor the majority class and lead to disparate mistreatment in the case of minorities. In this paper we investigate the low academic performance predictors of female and male participants of the Copenhagen Networks Study. We find that social indicators (e.g. mean grade point average of peers or fraction of low-performing peers) predict lowperformance of male participants more accurately than they do for female participants, and that this situation is reversed for individual behaviors. Because of the gender imbalance among the participants, optimal gender-oblivious models detect low-performing male students with higher accuracy than low-performing female students. We review the existing approaches to addressing the disparate mistreatment problem and propose our own method that outperforms the alternatives on the dataset in question. ACM Reference format: Piotr Sapiezynski, Valentin Kassarnig, Christo Wilson, Sune Lehmann, and Alan Mislove. 2017. Academic performance prediction in a genderimbalanced environment. In Proceedings of FATRECWorkshop on Responsible Recommendation at ACM RecSys, Como, Italy, August 2017 (FATREC’17), 4 pages. https://doi.org/10.18122/B20Q5R

个人特征和非正式的社会过程是在学术环境中促进学生表现的因素，可以通过针对危险的学生来限制辍学率并增加绩效。最佳预测的指标与大多数人的预测性不同。与女性参与者相比，男性的表现低得多，并且由于参与者的性别失衡而扭转这种情况，与低表现的女性相比，与低表现的女性相比，符合性别的男性的最佳性别不平衡参考格式：Piotr Sapiezynski，Valentin Kassarnig，Christo Wilson，Sune Lehmann和Alan Mislove 22/b20q5r