Academic performance prediction in a gender-imbalanced environment

Como, Italy

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.