Academic performance prediction in a gender-imbalanced environment
By Piotr Sapiezynski, Valentin Kassarnig, Christo Wilson. 2017
32 citations recorded by the source corpus.
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
Subfields: Computer Science Applications, Economics and Econometrics, Education
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
In Economics and Econometrics
In Education
Citations in the corpus
- Fairness Without Demographics in Repeated Loss Minimization (2018, background): “, 2016), part-of-speech tagging (Hovy & Sgaard, 2015), academic recommender systems (Sapiezynski et al., 2017), and automatic video captioning (Tatman, 2017).”