Gender: Male/Female

Kai et al. (2017) pdf

Models predicting student retention in an online college program
J48 decision trees achieved significantly lower Kappa but higher AUC for male students than female students
JRip decision rules achieved much lower Kappa and AUC for male students than female students

Hu and Rangwala (2020) pdf

Models predicting if a college student will fail in a course
Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against male students, performing particularly better for Psychology course.
Other models (Logistic Regression and Rawlsian Fairness) performed far worse for male students, performing particularly worse in Computer Science and Electrical Engineering.

Anderson et al. (2019) pdf

Models predicting six-year college graduation
False negatives rates were greater for male students than female students when SVM, Logistic Regression, and SGD were used

Gardner, Brooks and Baker (2019) [pdf]

Model predicting MOOC dropout, specifically through slicing analysis
Some algorithms studied performed worse for female students than male students, particularly in courses with 45% or less male presence

Riazy et al. (2020) [pdf]

Model predicting course outcome
Marginal differences were found for prediction quality and in overall proportion of predicted pass between groups
Inconsistent in direction between algorithms.

Lee and Kizilcec (2020) [pdf]

Models predicting college success (or median grade or above)
Random forest algorithms performed significantly worse for male students than female students
The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values

Yu et al. (2020) [pdf]

Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
Female students were inaccurately predicted to achieve greater short-term and long-term success than male students.
The fairness of models improved when a combination of institutional and click data was used in the model

Yu and colleagues (2021) [pdf]

Models predicting college dropout for students in residential and fully online program
Whether the protected attributed were included or not, the models had worse true negative rates but better recall for male students
The model was worse for male students studying in online program in terms of true negative rates, recall and accuracy.

Models predicting course outcome of students in a virtual learning environment (VLE)
More male students were predicted to pass the course than female students, but this overestimation was fairly small and not consistent across different algorithms
Among the algorithms, Naive Bayes had the lowest normalized mutual information value and the highest ABROCA value

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