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
• Fairly 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.

Riazy et al. (2020) [[pdf](https://www.scitepress.org/Papers/2020/93241/93241.pdf)]

• 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 by slight difference and this overestimation was not consistent across different algorithms
• Among the algorithms, Naive Bayes had the lowest normalized mutual information value and the highest ABROCA value, or differences between the area under curve