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

Christie et al. (2019) pdf

• Models predicting student's high school dropout
• The decision trees showed very minor differences in AUC between female and male 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 from 0.5 to group-specific 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 socio-demographic information was included or not, the model showed worse true negative rates for male students, and worse accuracy if they are studying online
• The model showed better recall for male students, especially for those studying in person
• The difference in recall and true negative rates were lower, and thus fairer, for male students studying online if their socio-demographic information was not included in the model

Riazy et al. (2020) 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 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

Bridgeman et al. (2009) pdf

• Automated scoring models for evaluating English essays, or e-rater
• E-Rater system performed comparably accurately for male and female students when assessing their 11th grade essays

Bridgeman, Trapani, and Attali (2012) pdf

• A later version of automated scoring models for evaluating English essays, or e-rater
• The score difference between human rater and e-rater was marginal when written responses to GRE issue prompt by male and female test-takers were compared
• The difference in score was significantly greater when assessing written responses to GRE argument prompt, as e-rater gave lower score for male test-takers, particularly for African American, American Indian, and Hispanic males, when assessing written responses to GRE argument prompt