Gender: Male/Female
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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 course grades and average GPA
- female students were generally inaccurately predicted to perform better than male students
Yu and colleagues (2021) [pdf]
- Model predicting college dropout
- Worse true negative rates for male students, but somewhat better recall for male students taking courses in-person