Gender: Male/Female

Kai et al. (2017) pdf

• Models predicting student retention in an online college program
• performance was very good for both groups
• JRip decision tree model performed more equitably than a J48 decision tree model for both male and female students.
• JRip model had moderately better performance for female students than male students

Hu and Rangwala (2020) pdf

• Models predicting if student at-risk for failing a course
• Performed worse for male students, but that this result is inconsistent across university courses

Anderson et al. (2019) pdf

• Models predicting six-year college graduation
• Algorithms had higher false negative rates for male students

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]

• Model predicting college course grade of median or above
• Unmodified algorithm, before correction, performed worse for male students than for female students

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