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 et al. (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 and worse accuracy for male students
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 et al. (2012) pdf

A later version of automated scoring models for evaluating English essays, or e-rater
E-Rater system correlated comparably well with human rater when assessing TOEFL and GRE essays written by male and female students

Verdugo et al. (2022) pdf

An algorithm predicting dropout from university after the first year
Several algorithms achieved better AUC for male than female students; results were mixed for F1.

Zhang et al. (in press)

Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
For each SRL-related detector, relatively small differences in AUC were observed across gender groups.
No gender group consistently had best-performing detectors

Rzepka et al. (2022) pdf

Models predicting whether student will quit spelling learning activity without completing
Multiple algorithms have slightly better false positive rates and AUC ROC for male students than female students, but equivalent performance on multiple other metrics.

Li, Xing, & Leite (2022) pdf

Models predicting whether two students will communicate on an online discussion forum
Multiple fairness approaches lead to ABROCA of under 0.01 for female versus male students

Sha et al. (2021) pdf

Models predicting a MOOC discussion forum post is content-relevant or content-irrelevant
Some algorithms achieved ABROCA under 0.01 for female students versus male students,

but other algorithms (Naive Bayes) had ABROCA as high as 0.06

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