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


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

  • Balancing the size of each group in the training set reduced ABROCA


Litman et al. (2021) html

  • Automated essay scoring models inferring text evidence usage
  • All algorithms studied have less than 1% of error explained by whether student is female and male

Sha et al. (2022) [1]

  • Three data sets and algorithms: predicting course pass/fail (random forest), dropout (neural network), and forum post relevance (neural network)
  • A range of over-sampling methods tested
  • Regardless of over-sampling method used, course pass/fail performance was moderately better for males, dropout performance was slightly better for males, and forum post relevance performance was moderately better for females.