Difference between revisions of "Gender: Male/Female"

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Yu et al. (2020) [[https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]]
Yu et al. (2020) [[https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]]
* Model predicting undergraduate course grades and average GPA
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* female students were generally inaccurately predicted to perform better than male students
* Classification algorithms inaccurately predicted that female students would achieve short-term and long-term success
* Females students were not biased against when a combination of institutional and click data was used in the model





Revision as of 20:56, 22 March 2022

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 short-term (course grades) and long-term (average GPA) success
  • Classification algorithms inaccurately predicted that female students would achieve short-term and long-term success
  • Females students were not biased against when a combination of institutional and click data was used in the model


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