Difference between revisions of "Gender: Male/Female"

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Yu and colleagues (2021) [[https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]]
Yu and colleagues (2021) [[https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]]
* Model predicting college dropout
* Models predicting college dropout for students in residential and fully online program
* Worse true negative rates for male students, but somewhat better recall for male students taking courses in-person
* Whether the protected attributed were included or not, the models had worse true negative rates and recall for underrepresented minority (URM) students, in residential and online programs.
* The model was less accurate for URM students studying in residential program.

Revision as of 21:35, 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
  • 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 and colleagues (2021) [pdf]

  • Models predicting college dropout for students in residential and fully online program
  • Whether the protected attributed were included or not, the models had worse true negative rates and recall for underrepresented minority (URM) students, in residential and online programs.
  • The model was less accurate for URM students studying in residential program.