Difference between revisions of "Gender: Male/Female"

Revision as of 19:24, 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 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

@@ Line 27: / Line 27: @@
 Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]]
-* Model predicting college course grade of median or above
+* Models predicting college success (or median grade or above)
-* Unmodified algorithm, before correction, performed worse for male students than for female students
+* 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