Difference between revisions of "Gender: Male/Female"
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* Models predicting six-year college graduation | * 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 | * False negatives rates were greater for male students than female students when SVM, Logistic Regression, and SGD were used | ||
Gardner, Brooks and Baker (2019) [[https://www.upenn.edu/learninganalytics/ryanbaker/LAK_PAPER97_CAMERA.pdf pdf]] | Gardner, Brooks and Baker (2019) [[https://www.upenn.edu/learninganalytics/ryanbaker/LAK_PAPER97_CAMERA.pdf pdf]] | ||
* Model predicting MOOC dropout, specifically through slicing analysis | * 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 | * Some algorithms studied performed worse for female students than male students, particularly in courses with 45% or less male presence | ||
Riazy et al. (2020) [[https://www.scitepress.org/Papers/2020/93241/93241.pdf pdf]] | Riazy et al. (2020) [[https://www.scitepress.org/Papers/2020/93241/93241.pdf pdf]] | ||
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* Model predicting college course grade of median or above | * Model predicting college course grade of median or above | ||
* Unmodified algorithm, before correction, performed worse for male students than for female students | * Unmodified algorithm, before correction, performed worse for male students than for female students | ||
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 course grades and average GPA | ||
* female students were generally inaccurately predicted to perform better than male students | * female students were generally inaccurately predicted to perform better than male students | ||
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 | * Model predicting college dropout | ||
* Worse true negative rates for male students, but somewhat better recall for male students taking courses in-person | * Worse true negative rates for male students, but somewhat better recall for male students taking courses in-person | ||
Revision as of 18:59, 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]
- Model predicting college course grade of median or above
- Unmodified algorithm, before correction, performed worse for male students than for female students
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