Difference between revisions of "Gender: Male/Female"
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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 | * Model predicting MOOC dropout | ||
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]] | ||
Revision as of 02:00, 24 January 2022
Kai et al. (2017) pdf
- Models predicting student retention in an online college program
- performance was very good for both groups
- JRip decision tree model performed more equitably than a J48 decision tree model for both male and female students.
- JRip model had moderately better performance for female students than male students
Hu and Rangwala (2020) pdf
- Models predicting if student at-risk for failing a course
- Performed worse for male students, but that this result is inconsistent across university courses
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- Algorithms had higher false negative rates for male students
Gardner, Brooks and Baker (2019) [pdf]
- Model predicting MOOC dropout
- 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.