Difference between revisions of "Gender: Male/Female"
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Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf] | Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf] | ||
* Models predicting student retention in an online college program | * Models predicting student retention in an online college program | ||
* J48 decision trees achieved significantly | * J48 decision trees achieved significantly lower Kappa but higher AUC for male students than female students | ||
* J48 decision trees achieved much | * J48 decision trees achieved much lower Kappa and AUC for male students than female students | ||
Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf] | Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf] | ||
Revision as of 17:16, 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
- J48 decision trees achieved much lower Kappa and AUC for male students than female students
Hu and Rangwala (2020) pdf
- Models predicting if college student at-risk for failing a course
- Performed worse for male students, but the degree differed across 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, 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