Difference between revisions of "Gender: Male/Female"
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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] | ||
* Models predicting if student at-risk for failing a course | * 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) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM2019_paper56.pdf pdf] | |||
* Models predicting six-year college graduation | |||
* Algorithms had higher false negative rates for male students | |||
Revision as of 01:50, 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