Difference between revisions of "At-risk/Dropout/Stopout/Graduation Prediction"
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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, specifically through slicing analysis | ||
* Some algorithms | * Some algorithms performed worse for female students than male students, particularly in courses with 45% or less male presence | ||
Revision as of 04:12, 24 January 2022
Hu and Rangwala (2020) pdf
- Models predicting if student at-risk for failing a course
- Several algorithms perform worse for African-American students
Kai et al. (2017) pdf
- Models predicting student retention in an online college program
- J48 decision trees achieved much lower Kappa and AUC for Black students than White students
- JRip decision rules achieved almost identical Kappa and AUC for Black students and White students
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- Performance for African-American students comparable to performance for students in other races.
Yu, Lee, and Kizilcec (2021)[pdf]
- Model predicting college dropout
- worse true negative rates and better recall for students who are not White or Asian, and also worse accuracy if the student is studying in person
Gardner, Brooks and Baker (2019) [pdf]
- Model predicting MOOC dropout, specifically through slicing analysis
- Some algorithms performed worse for female students than male students, particularly in courses with 45% or less male presence