Difference between revisions of "At-risk/Dropout/Stopout/Graduation Prediction"
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* For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance of 70% or 71% to chance. | * For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance of 70% or 71% to chance. | ||
* For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance. | * For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance. | ||
Kai et al. (2017)[[https://files.eric.ed.gov/fulltext/ED596601.pdf pdf]] | |||
* Models predicting student retention in an online college program | |||
* J-48 decision trees achieved much higher Kappa and AUC for students whose parents did not attend college than those whose parents did | |||
* J-Rip decision rules achieved much higher Kappa and AUC for students whose parents did not attended college than those whose parents did | |||
Revision as of 05:47, 17 February 2022
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
- JRip decision rules achieved moderately higher Kappa and AUC for female students than male students
Hu and Rangwala (2020) pdf
- Models predicting if college student at-risk for failing a course
- Several algorithms perform worse for African-American students
- Performed worse for male students, but the degree differed across courses
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
Baker et al. (2020) [pdf]
- Model predicting student graduation and SAT scores for military-connected students
- For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance of 70% or 71% to chance.
- For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance.
Kai et al. (2017)[pdf]
- Models predicting student retention in an online college program
- J-48 decision trees achieved much higher Kappa and AUC for students whose parents did not attend college than those whose parents did
- J-Rip decision rules achieved much higher Kappa and AUC for students whose parents did not attended college than those whose parents did