At-risk/Dropout/Stopout/Graduation Prediction

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

Yu et al. (2021) pdf

• Models predicting college dropout
• Models for first-generation residential students showed worse accuracy and true negative rate (i.e., predicting power of sophomore year persistence on college persistence)
• Models for first-generation residential students showed significantly better recall (i.e., proportion of correctly identified dropouts) than online peers, whether the attribute were made aware or not

Yu et al. (2021) pdf

• Model performed with significantly lower accuracy and true negative rate for residential students with greater financial need than online counterparts, whether the attribute were made aware or not
• Model performed with significantly higher recall for residential students with greater financial need than online counterparts, whether the attribute were made aware or not