Parental Educational Background

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. (2020) pdf

• Models predicting undergraduate course grades and average GPA

• First-generation college students were inaccurately predicted to get lower course grade and average GPA
• Fairness of models improved with the inclusion of clickstream and survey data

Yu et al. (2021) pdf

• Models predicting college dropout for students in residential and fully online program
• Whether the socio-demographic information was included or not, the model showed worse accuracy and true negative rates for first-generation students who are studying in person
• The model showed better recall for first-generation students, especially for those studying in person

Kung & Yu (2020) pdf

• Predicting course grades and later GPA at public U.S. university
• Worse performance on independence for first-generation students in course grade prediction on 5 of 5 classic machine algorithms; worse performance on separation for 3 of 5 algorithms; comparable performance on sufficiency for 5 of 5 algorithms
• Worse performance on independence for first-generation students in later GPA prediction for three of five algorithms; two algorithms had worse separation and one algorithm had worse sufficiency

Rzepka et al. (2022) pdf

• Models predicting whether student will quit spelling learning activity without completing
• Multiple algorithms have slightly better false positive rates and AUC ROC for students with at least one parent who graduated high school, but equivalent performance on multiple other metrics.