Difference between revisions of "Parental Educational Background"

Latest revision as of 22:02, 20 June 2022

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

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.

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 Rzepka et al. (2022) [https://www.insticc.org/node/TechnicalProgram/CSEDU/2022/presentationDetails/109621 pdf]
 * Models predicting whether student will quit spelling learning activity without completing
-* Multiple algorithms have slightly better false positive rates for second-language speakers than native speakers, but equivalent performance on multiple other metrics.
 * 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.
-* Multiple algorithms have slightly better false positive rates and AUC ROC for male students than female students, but equivalent performance on multiple other metrics.