Difference between revisions of "Parental Educational Background"
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* Models predicting college dropout | * 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 | * Models for first-generation residential students showed worse accuracy and true negative rate (i.e., predicting power of sophomore year persistence in college) | ||
* Models for first-generation residential students showed significantly better recall (i.e., proportion of correctly identified dropouts) than online peers, whether | * Models for first-generation residential students showed significantly better recall (i.e., proportion of correctly identified dropouts) than online peers, whether their status were included or not | ||
Revision as of 07:07, 17 May 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
- 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
- Models for first-generation residential students showed worse accuracy and true negative rate (i.e., predicting power of sophomore year persistence in college)
- Models for first-generation residential students showed significantly better recall (i.e., proportion of correctly identified dropouts) than online peers, whether their status were included or not