Difference between revisions of "Parental Educational Background"
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(Created page with "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") |
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Kai et al. (2017) | Kai et al. (2017) [https://files.eric.ed.gov/fulltext/ED596601.pdf pdf] | ||
* Models predicting student retention in an online college program | * 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-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 | * 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) [https://files.eric.ed.gov/fulltext/ED608066.pdf 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) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 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) | |||
[https://dl.acm.org/doi/pdf/10.1145/3386527.3406755 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) [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 and AUC ROC for students with at least one parent who graduated high school, but equivalent performance on multiple other metrics. | |||
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
- 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.