Difference between revisions of "Black/African-American Learners in North America"
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* Models predicting six-year college graduation | * Models predicting six-year college graduation | ||
* Performance for African-American students comparable to performance for students in other races. | * Performance for African-American students comparable to performance for students in other races. | ||
Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]] | |||
* Model predicting college course grade of median or above | |||
* Out-of-the-box random forest model violates demographic parity and equality of opportunity for URM(underrepresented minority: American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than for non-URM students (White and Asian) | |||
Revision as of 23:42, 23 January 2022
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
Hu and Rangwala (2020) pdf
- Models predicting if student at-risk of failing a course
- Several algorithms perform worse for African-American students
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- Performance for African-American students comparable to performance for students in other races.
Lee and Kizilcec (2020) [pdf]
- Model predicting college course grade of median or above
- Out-of-the-box random forest model violates demographic parity and equality of opportunity for URM(underrepresented minority: American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than for non-URM students (White and Asian)