Difference between revisions of "Latino/Latina/Latinx/Hispanic Learners in North America"
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* Models predicting six-year college graduation | * Models predicting six-year college graduation | ||
* Algorithms had higher false positive rates for White students and higher false negative rates for Latino students. | * Algorithms had higher false positive rates for White students and higher false negative rates for Latino students. | ||
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 23:41, 23 January 2022
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- Algorithms had higher false positive rates for White students and higher false negative rates for Latino students.
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)