Difference between revisions of "Latino/Latina/Latinx/Hispanic Learners in North America"

Revision as of 00:17, 24 January 2022

Anderson et al. (2019) pdf

Models predicting six-year college graduation
Algorithms showed higher false negative rates for Latino students while showing higher false positive rates for White students.

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 Anderson et al. (2019) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM2019_paper56.pdf pdf]
 * Models predicting six-year college graduation
-* Algorithms had higher false positive rates for White students and higher false negative rates for Latino students.
+* Algorithms showed higher false negative rates for Latino students while showing higher false positive rates for White 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)