Difference between revisions of "Indigenous Learners in North America"

Revision as of 15:18, 28 March 2022

Lee and Kizilcec (2020) [pdf]

Models predicting college success (or median grade or above)
Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values

Christie et al. (2019) pdf

Models predicting student's high school dropout
The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.
The decision trees showed very minor differences in AUC between female and male students

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 *Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
 *The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values
+Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
+*Models predicting student's high school dropout
+*The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and  Native Hawaiian and Pacific Islander.
+*The decision trees showed very minor differences in AUC between female and male students