Difference between revisions of "Indigenous Learners in North America"
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Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf 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 | |||
Revision as of 19:28, 22 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