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 20:28, 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