Difference between revisions of "Indigenous Learners in North America"
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Lee and Kizilcec (2020) | Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf] | ||
*Models predicting college success (or median grade or above) | *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) | *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 | *The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values | ||
Revision as of 07:44, 18 May 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 from 0.5 to group-specific 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