Difference between revisions of "Indigenous Learners in North America"

Revision as of 09:03, 21 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

Jiang & Pardos (2021) pdf

Predicting university course grades using LSTM
Roughly equal accuracy across racial groups (including Native American and Pacific Islander students)
Slightly better accuracy (~1%) across racial groups when including race in model

@@ Line 9: / Line 9: @@
 *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
+Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
+* Predicting university course grades using LSTM
+* Roughly equal accuracy across racial groups (including Native American and Pacific Islander students)
+* Slightly better accuracy (~1%) across racial groups when including race in model