Indigenous Learners in North America

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 American Indian and Alaska Native, White, Black, Hispanic, Asian, 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

Jeong et al. (2022) [1]

• Predicting 9th grade math score from academic performance, surveys, and demographic information
• Despite comparable accuracy, model tends to underpredict Native American students' performance
• Several fairness correction methods equalize false positive and false negative rates across groups.