Asian/Asian-American Learners in North America

Christie et al. (2019) pdf

• Models predicting student's high school dropout
• The decision trees showed little difference in AUC among Asian, White, Black, Hispanic, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Bridgeman et al. (2009) page

• Automated scoring models for evaluating English essays, or e-rater
• E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students

Lee and Kizilcec (2020) pdf

• Models predicting college success (or median grade or above)
• Random forest algorithms performed significantly better for non-URM students (Asian and White) than for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural)
• 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

Jiang & Pardos (2021) pdf

• Predicting university course grades using LSTM
• Roughly equal accuracy across racial groups
• 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 overpredict Asian students' performance
• Several fairness correction methods equalize false positive and false negative rates across groups.