Asian/Asian-American Learners in North America
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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.