White Learners in North America

Bridgeman et al. (2009) pdf

• Automated scoring models for evaluating English essays, or e-rater
• The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students than other groups

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

Zhang et al. (in press) [1]

• Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
• For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
• No racial/ethnic group consistently had best-performing detectors

Li, Xing, & Leite (2022) pdf

• Models predicting whether two students will communicate on an online discussion forum
• Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White)
• Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students

Sulaiman & Roy (2022) [2]

• Models predicting whether a law student will pass the bar exam (to practice law)
• Compared White and non-White students
• Models not applying fairness constraints performed significantly worse for White students in terms of ABROCA
• Models applying fairness constraints performed equivalently for White and non-White students

Jeong et al. (2022) [3]

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