White Learners in North America
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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. (2022) 
- 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) 
- 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) 
- 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.
Permodo et al. (2023) pdf
- Paper discusses system that predicts probabilities of on-time graduation
- Prediction is less accurate for White students than other students
Zhang et al.(2023) pdf
- Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.
- Models have approximately equal performance for White, African American and Hispanic/Latinx students.
Almoubayyed et al. (2023) pdf
- Models discovering generalization of the performance for reading comprehension ability in the context of middle school students’ usage of Carnegie Learning’s ITS for mathematics instruction
- Model trained on smaller dataset achieves greater fairness in prediction for white and non-white students
- For model trained on larger dataset, prediction is more accurate for white students than for non-white students.