White Learners in North America

From Penn Center for Learning Analytics Wiki
Jump to navigation Jump to search

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.