Black/African-American Learners in North America

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Kai et al. (2017) pdf

  • Models predicting student retention in an online college program
  • J48 decision trees achieved much lower Kappa and AUC for Black students than White students
  • JRip decision rules achieved almost identical Kappa and AUC for Black students and White students


Hu and Rangwala (2020) pdf

  • Models predicting if a college student will fail in a course
  • Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
  • The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science


Christie et al. (2019) pdf

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


Lee and Kizilcec (2020) pdf

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


Yu et al. (2020) pdf

  • Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
  • Black students were inaccurately predicted to perform worse for both short-term and long-term
  • The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model


Yu et al. (2021) pdf

  • Models predicting college dropout for students in residential and fully online program
  • Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
  • The model showed better recall for URM students, whether they were in residential or online program


Ramineni & Williamson (2018) pdf

  • Revised automated scoring engine for assessing GRE essay
  • E-rater gave African American test-takers significantly lower scores than human raters when assessing their written responses to argument prompts
  • The shorter essays written by African American test-takers were more likely to receive lower scores as showing weakness in content and organization


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 African American and White students


Bridgeman et al. (2012) pdf

  • A later version of automated scoring models for evaluating English essays, or e-rater
  • E-rater gave significantly lower score than human rater when assessing African-American students’ written responses to issue prompt in GRE


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) pdf

  • 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 (over 2/3

Black/African American)

  • Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students


Litman et al. (2021) html

  • Automated essay scoring models inferring text evidence usage
  • All algorithms studied have less than 1% of error explained by whether student is Black


Jeong et al. (2022) [1]

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