Black/African-American Learners in North America

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 White, Black, 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; American Indian, Black, 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

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 protected attributed were included or not, the models had worse true negative rates but better recall for underrepresented minority (URM) students, in residential and online programs
• The model was less accurate for URM students studying in residential program

Ramineni & Williamson (2018) pdf

• Revised automated scoring engine for assessing GRE essay
• Relative weakness in content and organization by African American test takers resulted in lower scores than Chinese peers who wrote longer

Bridgeman et al. (2009) pdf

• Automated scoring models for evaluating English essays, or e-rater
• E-rater gave significantly higher score for 11th grade essays written by Asian American and Hispanic students, particularly, Hispanic female students
• The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students
• E-rater gave slightly lower score for GRE essays (argument and issue) written by Black test-takers while e-rated scores were higher for Asian test-takers in the U.S

Bridgeman, Trapani, and Attali (2012) pdf

• A later version of automated scoring models for evaluating English essays, or e-rater
• E-rater gave slightly lower scores for African-American, Hispanic, and American-Indian test-takers, most significantly for African-American, and American-Indian males, when assessing written responses to issue prompt in GRE
• The score was significantly lower when e-rater was assessing GRE argument essays written by African-American test-takers