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 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)

• 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