Latino/Latina/Latinx/Hispanic Learners in North America

Anderson et al. (2019) pdf

• Models predicting six-year college graduation
• False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used
• White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD

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
• Hispanic 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 and colleagues (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.