Latino/Latina/Latinx/Hispanic Learners in North America

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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 Hispanic, White, Black, 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; Hispanic, American Indian, Black, Hawaiian or Pacific Islander, 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
  • 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 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


Bridgeman et al. (2009) page

  • Automated scoring models for evaluating English essays, or e-rater
  • E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students


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


Kung & Yu (2020) pdf

  • Predicting course grades and later GPA at public U.S. university
  • Poorer independence, separation, sufficiency for Latinx students than white students for five different classic machine learning algorithms


Jeong et al. (2022) [1]

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