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 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. (2022) 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.

Zhang et al.(2023) pdf

• Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process,2) commenting on the answer, and 3) relating to self.
• Models have approximately equal performance for Hispanic/Latinx, African American, and White students.