Difference between revisions of "Latino/Latina/Latinx/Hispanic Learners in North America"

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* 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)
* 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
* 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) [[https://files.eric.ed.gov/fulltext/ED608066.pdf 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

Revision as of 21:10, 22 March 2022

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


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