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

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Lee and Kizilcec (2020) https://arxiv.org/pdf/2007.00088.pdf pdf
Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* 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)
* 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)
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Bridgeman et al. (2009) [[https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]]
Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]
* Automated scoring models for evaluating English essays, or e-rater
* Automated scoring models for evaluating English essays, or e-rater
* E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students
* E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students

Revision as of 07:35, 19 May 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


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 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 they are studying in person
  • The model showed better recall for URM students


Bridgeman et al. (2009) page

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