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) | 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 06:35, 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