Difference between revisions of "Latino/Latina/Latinx/Hispanic Learners in North America"
Jump to navigation
Jump to search
| Line 3: | Line 3: | ||
* False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used | * 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 | * White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD | ||
Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf] | Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf] | ||
* Models predicting student's high school dropout | * 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. | * 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) [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) | ||
* 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 from 0.5 to group-specific values | ||
| Line 20: | Line 22: | ||
Yu | Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf] | ||
* Models predicting college dropout for students in residential and fully online program | * Models predicting college dropout for students in residential and fully online program | ||
* Whether the protected attributed were included or not, the models had worse true negative rates but better recall for underrepresented minority (URM) students, in residential and online programs | * Whether the protected attributed were included or not, the models had worse true negative rates but better recall for underrepresented minority (URM) students, in residential and online programs | ||
Revision as of 07:37, 18 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 protected attributed were included or not, the models had worse true negative rates but better recall for underrepresented minority (URM) students, in residential and online programs
- The model was less accurate for URM students studying in residential program
Bridgeman et al. (2009) pdf
- Automated scoring models for evaluating English essays, or e-rater
- E-rater gave significantly higher score for 11th grade essays written by Asian American and Hispanic students, particularly, Hispanic female students
- The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students