Difference between revisions of "Black/African-American Learners in North America"
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* Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse | * Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse | ||
* The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science | * The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science | ||
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]] | ||
Revision as of 15:33, 28 March 2022
Kai et al. (2017) pdf
- Models predicting student retention in an online college program
- J48 decision trees achieved much lower Kappa and AUC for Black students than White students
- JRip decision rules achieved almost identical Kappa and AUC for Black students and White students
Hu and Rangwala (2020) pdf
- Models predicting if a college student will fail in a course
- Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
- The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science
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
Yu et al. (2020) [pdf]
- Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
- Black 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 and colleagues (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.
Ramineni & Williamson (2018) [pdf]
- Revised automated scoring engine for assessing GSE essay
- Relative weakness in content and organization by African American test takers resulted in lower scores than Chinese peers who wrote longer.