Difference between revisions of "Black/African-American Learners in North America"
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* J48 decision trees achieved much lower Kappa and AUC for Black students than White students | * 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 | * JRip decision rules achieved almost identical Kappa and AUC for Black students and White students | ||
Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf] | Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf] | ||
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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 | ||
Anderson et al. (2019) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM2019_paper56.pdf pdf] | Anderson et al. (2019) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM2019_paper56.pdf pdf] | ||
* Models predicting six-year college graduation | * Models predicting six-year college graduation | ||
* Performance for African-American students comparable to performance for students in other races. | * Performance for African-American students comparable to performance for students in other races. | ||
Ramineni & Williamson (2018) [[https://onlinelibrary.wiley.com/doi/10.1002/ets2.12192 pdf]] | Ramineni & Williamson (2018) [[https://onlinelibrary.wiley.com/doi/10.1002/ets2.12192 pdf]] | ||
* Revised automated scoring engine for assessing GSE essay | * 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. | * Relative weakness in content and organization by African American test takers resulted in lower scores than Chinese peers who wrote longer. | ||
Revision as of 16:26, 22 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
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- Performance for African-American students comparable to performance for students in other races.
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.