Difference between revisions of "National and International Examination"
Jump to navigation
Jump to search
m |
|||
| (3 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
Baker | Baker et al. (2020) [https://www.upenn.edu/learninganalytics/ryanbaker/BakerBerningGowda.pdf pdf] | ||
* Model predicting student graduation and SAT scores for military-connected students | * Model predicting student graduation and SAT scores for military-connected students | ||
* For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance | * For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance by 70% or 71% towards chance. | ||
* For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance. | * For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance. | ||
Li et al. (2021) | Li et al. (2021) [https://arxiv.org/pdf/2103.15212.pdf pdf] | ||
*Model predicting student achievement on the standardized examination PISA | *Model predicting student achievement on the standardized examination PISA | ||
*Inaccuracy of the U.S.-trained model was greater for students from countries with lower scores of national development (e.g. Indonesia, Vietnam, Moldova) | *Inaccuracy of the U.S.-trained model was greater for students from countries with lower scores of national development (e.g. Indonesia, Vietnam, Moldova) | ||
Sulaiman & Roy (2022) [https://fated2022.github.io/assets/pdf/FATED-2022_paper_Sulaiman_Transformers.pdf] | |||
* Models predicting whether a law student will pass the bar exam (to practice law) | |||
* Compared White and non-White students | |||
* Models not applying fairness constraints performed significantly worse for White students in terms of ABROCA | |||
* Models applying fairness constraints performed equivalently for White and non-White students | |||
Latest revision as of 10:04, 17 December 2022
Baker et al. (2020) pdf
- Model predicting student graduation and SAT scores for military-connected students
- For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance by 70% or 71% towards chance.
- For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance.
Li et al. (2021) pdf
- Model predicting student achievement on the standardized examination PISA
- Inaccuracy of the U.S.-trained model was greater for students from countries with lower scores of national development (e.g. Indonesia, Vietnam, Moldova)
Sulaiman & Roy (2022) [1]
- Models predicting whether a law student will pass the bar exam (to practice law)
- Compared White and non-White students
- Models not applying fairness constraints performed significantly worse for White students in terms of ABROCA
- Models applying fairness constraints performed equivalently for White and non-White students