Difference between revisions of "National and International Examination"

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

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 * 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 of 70% or 71% to chance.
+* 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.