View source for National and International Examination

Baker et al. (2020) [https://www.upenn.edu/learninganalytics/ryanbaker/BakerBerningGowda.pdf 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) [https://arxiv.org/pdf/2103.15212.pdf 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) [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