National and International Examination - Revision history

Ryan at 15:04, 17 December 2022

2022-12-17T15:04:56Z

← Older revision		Revision as of 11:04, 17 December 2022
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	* 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 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.		* 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.

Ryan: Added Sulaiman & Roy

2022-08-04T19:58:07Z

Added Sulaiman & Roy

← Older revision		Revision as of 15:58, 4 August 2022
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	*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

Seiyon at 12:24, 18 May 2022

2022-05-18T12:24:27Z

← Older revision		Revision as of 08:24, 18 May 2022
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	Baker et al. (2020) [[https://www.upenn.edu/learninganalytics/ryanbaker/BakerBerningGowda.pdf pdf]]		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
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	Li et al. (2021) [[https://arxiv.org/pdf/2103.15212.pdf pdf]]		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)

Seiyon at 09:23, 3 February 2022

2022-02-03T09:23:43Z

← Older revision		Revision as of 05:23, 3 February 2022
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	Baker at al. (2020) [[https://www.upenn.edu/learninganalytics/ryanbaker/BakerBerningGowda.pdf pdf]]		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

Seiyon at 09:23, 3 February 2022

2022-02-03T09:23:18Z

← Older revision		Revision as of 05:23, 3 February 2022
Line 1:		Line 1:
			Baker at 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 of 70% or 71% 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) [[https://arxiv.org/pdf/2103.15212.pdf pdf]]		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)

Seiyon: Created page with "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)"

2022-01-26T03:31:44Z

Created page with "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)"

New page

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)