Military-Connected Status - Revision history

Seiyon at 12:24, 18 May 2022

2022-05-18T12:24:01Z

← 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 and non-military-connected students		* Model predicting student graduation and SAT scores for military-connected students and non-military-connected students
	* For prediction of graduation, applying algorithms across population resulted an poorer AUC ROC		* For prediction of graduation, applying algorithms across population resulted an poorer AUC ROC
	* For prediction of SAT scores, applying algorithms across population resulted in a poorer Spearman's ρ.		* For prediction of SAT scores, applying algorithms across population resulted in a poorer Spearman's ρ.

Ryan: Rewritten for clarity

2022-03-28T16:57:57Z

Rewritten for clarity

← Older revision		Revision as of 12:57, 28 March 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 and non-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, applying algorithms across population resulted an poorer AUC ROC
	* 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, applying algorithms across population resulted in a poorer Spearman's ρ.

Seiyon at 09:24, 3 February 2022

2022-02-03T09:24:10Z

← Older revision		Revision as of 05:24, 3 February 2022
Line 1:		Line 1:
	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
	* 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 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.		* 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.

Seiyon: Created page with "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..."

2022-02-03T09:20:08Z

Created page with "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..."

New page

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.