Socioeconomic Status - Revision history

Shruti at 04:14, 28 November 2023

2023-11-28T04:14:54Z

← Older revision		Revision as of 00:14, 28 November 2023
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	* Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)		* Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)
	* Model performs worse for students from school with higher socio-economic status in open-ended environment (FNR 0.73 for higher SES and FNR 0.57 for medium SES).		* Model performs worse for students from school with higher socio-economic status in open-ended environment (FNR=0.73 for higher SES and FNR=0.57 for medium SES).

Shruti at 04:12, 28 November 2023

2023-11-28T04:12:30Z

← Older revision		Revision as of 00:12, 28 November 2023
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	* Paper discusses system that predicts probabilities of on-time graduation		* Paper discusses system that predicts probabilities of on-time graduation
	* Prediction is more accurate for low-income students than non-low-income students		* Prediction is more accurate for low-income students than non-low-income students


			Cock et al.(2023) [[https://dl.acm.org/doi/abs/10.1145/3576050.3576149?casa_token=6Fjh-EUzN-gAAAAA%3AtpRMYzSAVoQFYNzwY5gwSsrnzHIlI0tUjMq6okwgdcCUmuBMVZEtn8eLO52dCtIYUbrHBV_Il9Sx pdf]]

			* Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)
			* Model performs worse for students from school with higher socio-economic status in open-ended environment (FNR 0.73 for higher SES and FNR 0.57 for medium SES).

Shruti: addition new paper

2023-06-29T01:15:10Z

addition new paper

← Older revision		Revision as of 21:15, 28 June 2023
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	Permodo et al. (2023)		Permodo et al. (2023) [https://www.researchgate.net/publication/370001437_Difficult_Lessons_on_Social_Prediction_from_Wisconsin_Public_Schools pdf]

	* Paper discusses system that predicts probabilities of on-time graduation		* Paper discusses system that predicts probabilities of on-time graduation
	* Prediction is more accurate for low-income students than non-low-income students		* Prediction is more accurate for low-income students than non-low-income students

Shruti: addition new paper

2023-06-29T00:41:25Z

addition new paper

← Older revision		Revision as of 20:41, 28 June 2023
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	* Model was unable to make prediction of student success (F1 score = 0.0) for students not in a social welfare program (higher socioeconomic status)		* Model was unable to make prediction of student success (F1 score = 0.0) for students not in a social welfare program (higher socioeconomic status)
	* Model had slightly lower AUC ROC (0.52 instead of 0.56) for students not in a social welfare program (higher socioeconomic status)		* Model had slightly lower AUC ROC (0.52 instead of 0.56) for students not in a social welfare program (higher socioeconomic status)


			Permodo et al. (2023)

			* Paper discusses system that predicts probabilities of on-time graduation
			* Prediction is more accurate for low-income students than non-low-income students

Ryan: Rewrote Queiroga

2023-06-04T15:35:18Z

Rewrote Queiroga

← Older revision		Revision as of 11:35, 4 June 2023
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	Queiroga et al. (2022) [https://~~doi~~.~~org~~/~~10.3390~~/~~info13090401~~ pdf]		Queiroga et al. (2022) [https://www.mdpi.com/2078-2489/13/9/401 pdf]

	* Models predicting secondary school students at risk of failure or dropping out		* Models predicting secondary school students at risk of failure or dropping out
	* ~~Models achieved high performances with an AUC ROC higher than~~ 0.~~90 and F1 higher than~~ 0~~.88~~		* Model was unable to make prediction of student success (F1 score = 0.0) for students not in a social welfare program (higher socioeconomic status)
	* Equal performance for ~~both genders~~ students ~~who participated in the educational system and completed their studies~~		* Model had slightly lower AUC ROC (0.52 instead of 0.56) for students not in a social welfare program (higher socioeconomic status)
	* Students engaged in social welfare ~~programs results in fewer problems in education~~
	* ~~The F1 score for social welfare program is~~ 0.~~80, while~~ for ~~no social welfare program is 0, indicating bias toward~~ social welfare program ~~students~~
	* The two most important features to predict students at risk in secondary school in Uruguay early are first-year primary school zones (~~rural or urban~~) ~~and sixth-year assessment-based grouping~~

Zhanlan at 21:52, 1 June 2023

2023-06-01T21:52:16Z

← Older revision		Revision as of 17:52, 1 June 2023
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	* Students engaged in social welfare programs results in fewer problems in education		* Students engaged in social welfare programs results in fewer problems in education
	* The F1 score for social welfare program is 0.80, while for no social welfare program is 0, indicating bias toward social welfare program students		* The F1 score for social welfare program is 0.80, while for no social welfare program is 0, indicating bias toward social welfare program students
	* The two most important features to predict students at risk in secondary school in Uruguay early are ~~First~~-year primary school zones (rural or urban) and sixth-year assessment-based grouping		* The two most important features to predict students at risk in secondary school in Uruguay early are first-year primary school zones (rural or urban) and sixth-year assessment-based grouping

Zhanlan at 21:43, 1 June 2023

2023-06-01T21:43:28Z

← Older revision		Revision as of 17:43, 1 June 2023
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	* Models predicting secondary school students at risk of failure or dropping out		* Models predicting secondary school students at risk of failure or dropping out
	* Models achieved high performances with an AUC higher than 0.90 and F1 higher than 0.88		* Models achieved high performances with an AUC ROC higher than 0.90 and F1 higher than 0.88
	* Equal performance for both genders students who participated in the educational system and completed their studies		* Equal performance for both genders students who participated in the educational system and completed their studies
	* Students engaged in social welfare programs results in fewer problems in education		* Students engaged in social welfare programs results in fewer problems in education
	* The F1 score for social welfare program is 0.80, while for no social welfare program is 0, indicating bias toward social welfare program students		* The F1 score for social welfare program is 0.80, while for no social welfare program is 0, indicating bias toward social welfare program students
	* The two most important features to predict students at risk in secondary school in Uruguay early are First-year primary school zones (rural or urban) and sixth-year assessment-based grouping		* The two most important features to predict students at risk in secondary school in Uruguay early are First-year primary school zones (rural or urban) and sixth-year assessment-based grouping

Zhanlan at 21:41, 1 June 2023

2023-06-01T21:41:20Z

← Older revision		Revision as of 17:41, 1 June 2023
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	* Automated essay scoring models inferring text evidence usage		* Automated essay scoring models inferring text evidence usage
	* All algorithms studied have less than 1% of error explained by whether student receives free/reduced price lunch		* All algorithms studied have less than 1% of error explained by whether student receives free/reduced price lunch


			Queiroga et al. (2022) [https://doi.org/10.3390/info13090401 pdf]

			* Models predicting secondary school students at risk of failure or dropping out
			* Models achieved high performances with an AUC higher than 0.90 and F1 higher than 0.88
			* Equal performance for both genders students who participated in the educational system and completed their studies
			* Students engaged in social welfare programs results in fewer problems in education
			* The F1 score for social welfare program is 0.80, while for no social welfare program is 0, indicating bias toward social welfare program students
			* The two most important features to predict students at risk in secondary school in Uruguay early are First-year primary school zones (rural or urban) and sixth-year assessment-based grouping

Ryan: Added Litman et al. (2021)

2022-07-04T16:32:30Z

Added Litman et al. (2021)

← Older revision		Revision as of 12:32, 4 July 2022
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	* Equal performance for low-income and upper-income students in course grade prediction for several algorithms and metrics		* Equal performance for low-income and upper-income students in course grade prediction for several algorithms and metrics
	* Worse performance on independence for low-income students than high-income students in later GPA prediction for four of five algorithms; one algorithm had worse separation and two algorithms had worse sufficiency		* Worse performance on independence for low-income students than high-income students in later GPA prediction for four of five algorithms; one algorithm had worse separation and two algorithms had worse sufficiency


			Litman et al. (2021) [https://link.springer.com/chapter/10.1007/978-3-030-78292-4_21 html]
			* Automated essay scoring models inferring text evidence usage
			* All algorithms studied have less than 1% of error explained by whether student receives free/reduced price lunch

Ryan: added Kung & Yu (2020)

2022-06-13T14:45:24Z

added Kung & Yu (2020)

← Older revision		Revision as of 10:45, 13 June 2022
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	*Whether the socio-demographic information was included or not, the model showed worse accuracy and true negative rates for residential students with greater financial needs		*Whether the socio-demographic information was included or not, the model showed worse accuracy and true negative rates for residential students with greater financial needs
	*The model showed better recall for students with greater financial needs, especially for those studying in person		*The model showed better recall for students with greater financial needs, especially for those studying in person


			Kung & Yu (2020)
			[https://dl.acm.org/doi/pdf/10.1145/3386527.3406755 pdf]
			* Predicting course grades and later GPA at public U.S. university
			* Equal performance for low-income and upper-income students in course grade prediction for several algorithms and metrics
			* Worse performance on independence for low-income students than high-income students in later GPA prediction for four of five algorithms; one algorithm had worse separation and two algorithms had worse sufficiency