White Learners in North America - Revision history

Shruti: addition new paper

2023-08-17T17:05:42Z

addition new paper

← Older revision		Revision as of 13:05, 17 August 2023
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	* Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.		* Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.
	*Models have approximately equal performance for White, African American and Hispanic/Latinx students.		*Models have approximately equal performance for White, African American and Hispanic/Latinx students.


			Almoubayyed et al. (2023) [https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.18/2023.EDM-long-papers.18.pdf pdf]

			* Models discovering generalization of the performance for reading comprehension ability in the context of middle school students’ usage of Carnegie Learning’s ITS for mathematics instruction
			* Model trained on smaller dataset achieves greater fairness in prediction for white and non-white students
			* For model trained on larger dataset, prediction is more accurate for white students than for non-white students.

Shruti: Addition

2023-06-29T01:04:57Z

Addition

@@ Line 1: / Line 1: @@
 Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
@@ Line 37: / Line 33: @@
 * Despite comparable accuracy, model tends to overpredict White students' performance
 * Several fairness correction methods equalize false positive and false negative rates across groups.

Shruti: addition new paper

2023-06-29T00:29:11Z

addition new paper

← Older revision		Revision as of 20:29, 28 June 2023
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			Permodo et al. (2023) [https://arxiv.org/abs/2304.06205 pdf]

			* Paper discusses system that predicts probabilities of on-time graduation
			* Prediction is less accurate for White students than other students

	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater

Shruti: year

2023-06-28T21:54:31Z

year

← Older revision		Revision as of 17:54, 28 June 2023
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	* Slightly better accuracy (~1%) across racial groups when including race in model		* Slightly better accuracy (~1%) across racial groups when including race in model

	Zhang et al. (~~in press~~) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf]		Zhang et al. (2022) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf]
	* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process		* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
	* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.		* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.

Ryan: Added Jeong et al (2022)

2022-08-04T20:05:27Z

Added Jeong et al (2022)

← Older revision		Revision as of 16:05, 4 August 2022
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	* Models not applying fairness constraints performed significantly worse for White students in terms of ABROCA		* 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		* Models applying fairness constraints performed equivalently for White and non-White students


			Jeong et al. (2022) [https://fated2022.github.io/assets/pdf/FATED-2022_paper_Jeong_Racial_Bias_ML_Algs.pdf]
			* Predicting 9th grade math score from academic performance, surveys, and demographic information
			* Despite comparable accuracy, model tends to overpredict White students' performance
			* Several fairness correction methods equalize false positive and false negative rates across groups.

Ryan: Added Sulaiman & Roy

2022-08-04T19:57:29Z

Added Sulaiman & Roy

← Older revision		Revision as of 15:57, 4 August 2022
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	* Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White)		* Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White)
	* Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students		* Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students


			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

Ryan: Added Li, Xing, & Leite (2022)

2022-07-04T13:20:02Z

Added Li, Xing, & Leite (2022)

← Older revision		Revision as of 09:20, 4 July 2022
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	* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.		* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
	* No racial/ethnic group consistently had best-performing detectors		* No racial/ethnic group consistently had best-performing detectors


			Li, Xing, & Leite (2022) [https://dl.acm.org/doi/pdf/10.1145/3506860.3506869?casa_token=OZmlaKB9XacAAAAA:2Bm5XYi8wh4riSmEigbHW_1bWJg0zeYqcGHkvfXyrrx_h1YUdnsLE2qOoj4aQRRBrE4VZjPrGw pdf]
			* Models predicting whether two students will communicate on an online discussion forum
			* Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White)
			* Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students

Ryan: added Zhang article

2022-06-10T10:14:30Z

added Zhang article

← Older revision		Revision as of 06:14, 10 June 2022
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	* Roughly equal accuracy across racial groups		* Roughly equal accuracy across racial groups
	* Slightly better accuracy (~1%) across racial groups when including race in model		* Slightly better accuracy (~1%) across racial groups when including race in model

			Zhang et al. (in press) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf]
			* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
			* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
			* No racial/ethnic group consistently had best-performing detectors

Ryan: Added Jiang & Pardos

2022-05-21T13:02:58Z

Added Jiang & Pardos

← Older revision		Revision as of 09:02, 21 May 2022
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	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students		* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students than other groups
	than other groups

			Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
			* Predicting university course grades using LSTM
			* Roughly equal accuracy across racial groups
			* Slightly better accuracy (~1%) across racial groups when including race in model

Ryan: clarification

2022-05-19T13:50:16Z

clarification

← Older revision		Revision as of 09:50, 19 May 2022
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	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students		* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students
			than other groups