Latino/Latina/Latinx/Hispanic Learners in North America - Revision history

Shruti at 00:49, 29 June 2023

2023-06-29T00:49:33Z

← Older revision		Revision as of 20:49, 28 June 2023
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	* Despite comparable accuracy, model tends to underpredict Hispanic students' performance		* Despite comparable accuracy, model tends to underpredict Hispanic students' performance
	* Several fairness correction methods equalize false positive and false negative rates across groups.		* Several fairness correction methods equalize false positive and false negative rates across groups.


	Zhang et al.(2023) [https://learninganalytics.upenn.edu/ryanbaker/ISLS23_annotation%20detector_short_submit.pdf pdf]		Zhang et al.(2023) [https://learninganalytics.upenn.edu/ryanbaker/ISLS23_annotation%20detector_short_submit.pdf pdf]
	* 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 Hispanic/Latinx, African American, and White students.		* Models have approximately equal performance for Hispanic/Latinx, African American, and White students.

Shruti: Addition

2023-06-29T00:49:11Z

Addition

← Older revision		Revision as of 20:49, 28 June 2023
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	Zhang et al. (~~in press~~) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf pdf]		Zhang et al. (2022) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf 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.
Line 55:		Line 55:
	* Despite comparable accuracy, model tends to underpredict Hispanic students' performance		* Despite comparable accuracy, model tends to underpredict Hispanic students' performance
	* Several fairness correction methods equalize false positive and false negative rates across groups.		* Several fairness correction methods equalize false positive and false negative rates across groups.

			Zhang et al.(2023) [https://learninganalytics.upenn.edu/ryanbaker/ISLS23_annotation%20detector_short_submit.pdf pdf]
			* 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 Hispanic/Latinx, African American, and White students.

Ryan: Added Jeong et al (2022)

2022-08-04T20:05:05Z

Added Jeong et al (2022)

← Older revision		Revision as of 16:05, 4 August 2022
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	* Predicting course grades and later GPA at public U.S. university		* Predicting course grades and later GPA at public U.S. university
	* Poorer independence, separation, sufficiency for Latinx students than white students for five different classic machine learning algorithms		* Poorer independence, separation, sufficiency for Latinx students than white students for five different classic machine learning algorithms


			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 underpredict Hispanic students' performance
			* Several fairness correction methods equalize false positive and false negative rates across groups.

Ryan at 14:45, 13 June 2022

2022-06-13T14:45:43Z

← Older revision		Revision as of 10:45, 13 June 2022
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	[https://dl.acm.org/doi/pdf/10.1145/3386527.3406755 pdf]		[https://dl.acm.org/doi/pdf/10.1145/3386527.3406755 pdf]
	* Predicting course grades and later GPA at public U.S. university		* Predicting course grades and later GPA at public U.S. university
	* Poorer independence, separation sufficiency for Latinx students than white students for five different classic machine learning algorithms		* Poorer independence, separation, sufficiency for Latinx students than white students for five different classic machine learning algorithms

Ryan at 14:42, 13 June 2022

2022-06-13T14:42:26Z

← Older revision		Revision as of 10:42, 13 June 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


	Kung & Yu (2020)		Kung & Yu (2020)

Ryan: added Kung & Yu (2020)

2022-06-13T14:42:12Z

added Kung & Yu (2020)

← Older revision		Revision as of 10:42, 13 June 2022
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	Zhang et al. (in press) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf]		Zhang et al. (in press) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf 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.
	* No racial/ethnic group consistently had best-performing detectors		* No racial/ethnic group consistently had best-performing detectors

			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
			* Poorer independence, separation sufficiency for Latinx students than white students for five different classic machine learning algorithms

Ryan: added missing citation

2022-06-10T10:12:17Z

added missing citation

← Older revision		Revision as of 06:12, 10 June 2022
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	Zhang et al. (in press)		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		* 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.
	* No racial/ethnic group consistently had best-performing detectors		* No racial/ethnic group consistently had best-performing detectors

Ryan: re-ordering

2022-06-10T10:01:56Z

re-ordering

← Older revision		Revision as of 06:01, 10 June 2022
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	Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]		Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
	* Models predicting student's high school dropout		* Models predicting student's high school dropout
	* The decision trees showed little difference in AUC among White, Black~~, Hispanic~~, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.		* The decision trees showed little difference in AUC among Hispanic, White, Black, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.


	Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]		Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
	* Models predicting college success (or median grade or above)		* Models predicting college success (or median grade or above)
	* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander~~, Hispanic~~, and Multicultural) than non-URM students (White and Asian)		* Random forest algorithms performed significantly worse for underrepresented minority students (URM; Hispanic, American Indian, Black, Hawaiian or Pacific Islander, and Multicultural) than non-URM students (White and Asian)
	* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values		* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Joyce at 17:22, 3 June 2022

2022-06-03T17:22:02Z

← Older revision		Revision as of 13:22, 3 June 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
	* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students		* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students


	Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]		Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
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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)
			* 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 (2021)

2022-05-21T13:01:47Z

Added Jiang & Pardos (2021)

← Older revision		Revision as of 09:01, 21 May 2022
Line 31:		Line 31:
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students		* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students

			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