National Origin or National Location - Revision history

Valdemar: Add empty lines

2024-09-02T00:13:45Z

Add empty lines

← Older revision		Revision as of 20:13, 1 September 2024
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	Švábenský et al. (2024) [https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.82/2024.EDM-posters.82.pdf pdf]		Švábenský et al. (2024) [https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.82/2024.EDM-posters.82.pdf pdf]

	* Classification models for predicting grades (worse than an average grade, “unsuccessful”, or equal/better than an average grade, “successful”)		*Classification models for predicting grades (worse than an average grade, “unsuccessful”, or equal/better than an average grade, “successful”)
	* Investigating bias based on university students' regional background in the context of the Philippines		*Investigating bias based on university students' regional background in the context of the Philippines
	* Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas		*Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas
	* Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed		*Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed


	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
	* Inaccuracy of the U.S.-trained model was greater for students from countries with lower scores of national development (e.g. Indonesia, Vietnam, Moldova)

	Wang et al. (2018) [https://www.researchgate.net/publication/336009443_Monitoring_the_performance_of_human_and_automated_scores_for_spoken_responses 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)


			Wang et al. (2018) [https://www.researchgate.net/publication/336009443_Monitoring_the_performance_of_human_and_automated_scores_for_spoken_responses pdf]

			*Automated scoring model for evaluating English spoken responses
			*SpeechRater gave a significantly lower score than human raters for German students
			*SpeechRater scored gave higher scores than human raters for Chinese students, with H1-rater scores higher than mean

	* Automated scoring model for evaluating English spoken responses
	* SpeechRater gave a significantly lower score than human raters for German students
	* SpeechRater scored gave higher scores than human raters for Chinese students, with H1-rater scores higher than mean

			Ogan et al. (2015) [https://link.springer.com/content/pdf/10.1007/s40593-014-0034-8.pdf pdf]

	~~Ogan et al. (2015) [https://link~~.~~springer~~.~~com/content/pdf/10~~.~~1007/s40593-014-0034-8~~.~~pdf pdf]~~		*Multi-national models predicting learning gains from student's help-seeking behavior
			*Models built on only U.S. or combined data sets performed extremely poorly for Costa Rica
			*Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data

	* Multi-national models predicting learning gains from student's help-seeking behavior
	* Models built on only U.S. or combined data sets performed extremely poorly for Costa Rica
	* Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data<br />
	~~Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502?needAccess=true pdf]~~

	* A later version of automated scoring models for evaluating English essays, or e-rater		Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502?needAccess=true pdf]
	* E-rater gave better scores for test-takers from Chinese speakers (Mainland China, Taiwan, Hong Kong) and Korean speakers when assessing TOEFL (independent prompt) essay
	* E-rater gave lower scores for Arabic, Hindi, and Spanish speakers when assessing their written responses to independent prompt in TOEFL		*A later version of automated scoring models for evaluating English essays, or e-rater
			*E-rater gave better scores for test-takers from Chinese speakers (Mainland China, Taiwan, Hong Kong) and Korean speakers when assessing TOEFL (independent prompt) essay
			*E-rater gave lower scores for Arabic, Hindi, and Spanish speakers when assessing their written responses to independent prompt in TOEFL


	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]

	* 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 TOEFL essays (independent task) written by speakers of Chinese and Korean
	* E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean		*E-Rater correlated poorly with human rater and gave better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers
	* E-Rater correlated poorly with human rater and gave better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers

Valdemar: Reordered from latest (did not change content)

2024-09-02T00:11:49Z

Reordered from latest (did not change content)

@@ Line 6: / Line 6: @@
 * Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas
 * Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed
 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)
 Wang et al. (2018) [https://www.researchgate.net/publication/336009443_Monitoring_the_performance_of_human_and_automated_scores_for_spoken_responses pdf]
@@ Line 26: / Line 17: @@
 * SpeechRater gave a significantly lower score than human raters for German students
 * SpeechRater scored gave higher scores than human raters for Chinese students, with H1-rater scores higher than mean
@@ Line 34: / Line 37: @@
 * E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean
 * E-Rater correlated poorly with human rater and gave better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers

Valdemar at 00:05, 2 September 2024

2024-09-02T00:05:42Z

← Older revision		Revision as of 20:05, 1 September 2024
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	* Multi-national models predicting learning gains from student's help-seeking behavior		* Multi-national models predicting learning gains from student's help-seeking behavior
	* Models built on only U.S. or combined data sets performed extremely poorly for Costa Rica		* Models built on only U.S. or combined data sets performed extremely poorly for Costa Rica
	* Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data~~<br />~~		* Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data


	Li et al. (2021) [https://arxiv.org/pdf/2103.15212.pdf pdf]		Li et al. (2021) [https://arxiv.org/pdf/2103.15212.pdf pdf]

Valdemar: Add Svabensky@EDM'24

2024-09-02T00:05:13Z

Add Svabensky@EDM'24

← Older revision		Revision as of 20:05, 1 September 2024
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			Švábenský et al. (2024) [https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.82/2024.EDM-posters.82.pdf pdf]

			* Classification models for predicting grades (worse than an average grade, “unsuccessful”, or equal/better than an average grade, “successful”)
			* Investigating bias based on university students' regional background in the context of the Philippines
			* Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas
			* Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed


	Ogan et al. (2015) [https://link.springer.com/content/pdf/10.1007/s40593-014-0034-8.pdf pdf]		Ogan et al. (2015) [https://link.springer.com/content/pdf/10.1007/s40593-014-0034-8.pdf pdf]
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	* Multi-national models predicting learning gains from student's help-seeking behavior		* Multi-national models predicting learning gains from student's help-seeking behavior
	* Models built on only U.S. or combined data sets performed extremely poorly for Costa Rica		* Models built on only U.S. or combined data sets performed extremely poorly for Costa Rica
	* Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data		* Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data<br />



	Li et al. (2021) [https://arxiv.org/pdf/2103.15212.pdf pdf]		Li et al. (2021) [https://arxiv.org/pdf/2103.15212.pdf pdf]

Ryan: terminology corrections

2022-06-10T10:04:56Z

terminology corrections

← Older revision		Revision as of 06:04, 10 June 2022
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	* Automated scoring model for evaluating English spoken responses		* Automated scoring model for evaluating English spoken responses
	* SpeechRater gave a significantly lower score than human raters for German		* SpeechRater gave a significantly lower score than human raters for German students
	* SpeechRater scored ~~in favor of~~ Chinese ~~group~~, with H1-rater scores higher than mean		* SpeechRater scored gave higher scores than human raters for Chinese students, with H1-rater scores higher than mean

Seiyon at 12:34, 19 May 2022

2022-05-19T12:34:19Z

← Older revision		Revision as of 08:34, 19 May 2022
Line 33:		Line 33:

	* A later version of automated scoring models for evaluating English essays, or e-rater		* A later version of automated scoring models for evaluating English essays, or e-rater
	* E-rater gave better scores for test-takers from Chinese speakers (Mainland China, Taiwan, Hong Kong) and Korean speakers when assessing TOEFL (independent prompt) ~~and GRE essays~~		* E-rater gave better scores for test-takers from Chinese speakers (Mainland China, Taiwan, Hong Kong) and Korean speakers when assessing TOEFL (independent prompt) essay
	* E-rater gave lower scores for Arabic, Hindi, and Spanish speakers when assessing their written responses to independent prompt in TOEFL		* E-rater gave lower scores for Arabic, Hindi, and Spanish speakers when assessing their written responses to independent prompt in TOEFL

Seiyon at 12:31, 19 May 2022

2022-05-19T12:31:27Z

← Older revision		Revision as of 08:31, 19 May 2022
Line 26:		Line 26:

	* E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean		* E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean
	* E-Rater correlated poorly with human rater and ~~give~~ better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers		* E-Rater correlated poorly with human rater and gave better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers

Seiyon at 12:30, 19 May 2022

2022-05-19T12:30:02Z

← Older revision		Revision as of 08:30, 19 May 2022
Line 26:		Line 26:

	* E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean		* E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean
	* E-Rater correlated poorly with human rater and give better scores for GRE essays (both issue and argument prompts) written by Chinese speakers		* E-Rater correlated poorly with human rater and give better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers

Seiyon at 12:29, 19 May 2022

2022-05-19T12:29:42Z

← Older revision		Revision as of 08:29, 19 May 2022
Line 25:		Line 25:
	* 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 for TOEFL essays (independent task) written by speakers of Chinese and Korean		* E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean
	* E-Rater correlated poorly with human rater and give better scores for GRE essays (both issue and argument prompts) written by Chinese speakers		* E-Rater correlated poorly with human rater and give better scores for GRE essays (both issue and argument prompts) written by Chinese speakers

Seiyon at 11:52, 19 May 2022

2022-05-19T11:52:04Z

← Older revision		Revision as of 07:52, 19 May 2022
Line 27:		Line 27:
	* E-Rater gave significantly better scores for TOEFL essays (independent task) written by speakers of Chinese and Korean		* E-Rater gave significantly better scores for TOEFL essays (independent task) written by speakers of Chinese and Korean
	* E-Rater correlated poorly with human rater and give better scores for GRE essays (both issue and argument prompts) written by Chinese speakers		* E-Rater correlated poorly with human rater and give better scores for GRE essays (both issue and argument prompts) written by Chinese speakers



	Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502?needAccess=true pdf]		Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502?needAccess=true pdf]