Difference between revisions of "National Origin or National Location"
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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 | |||
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] | |||
*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] | |||
*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 | |||
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 | |||
*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. ( | 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 | ||
* E- | *E-Rater gave significantly better scores than human rater for TOEFL essays (independent task) written by speakers of Chinese and Korean | ||
* E-rater gave | *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 | ||
Latest revision as of 19:13, 1 September 2024
Švábenský et al. (2024) 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
Li et al. (2021) 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) 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
Ogan et al. (2015) 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
Bridgeman et al. (2012) pdf
- 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) page
- 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 correlated poorly with human rater and gave better scores than human rater for GRE essays (both issue and argument prompts) written by Chinese speakers