Difference between revisions of "National Origin or National Location"

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Bridgeman, Trapani, and Attali (2012) [https://www.researchgate.net/publication/233291671_Comparison_of_Human_and_Machine_Scoring_of_Essays_Differences_by_Gender_Ethnicity_and_Country pdf]
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
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-rater gave slightly higher scores for test-takers from Chinese speakers (Mainland China, Taiwan, Hong Kong) and Korean speakers when assessing written responses to independent prompt in Test of English as a Foreign Language (TOEFL)
* 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 slightly lower scores for Arabic and Hindi 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
* E-rater gave  significantly higher scores for test-takers from Mainland China than from Taiwan, Korea and Japan when assessing their GRE writings which tended to be below average on grammar, usage, and mechanics but longest response

Revision as of 17:09, 18 May 2022

Bridgeman, Trapani, and Attali (2009) pdf

  • 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 correlated poorly with human rater and give better scores for GRE essays (both issue and argument prompts) written by Chinese speakers


Bridgeman, Trapani, and Attali (2012) pdf

  • A later version of E-Rater system for automatic grading of GSE essay
  • Chinese students were given higher scores than when graded by human essay raters
  • Speakers of Arabic and Hindi were given lower scores


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 dataset, except for Philippines which was outperformed slightly by U.S. model


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
  • SpeechRater scored in favor of Chinese group, with H1-rater scores higher than mean


Bridgeman et al. (2009) pdf

  • Automated scoring models for evaluating English essays, or e-rater
  • E-rater gave significantly higher score for students from China and South Korea than 14 other countries when assessing independent writing task in Test of English as a Foreign Language (TOEFL)
  • E-rater gave slightly higher scores for GRE analytical writing, both argument and issue prompts, by students from China whose written responses tended to be the longest and below average on grammar, usage and mechanics


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) and GRE essays
  • E-rater gave lower scores for Arabic, Hindi, and Spanish speakers when assessing their written responses to independent prompt in TOEFL