National Origin or National Location

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

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) 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

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