Difference between revisions of "National Origin or National Location"
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*models predicting student learning gains from a mixture of their behaviors related to help-seeking | *models predicting student learning gains from a mixture of their behaviors related to help-seeking | ||
*Models built using data from learners in the Philippines, Costa Rica, and the United States were each more accurate on students from their own countries than for students from other countries. | *Models built using data from learners in the Philippines, Costa Rica, and the United States were each more accurate on students from their own countries than for students from other countries. | ||
Li and colleagues (2021) | |||
* Model predicting student achievement on a standardized examination | |||
* Highly accurate for students from other economically developed countries but less accurate for students from less economically developed countries. | |||
Revision as of 02:28, 24 January 2022
Bridgeman, Trapani, and Attali (2009) [pdf]
- E-Rater system that automatically grades a student’s essay
- Inaccurately high scores were given to Chinese and Korean students
- System showed poor correlation for GRE essay scores of Chinese students
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 and colleagues (2015) [pdf]
- models predicting student learning gains from a mixture of their behaviors related to help-seeking
- Models built using data from learners in the Philippines, Costa Rica, and the United States were each more accurate on students from their own countries than for students from other countries.
Li and colleagues (2021)
- Model predicting student achievement on a standardized examination
- Highly accurate for students from other economically developed countries but less accurate for students from less economically developed countries.