Automated Essay Scoring

Bridgeman et al. (2009) pdf

• Automated scoring models for evaluating English essays, or e-rater
• E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students
• 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 system performed comparably accurately for male and female students when assessing their 11th grade essays, TOEFL, and GRE writings

Bridgeman et al. (2012) pdf

• A later version of automated scoring models for evaluating English essays, or e-rater
• E-rater gave significantly lower score than human rater when assessing African-American students’ written responses to issue prompt in GRE
• 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
• E-Rater system correlated comparably well with human rater when assessing TOEFL and GRE essays written by male and female students

Ramineni & Williamson (2018) pdf

• Revised automated scoring engine for assessing GSE essay

• E-rater gave African American test-takers significantly lower scores than human raters when assessing their written responses to argument prompts
• The shorter essays written by African American test-takers were more likely to receive lower scores as showing weakness in content and organization

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