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Critique of Mark D. Shermis & Ben Hamner, 'Contrasting State-of-the-Art Automated Scoring of Essays: Analysis'
Get PDFAlthough the unpublished study by Shermis & Hamner (2012) received substantial publicity about its claim that automated essay scoring (AES) of student essays was as accurate as scoring by human readers, a close examination of the paper's methodology demonstrates that the data and analytic procedures employed in the study do not support such a claim. The most notable shortcoming in the study is the absence of any articulated construct for writing, the variable that is being measured. Indeed, half of the writing samples used were not essays but short one-paragraph responses involving literary analysis or reading comprehension that were not evaluated on any construct involving writing. In addition, the study's methodology employed one method for calculating the reliability of human readers and a different method for calculating the reliability of machines, this difference artificially privileging the machines in half the writing samples. Moreover, many of the study's conclusions were based on impressionistic and sometimes inaccurate comparisons drawn without the performance of any statistical tests. Finally, there was no standard testing of the model as a whole for significance, which, given the large number of comparisons, allowed machine variables to occasionally surpass human readers merely through random chance. These defects in methodology and reporting should prompt the authors to consider formally retracting the study. Furthermore, because of the widespread publicity surrounding this study and because its findings may be used by states and state consortia in implementing the Common Core State Standards, the authors should make the test data publicly available for analysis
State-Of-The-Art Automated Essay Scoring: Competition, Results, and Future Directions from a United States Demonstration
No full textThis article summarizes the highlights of two studies: a national demonstration that contrasted commercial vendors\u27 performance on automated essay scoring (AES) with that of human raters: and an international competition to match or exceed commercial vendor performance benchmarks. In these studies, the automated essay scoring engines performed well on five of seven measures and approximated human rater performance on the other two. With additional validity studies, it appears that automated essay scoring holds the potential to play a viable role in high-stakes writing assessments. (C) 2013 Elsevier Ltd. All rights reserved
Establishing a crosswalk between the Common European Framework for Languages (CEFR) and writing domains scored by automated essay scoring
No full textUsing ChatGPT to Score Essays and Short-Form Constructed Responses
Get PDFThis study aimed to determine if ChatGPT\u27s large language models could match the scoring accuracy of human and machine scores from the ASAP competition. The investigation focused on various prediction models, including linear regression, random forest, gradient boost, and boost. ChatGPT\u27s performance was evaluated against human raters using quadratic weighted kappa (QWK) metrics. Results indicated that while ChatGPT\u27s gradient boost model achieved QWKs close to human raters for some data sets, its overall performance was inconsistent and often lower than human scores. The study highlighted the need for further refinement, particularly in handling biases and ensuring scoring fairness. Despite these challenges, ChatGPT demonstrated potential for scoring efficiency, especially with domain-specific fine-tuning. The study concludes that ChatGPT can complement human scoring but requires additional development to be reliable for high-stakes assessments. Future research should improve model accuracy, address ethical considerations, and explore hybrid models combining ChatGPT with empirical methods.35 pages, 8 tables, 2 Figures, 27 reference
