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    <abstract>This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs). While previous approaches train on manually labeled data to predict the human-ratings assigned to SAQ responses, the approach presented here models examinee proficiency directly and does not require manually labeled data to train on. We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs). First, the latent trait of examinee proficiency is measured using the scored MCQs and then a model is trained on the experimental SAQ responses as input, aiming to predict proficiency as its target variable. The predicted value is then used as a “score” for the SAQ response and evaluated in terms of its contribution to the precision of proficiency estimation.</abstract>
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%0 Conference Proceedings
%T Automated Prediction of Examinee Proficiency from Short-Answer Questions
%A Ha, Le An
%A Yaneva, Victoria
%A Harik, Polina
%A Pandian, Ravi
%A Morales, Amy
%A Clauser, Brian
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ha-etal-2020-automated
%X This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs). While previous approaches train on manually labeled data to predict the human-ratings assigned to SAQ responses, the approach presented here models examinee proficiency directly and does not require manually labeled data to train on. We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs). First, the latent trait of examinee proficiency is measured using the scored MCQs and then a model is trained on the experimental SAQ responses as input, aiming to predict proficiency as its target variable. The predicted value is then used as a “score” for the SAQ response and evaluated in terms of its contribution to the precision of proficiency estimation.
%R 10.18653/v1/2020.coling-main.77
%U https://aclanthology.org/2020.coling-main.77
%U https://doi.org/10.18653/v1/2020.coling-main.77
%P 893-903

Markdown (Informal)

[Automated Prediction of Examinee Proficiency from Short-Answer Questions](https://aclanthology.org/2020.coling-main.77) (Ha et al., COLING 2020)

• Automated Prediction of Examinee Proficiency from Short-Answer Questions (Ha et al., COLING 2020)

ACL

• Le An Ha, Victoria Yaneva, Polina Harik, Ravi Pandian, Amy Morales, and Brian Clauser. 2020. Automated Prediction of Examinee Proficiency from Short-Answer Questions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 893–903, Barcelona, Spain (Online). International Committee on Computational Linguistics.