Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory

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Abstract

Automated short-answer grading (ASAG) methods using deep neural networks (DNN) have achieved state-of-the-art accuracy. However, further improvement is required for high-stakes and large-scale examinations because even a small scoring error will affect many test-takers. To improve scoring accuracy, we propose a new ASAG method that combines a conventional DNN-ASAG model and an item response theory (IRT) model. Our method uses an IRT model to estimate the test-taker’s ability from his/her true-false responses to objective questions that are offered with a target short-answer question in the same test. Then, the target short-answer score is predicted by jointly using the ability value and a distributed short-answer representation, which is obtained from an intermediate layer of a DNN-ASAG model.

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Uto, M., & Uchida, Y. (2020). Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12164 LNAI, pp. 334–339). Springer. https://doi.org/10.1007/978-3-030-52240-7_61

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