Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory
Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory
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DOI：
10.1007/978-3-030-52240-7_61
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发表时间：
2020-06-10
期刊：
影响因子：
--
通讯作者：
Uchida Y
中科院分区：
文献类型：
--
作者：
Uto M;Uchida Y
使用深神经网络（DNN）的自动求助方法（ASAG）方法已达到最新的精度，但是，高赌注和大规模考试需要进一步改进许多测试者。提高评分精度，我们提出了一种新的ASAG方法，将常规的DNN-ASAG模型和项目响应理论（IRT）模型结合在一起。在同一测试中，她对目标短路问题提供的客观问题的真实回答。来自DNN-ASAG模型的中间层。
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.
DOI：
10.1007/s40593-014-0026-8
发表时间：
2015-03-01
影响因子：
4.9
作者：
Burrows, Steven;Gurevych, Iryna;Stein, Benno
通讯作者：
Stein, Benno
影响因子：
3.7
作者：
Uto, Masaki;Ueno, Maomi
通讯作者：
Ueno, Maomi