Neural Automated Short-Answer Grading Considering Examinee-Specific Features

Automated short-answer grading (ASAG) is the task of automatically assigning scores to examinees' textual responses to short-answer questions. Recently, various ASAG models based on deep neural networks (DNNs) have been proposed and some have achieved high accuracy. Conventional ASAG models are generally trained and used independently for each short-answer question, even when a given test contains multiple short-answer questions. However, because tests are tools for evaluating particular examinee traits, multiple questions on the same test are generally designed to measure similar latent traits in examinees. Latent examinee traits across multiple short-answer questions may thus function as effective auxiliary features for ASAG. We therefore propose a new DNN-based ASAG model with an examinee-aware architecture that extracts examinee-specific features, including latent traits, from answer texts across multiple questions.