An Automatic Short-Answer Grading Model for Semi-Open-Ended Questions

Autor/inn/en	Zhang, Lishan; Huang, Yuwei; Yang, Xi; Yu, Shengquan; Zhuang, Fuzhen
Titel	An Automatic Short-Answer Grading Model for Semi-Open-Ended Questions
Quelle	In: Interactive Learning Environments, 30 (2022) 1, S.177-190 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhang, Lishan) ORCID (Yu, Shengquan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2019.1648300
Schlagwörter	Automation; Grading; Models; Artificial Intelligence; Reading Comprehension; Computer Uses in Education; Classification + Suchen Sie Ihr Suchwort? Notengebung; Schulnote; Analogiemodell; Künstliche Intelligenz; Leseverstehen; Computernutzung; Classification system; Klassifikation; Klassifikationssystem
Abstract	Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This method performs well for closed-ended questions that have single or very limited numbers of correct answers. However, some short-answer questions ask students to express their own thoughts based on various facts; hence, they have no reference answers. Such questions are called semi-open-ended short-answer questions. Questions of this type often appear in reading comprehension assessments. In this paper, we developed an automatic semi-open-ended short-answer grading model that integrates both domain-general and domain-specific information. The model also utilizes a long-short-term-memory recurrent neural network to learn the representation in the classifier so that word sequence information is considered. In experiments on 7 reading comprehension questions and over 16,000 short-answer samples, our proposed automatic grading model demonstrates its advantage over existing models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01