Automatic Question Tagging with Deep Neural Networks

Autor/inn/en	Sun, Bo; Zhu, Yunzong; Xiao, Yongkang; Xiao, Rong; Wei, Yungang
Titel	Automatic Question Tagging with Deep Neural Networks
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 1, S.29-43 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sun, Bo) ORCID (Zhu, Yunzong)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2018.2808187
Schlagwörter	Computer Assisted Testing; Student Evaluation; Test Items; Multiple Choice Tests; Information Retrieval; Neurological Organization; Item Banks; Adaptive Testing; Semantics; Metadata; Concept Mapping; Cues; Attention Control; English Instruction; Elementary Secondary Education; College Students; Indexing + Suchen Sie Ihr Suchwort? Schulnote; Studentische Bewertung; Test content; Testaufgabe; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Semantik; Metadaten; Concept Map; Stichwort; Aufmerksamkeitstest; English langauage lessons; Englischunterricht; Collegestudent; Indexierung; Sacherschließung
Abstract	In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging, an alternative, has not been studied extensively. In this paper, we propose a position-based attention model and keywords-based model to automatically tag questions with knowledge units. With regard to multiple-choice questions, the proposed models employ mechanisms to capture useful information from keywords to enhance tagging performance. Unlike traditional machine learning-based tagging methods, our models utilize deep neural networks to represent questions using contextual information. The experimental results show that our proposed models outperform some traditional classification and topic methods by a large margin on an English question bank dataset. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01