Identifying Enemy Item Pairs Using Natural Language Processing

Autor/inn/en	Becker, Kirk A.; Kao, Shu-chuan
Titel	Identifying Enemy Item Pairs Using Natural Language Processing
Quelle	In: Journal of Applied Testing Technology, 23 (2022), S.41-52 (12 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Item Banks; Natural Language Processing; Computer Assisted Testing; Scoring; Test Items; Identification; Test Theory; Semantics; Matrices; Classification + Suchen Sie Ihr Suchwort? Natürliche Sprache; Bewertung; Test content; Testaufgabe; Identifikation; Identifizierung; Testtheorie; Semantik; Matrizenrechnung; Classification system; Klassifikation; Klassifikationssystem
Abstract	Natural Language Processing (NLP) offers methods for understanding and quantifying the similarity between written documents. Within the testing industry these methods have been used for automatic item generation, automated scoring of text and speech, modeling item characteristics, automatic question answering, machine translation, and automated referencing. This paper presents research into the use of NLP for the identification of enemy and duplicate items to improve the maintenance of test item banks. Similar pairs of items can be identified using NLP, limiting the number of items content experts must review to identify enemy and duplicate items. Results from multiple testing programs show that previously unidentified enemy pairs can be discovered with this method. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01