A Machine Learning Approach for the Simultaneous Detection of Preknowledge in Examinees and Items When Both Are Unknown

Autor/inn/en	Pan, Yiqin; Wollack, James A.
Titel	A Machine Learning Approach for the Simultaneous Detection of Preknowledge in Examinees and Items When Both Are Unknown
Quelle	In: Educational Measurement: Issues and Practice, 42 (2023) 1, S.76-98 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0731-1745
DOI	10.1111/emip.12543
Schlagwörter	Artificial Intelligence; Prior Learning; Item Analysis; Test Content; Test Items; Knowledge Level; Informed Consent + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Vorkenntnisse; Itemanalyse; Testaufgabe; Test content; Wissensbasis
Abstract	Pan and Wollack (PW) proposed a machine learning method to detect compromised items. We extend the work of PW to an approach detecting compromised items and examinees with item preknowledge simultaneously and draw on ideas in ensemble learning to relax several limitations in the work of PW. The suggested approach also provides a confidence score, which is based on an autoencoder to represent our confidence that the detection result truly corresponds to item preknowledge. Simulation studies indicate that the proposed approach performs well in the detection of item preknowledge, and the confidence score can provide helpful information for users. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01