Literaturnachweis - Detailanzeige
Autor/inn/en | Pan, Yiqin; Wollack, James A. |
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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 |
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 |
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 |