Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests

Autor/inn/en	Ranger, Jochen; Schmidt, Nico; Wolgast, Anett
Titel	Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests
Quelle	In: Educational and Psychological Measurement, 83 (2023) 5, S.1033-1058 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ranger, Jochen)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221132723
Schlagwörter	Identification; Cheating; Information Retrieval; Tests; Algorithms; Data Collection; Artificial Intelligence; Accuracy + Suchen Sie Ihr Suchwort? Identifikation; Identifizierung; Prellen; Examination; Prüfung; Examen; Algorithm; Algorithmus; Data capture; Datensammlung; Künstliche Intelligenz
Abstract	Recent approaches to the detection of cheaters in tests employ detectors from the field of machine learning. Detectors based on supervised learning algorithms achieve high accuracy but require labeled data sets with identified cheaters for training. Labeled data sets are usually not available at an early stage of the assessment period. In this article, we discuss the approach of adapting a detector that was trained previously with a labeled training data set to a new unlabeled data set. The training and the new data set may contain data from different tests. The adaptation of detectors to new data or tasks is denominated as transfer learning in the field of machine learning. We first discuss the conditions under which a detector of cheating can be transferred. We then investigate whether the conditions are met in a real data set. We finally evaluate the benefits of transferring a detector of cheating. We find that a transferred detector has higher accuracy than an unsupervised detector of cheating. A naive transfer that consists of a simple reuse of the detector increases the accuracy considerably. A transfer via a self-labeling (SETRED) algorithm increases the accuracy slightly more than the naive transfer. The findings suggest that the detection of cheating might be improved by using existing detectors of cheating at an early stage of an assessment period. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01