Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests.
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...
| Published in: | Educational & Psychological Measurement Vol. 83; no. 5; pp. 1033 - 1059 |
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| Main Authors: | , , |
| Format: | Article |
| Published: |
Sage Publications Inc.
Oct2023
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=171309017&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 171309017 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Oct2023 vid: 83 iid: 5 pid: 344 pub: Sage Publications Inc. artinfo: ui: 171309017 10.1177/00131644221132723 ppf: 1033 ppct: 26 formats: tig: atl: Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests. aug: au: Ranger, Jochen Schmidt, Nico Wolgast, Anett affil: Martin-Luther-University Halle-Wittenberg, Germany University of Applied Sciences FHM, Hannover, Germany su: Student cheating Machine learning Descriptive statistics Computer terminals Data mining Algorithms sug: subj: Student cheating Computer and peripheral equipment manufacturing Computer Terminal and Other Computer Peripheral Equipment Manufacturing Computer, computer peripheral and pre-packaged software merchant wholesalers Machine learning Descriptive statistics Computer terminals Data mining Algorithms keyword: cheating data mining transfer learning cheating data mining transfer learning ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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