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Detecting Aberrant Test-Taking Behaviors in Computer-Based Testing Using One-Dimensional Convolutional Neural Networks

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Τίτλος: Detecting Aberrant Test-Taking Behaviors in Computer-Based Testing Using One-Dimensional Convolutional Neural Networks
Γλώσσα: English
Συγγραφείς: Chunyu Piao, Jiwei Zhang (ORCID 0000-0002-7454-1673), Jing Lu (ORCID 0000-0001-8333-9146)
Πηγή: Educational Measurement: Issues and Practice. 2025 44(4):5-17.
Διαθεσιμότητα: 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
Peer Reviewed: Y
Page Count: 13
Ημερομηνία έκδοσης: 2025
Τύπος εγγράφου: Journal Articles
Reports - Research
Descriptors: Student Behavior, Test Wiseness, Computer Assisted Testing, Item Response Theory, Reaction Time, Prior Learning, Artificial Intelligence, Models, Accuracy, Identification
DOI: 10.1111/emip.70010
ISSN: 0731-1745
1745-3992
Περίληψη: In educational assessments, detecting aberrant test-taking behaviors is crucial for ensuring test validity and reliability. We propose a deep learning method based on a one-dimensional convolutional neural network (1D-CNN) combined with a sliding window technique, and detect aberrant behaviors by analyzing responses and response times. To validate our method, we conduct simulations with two typical aberrant scenarios: test speededness and item preknowledge. We compare our method with several established methods, including extreme gradient boosting, logistic regression, decision trees, and multilayer perceptrons, as well as traditional educational methods such as person-fit and cumulative sum (CUSUM) statistics. Results show that the 1D-CNN model outperforms traditional methods in accuracy, sensitivity, specificity, and the area under curve (AUC), demonstrating superior capability in detecting complex aberrant patterns. Furthermore, real data validation confirms the practical effectiveness and applicability of our method.
Abstractor: As Provided
Entry Date: 2026
Αριθμός Καταχώρησης: EJ1493244
Βάση Δεδομένων: ERIC
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  Data: Detecting Aberrant Test-Taking Behaviors in Computer-Based Testing Using One-Dimensional Convolutional Neural Networks
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  Data: <searchLink fieldCode="AR" term="%22Chunyu+Piao%22">Chunyu Piao</searchLink><br /><searchLink fieldCode="AR" term="%22Jiwei+Zhang%22">Jiwei Zhang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7454-1673">0000-0002-7454-1673</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jing+Lu%22">Jing Lu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8333-9146">0000-0001-8333-9146</externalLink>)
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  Data: 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
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  Data: 13
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Wiseness%22">Test Wiseness</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Reaction+Time%22">Reaction Time</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+Learning%22">Prior Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Identification%22">Identification</searchLink>
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  Data: 10.1111/emip.70010
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  Data: In educational assessments, detecting aberrant test-taking behaviors is crucial for ensuring test validity and reliability. We propose a deep learning method based on a one-dimensional convolutional neural network (1D-CNN) combined with a sliding window technique, and detect aberrant behaviors by analyzing responses and response times. To validate our method, we conduct simulations with two typical aberrant scenarios: test speededness and item preknowledge. We compare our method with several established methods, including extreme gradient boosting, logistic regression, decision trees, and multilayer perceptrons, as well as traditional educational methods such as person-fit and cumulative sum (CUSUM) statistics. Results show that the 1D-CNN model outperforms traditional methods in accuracy, sensitivity, specificity, and the area under curve (AUC), demonstrating superior capability in detecting complex aberrant patterns. Furthermore, real data validation confirms the practical effectiveness and applicability of our method.
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  Data: 2026
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      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Test Wiseness
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      – SubjectFull: Computer Assisted Testing
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      – SubjectFull: Item Response Theory
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      – SubjectFull: Reaction Time
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      – SubjectFull: Prior Learning
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Models
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      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Identification
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      – TitleFull: Detecting Aberrant Test-Taking Behaviors in Computer-Based Testing Using One-Dimensional Convolutional Neural Networks
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              Type: published
              Y: 2025
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            – TitleFull: Educational Measurement: Issues and Practice
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