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Autoencoder-based confidence score for item preknowledge detection.

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Název: Autoencoder-based confidence score for item preknowledge detection.
Autoři: Pan, Yiqin
Zdroj: Behaviormetrika; Jul2025, Vol. 52 Issue 2, p317-342, 26p
Témata: AUTOENCODERS, DEEP learning, PRIOR learning, SIMULATION methods & models, DETECTION algorithms, SIGNAL detection, TEST design
Abstrakt: Item compromise and preknowledge pose significant challenges in educational testing, leading to the development of various detection methods. Despite advancements, existing approaches often lack a confidence score to indicate the certainty that the detection result truly corresponds to item preknowledge. Building upon the work of Pan and Wollack (Educ Meas Issues Pract 42:76–98, 2023; PW23), we introduced a confidence score that leverages autoencoders—a deep learning model utilizing both response scores and times. Our simulation studies demonstrate that this confidence score accurately assesses the likelihood of detection results aligning with actual item preknowledge. Notably, the proposed confidence score surpasses the PW23 confidence score in effectiveness, particularly in scenarios with a small magnitude of item preknowledge. [ABSTRACT FROM AUTHOR]
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Databáze: Biomedical Index
Popis
Abstrakt:Item compromise and preknowledge pose significant challenges in educational testing, leading to the development of various detection methods. Despite advancements, existing approaches often lack a confidence score to indicate the certainty that the detection result truly corresponds to item preknowledge. Building upon the work of Pan and Wollack (Educ Meas Issues Pract 42:76–98, 2023; PW23), we introduced a confidence score that leverages autoencoders—a deep learning model utilizing both response scores and times. Our simulation studies demonstrate that this confidence score accurately assesses the likelihood of detection results aligning with actual item preknowledge. Notably, the proposed confidence score surpasses the PW23 confidence score in effectiveness, particularly in scenarios with a small magnitude of item preknowledge. [ABSTRACT FROM AUTHOR]
ISSN:03857417
DOI:10.1007/s41237-024-00243-4