ERIC Number: EJ1390943
Record Type: Journal
Publication Date: 2023-Oct
Pages: 22
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0013-1644
EISSN: EISSN-1552-3888
Available Date: N/A
Detecting Preknowledge Cheating via Innovative Measures: A Mixture Hierarchical Model for Jointly Modeling Item Responses, Response Times, and Visual Fixation Counts
Educational and Psychological Measurement, v83 n5 p1059-1080 Oct 2023
Preknowledge cheating jeopardizes the validity of inferences based on test results. Many methods have been developed to detect preknowledge cheating by jointly analyzing item responses and response times. Gaze fixations, an essential eye-tracker measure, can be utilized to help detect aberrant testing behavior with improved accuracy beyond using product and process data types in isolation. As such, this study proposes a mixture hierarchical model that integrates item responses, response times, and visual fixation counts collected from an eye-tracker (a) to detect aberrant test takers who have different levels of preknowledge and (b) to account for nuances in behavioral patterns between normally-behaved and aberrant examinees. A Bayesian approach to estimating model parameters is carried out via an MCMC algorithm. Finally, the proposed model is applied to experimental data to illustrate how the model can be used to identify test takers having preknowledge on the test items.
Descriptors: Cheating, Reaction Time, Test Items, Responses, Eye Movements, Behavior Patterns, Test Bias, Bayesian Statistics, College Students, Accuracy
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A