作弊
计算机科学
人工智能
项目反应理论
固定(群体遗传学)
贝叶斯概率
分层数据库模型
凝视
眼动
机器学习
心理学
统计
心理测量学
数据挖掘
社会心理学
数学
人口学
社会学
人口
作者
Kaiwen Man,Jeffrey R. Harring
标识
DOI:10.1177/00131644221136142
摘要
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.
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