考试(生物学)
机器学习
计算机科学
人工智能
认证
推论
试验数据
数据收集
应用心理学
可靠性(半导体)
心理学
数据科学
统计
物理
法学
程序设计语言
功率(物理)
古生物学
政治学
生物
量子力学
数学
标识
DOI:10.1177/00131644231193625
摘要
In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such tests to provide reliable, accurate inference on a test-taker’s performance could be jeopardized by aberrant test-taking practices, for instance, practicing real items prior to the test. As a result, it is crucial for administrators of such assessments to develop strategies that detect potential aberrant test-takers after data collection. The aim of this study is to explore the implementation of machine learning methods in combination with multimodal data fusion strategies that integrate bio-information technology, such as eye-tracking, and psychometric measures, including response times and item responses, to detect aberrant test-taking behaviors in technology-assisted remote testing settings.
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