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通过创新措施检测预知作弊:一种混合层次模型,用于联合建模项目响应,响应时间和视觉固定计数

Kaiwen Man¹, Jeffrey R Harring²

¹University of Alabama, Tuscaloosa, USA.

Educational and psychological measurement

|September 4, 2023

概括

这项研究引入了一种新的模型,通过分析项目响应,响应时间和眼睛跟踪数据 (凝视定位) 来检测测试中的预知作弊. 该模型准确地识别了具有先前知识的异常测试者.

科学领域:

教育测量教育的测量
心理测量心理测量心理测量
认知心理学认知心理学

背景情况:

预知作弊损害了测试的有效性.
现有的方法分析项目响应和响应时间.
眼睛跟踪数据,特别是凝视定位,为增强检测异常测试行为提供了潜在的潜力.

研究的目的:

提出一种新的混合层次模型,整合多个数据源,用于预知作弊检测.
为了识别异常的考生,具有不同程度的预知.
为了区分正常和异常受试者之间的行为模式.

主要方法:

混合物层次模型的开发.
整合物品响应,响应时间和视觉固定计数从眼睛跟踪.
用马尔科夫链蒙特卡洛 (MCMC) 应用贝叶斯方法进行参数估计.

主要成果:

拟议的模型有效地检测出异常的考生.
该模型解释了预先知识水平的差异.
异常受试者的行为模式与正常受试者的行为模式有区别.

结论:

综合模型提高了检测预知作弊的准确性.
眼睛跟踪数据为考试行为提供了有价值的见解.
这种方法提供了一种可靠的方法来确保测试完整性.

关键词:

用眼睛追踪来进行追踪.凝视固定计数计数时间项目响应理论是物品响应理论.联合建模联合建模响应时间响应时间技术增强评估技术增强评估

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