Xu Yongze, He Ruihang, Huang Meiwei, Luo Fang
Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai City, Guangdong Province, China.
Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China.
Br J Math Stat Psychol. 2025 Nov;78(3):911-938. doi: 10.1111/bmsp.12388. Epub 2025 Mar 25.
Item preknowledge (IP) is a prevalent form of test fraud in educational assessment that can compromise test validity. Two common methods for detecting examinees with IP are score-differencing statistics and response similarity index (RSI). These statistics have different applications and respective advantages. In this paper, we propose a new method (Joint Survival Function Method, ) to combine these two types of statistics to calculate a fusion statistic that tries to address the issue of distribution differences between the original indicators. By combining the advantages of the original indicators, the fusion statistic can more effectively detect examinees with IP. We fused two typical RSI and four typical score-differencing statistics using different methods and compared their performance. The results demonstrate that the proposed exhibits strong cross-scenario stability and performs better than other fusion methods.
项目预知识（IP）是教育评估中一种普遍存在的考试欺诈形式，它会损害考试的有效性。检测有IP行为考生的两种常用方法是分数差异统计和反应相似性指数（RSI）。这些统计方法有不同的应用和各自的优势。在本文中，我们提出了一种新方法（联合生存函数法）来结合这两种统计方法，以计算一个融合统计量，试图解决原始指标之间的分布差异问题。通过结合原始指标的优势，融合统计量可以更有效地检测出有IP行为的考生。我们使用不同方法融合了两种典型的RSI和四种典型的分数差异统计量，并比较了它们的性能。结果表明，所提出的方法具有很强的跨场景稳定性，并且比其他融合方法表现更好。
