Belov Dmitry I, Toton Sarah L
Psychometric Research, Law School Admission Council, Newtown, PA, USA.
Data Forensics, Caveon, Midvale, UT, USA.
Appl Psychol Meas. 2022 Jun;46(4):273-287. doi: 10.1177/01466216221084202. Epub 2022 Apr 21.
Recently, Belov & Wollack (2021) developed a method for detecting groups of colluding examinees as cliques in a graph. The objective of this article is to study how the performance of their method on real data with item preknowledge (IP) depends on the mechanism of edge formation governed by a response similarity index (RSI). This study resulted in the development of three new RSIs and demonstrated a remarkable advantage of combining responses and response times for detecting examinees with IP. Possible extensions of this study and recommendations for practitioners were formulated.
最近，贝洛夫和沃莱克（2021年）开发了一种方法，用于在图中将相互勾结的考生群体检测为团。本文的目的是研究他们的方法在具有项目先验知识（IP）的真实数据上的性能如何取决于由响应相似性指数（RSI）控制的边形成机制。这项研究促成了三种新的RSI的开发，并证明了结合响应和响应时间来检测具有IP的考生具有显著优势。本文还阐述了该研究可能的扩展方向，并为从业者提供了建议。
