Fusion of score‐differencing and response similarity statistics for detecting examinees with item preknowledge

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, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mtext>JSFM</mml:mtext></mml:mrow> </mml:semantics> </mml:math> ) 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mtext>JSFM</mml:mtext></mml:mrow> </mml:semantics> </mml:math> exhibits strong cross-scenario stability and performs better than other fusion methods.