Presented at the 2022 National Council on Measurement in Education Annual Meeting

  • Approximation Answer and Response Similarity Analyses: A Practical Approach
  • Session Type: Coordinated Paper Session
  • Russell Smith (Alpine Testing Solutions)

Cheating damages the integrity of a testing program and can cause testing organizations significant losses. Security breaches can arise from individuals memorizing and sharing items, the concerted efforts of a test preparation company to harvest items and teach them to their customers, and answer copying or collusion among examinees during a testing event. Without proper detection, these types of cheating could remain undetected until their presence becomes significant enough to threaten test-score validity. It is crucial for a test sponsor to accurately identify cheaters and invalidate their scores to effectively deter cheating behaviors. However, many cheating detection techniques developed so far are based on complicated mathematical models and extensive ad-hoc data analyses and thus cannot be practically conducted on daily basis. Therefore, we propose a collaborative exercise in which five independent research groups each propose a method that could help effectively and efficiently detect cheaters in the operational setting. Each group will use the same data from two linear fixed-form IT certification exams with known security breaches. The five approaches will be comparatively evaluated regarding their accuracy in detecting cheating and feasibility to implement.

Additional papers presented during this session by:

  • Kirk Becker and Paul Edward Jones (Pearson VUE), Cengiz Zopluoglu (University of Oregon), Jennifer Davis (AWS), and Huijan Meng (AWS)
  • Session Organizer: Anjali Weber, Amazon Web Services (AWS)
  • Discussant: James Wollack, University of Wisconsin

To view the paper Dr. Smith presented during this session, click the download button below the media player. To view an animated excerpt from his session, click the play button below: