Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of…

Addressing academic integrity in the age of AI is essential to ensure honesty and student success. This Spotlight will help you learn about how educators nationwide are approaching AI in teaching and learning; review data investigating how many students are actually using AI to cheat; examine strategies teachers are using to fight AI cheating;…

In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008; Wang & Xu, 2015): solution behavior, rapid guessing behavior, and cheating behavior. Oftentimes examinees do not always solve all items due to various…

Cheating detection is an important issue in standardized testing, especially in large-scale settings. Statistical approaches are often computationally intensive and require specialised software to conduct. We present a two-stage approach that quickly filters suspected groups using statistical testing on an IRT-based answer-copying index. We also…

Cheating and unethical behavior in the context of high-stakes accountability has been well documented. Recently, the US Department of Education found Texas schools failing to comply with the nation's special education law as a result of a state accountability policy. This article examines how a group of principals recognized for their…