Cheating Detection in Tests: A Systematic Review

Abstract

Despite the significance of high-stakes testing in education, cheating behavior in tests continues to undermine interpretation and use of a test score. Numerous cheating detection methods were created to counter the problem, but no study was done to keep track of literature accumulation in this area. This systematic review was conducted to explore the literature landscape in the area of method to detect cheating in tests, ultimately to serve as a milestone for the field. The guiding research question of this systematic review is “What is the current literature trend in the field of cheating detection in tests?” After an exhaustive database search, 62 studies were selected for the in-depth review. 27 variables were extracted from each primary study by nominal descriptive coding, binary coding, and numerical coding. Results were divided into three main themes, literature distribution, methodological overview, and methodological concern. The area of cheating detection method in tests was found in its emerging stage, yet continually expanding with numerous methods available for use. Literature in this area has rooms for improvement on population representation and the need for the same standard in assessing performance of the proposed method. Future research could explore cheating behavior on other regions aside from western countries (e.g., Asia, Middle East), as well as applying the emerging technology of machine learning to detect cheating behavior at a higher efficiency. Essentially, cheating detection method is a promising area for resource investment that could benefit education as a whole.