A New Approach to Cheating Detection in Online Exams

Due to the COVID-19 pandemic, there have been significant changes in different aspects of education around the world, including the testing process. Online testing carried out without any supervision replaced the traditional evaluation processes. However, their lack of validity as a result of the increased possibility of cheating among the students concerns educators about the results' authenticity. In such cases, providing additional processes to check the results is a necessary step to administer safer online tests. In this research, a process with high accuracy is proposed. This process is able to detect fraud based on data mining techniques. It consists of several steps, including the use of statistical methods, similarity criteria, student grades, K-nearest neighbor algorithms, artificial neural network, and support vector machine. In this process, scoring has been done for each stage of the operation, and the final statement to judge whether the cheating ever occurred or not will be based on the sum score of each stage. The process uses the results of the students' exam evaluation in order to identify abnormal scores in exams, and the recommender system is able to detect students' cheating with 99.98% accuracy. The results show that the proposed online test system has t