A Block chain-Based Privacy-Preserving Framework for Detecting Cheating in Online Examinations without Video Surveillance

Abstract

With the increasing number of online exams, it has become very important to ensure that the students do not indulge in any dishonest practices, also taking care of the cases in which a privacy, related or technical issue would make the use of a webcam, based proctoring not advisable. This work presents a plagiarism detection solution as an innovative way of online testing, focusing on privacy and without the necessity of watching video or audio recordings. The method mostly depends on less intrusive monitoring of the user's browser activity as well as a server, side analysis of the suspicious behavior. A few basic anti, cheating steps, which are at least partially realizable through the techniques mentioned above, include the turning off of the options for a right, click, copying, and pasting as well as highly controlled tab switching. Furthermore, the platform is equipped with the functionalities for recognizing the user's disengagement from the test and tracking the occurrence of the odd patterns for the given answers. By adding a block chain layer, the overall system becomes more dependable and secure. This blockchain layer employs theSHA-256 hashing algorithm, thereby creating unalterable records of every student's activity along with their scores.