Camproctor Web App for Electronic Written Exams Proctoring System Using Temporal Convolutional Networks (Tcns)

Education is a weapon to improve one's life and develop countries.Nowadays, as a result of the unprecedented COVID-19 pandemic outbreak and countries' lock-downs, shifting to e-learning and e-assessment become inevitable.E-assessment is an interactive way of testing remote students.The central aspect that deserves serious consideration when one decides to take exams remotely is to ensure that the test measures the actual skills, quality and quantity of a student's knowledge.Furthermore, it is also essential to ensure that the test has legal validity, i.e. it is necessary to prevent the student from receiving suggestions or information from the outside.The aim of the project is to develop a CamProctor API for the student to take the test without being distracted or receiving illicit assistance using AI and Camera Vision Technologies.In this project an end-to-end video-based deep learning methods have been proposed to detect the level of at-risk students' engagement in electronic written exams, in which sequential frames of videos are fed to Temporal Convolution Network (TCN) for video-based action segmentation in order to detect the engagement level.CamProctor API is a web application based on the Model-View-Controller (MVC) architectural pattern that allows the management of electronic examinations for individual courses.The technology developed by us is divided into various components that analyse the ambient audio, the video stream captured by the webcam, the mouse movements and the keyboard.This system has been completely re-engineered to afford the crucial and delicate task of carrying out written exams in a secure way from remote, such as authentication issues, anti-cheating methods and described their approach to validate the exam conducted from a remote location.Our system can serve as a great complement to the current online proctor suite and will influence online learning even after pandemics by reducing human toil.