AI BASED ONLINE EXAM PROCTORING SYSTEM

AI BASED ONLINE EXAM PROCTORING SYSTEM

Mr.S.Sankar Assistant Professor,Computer Science and Engineering , Dhirajlal Gandhi College of Technology

Ms.J.Sherly Student, Computer Science and Engineering , Dhirajlal Gandhi College of Technology

Ms.K.Nandhini Student, Computer Science and Engineering , Dhirajlal Gandhi College of Technology

Ms.M.Swetha Student, Computer Science and Engineering , Dhirajlal Gandhi College of Technology

Ms.P.Sivaranjani Student, Computer Science and Engineering , Dhirajlal Gandhi College of Technology

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I.ABSTRCT

A weapon for enhancing one's life and helping nations thrive is education. Moving towards e-learning and e-assessment is now necessary because to the extraordinary COVID-19 pandemic breakout and country lock-downs. An engaging method of testing distant pupils is through e-assessment. When deciding whether to take examinations online, it is important to make sure that the test accurately assesses the knowledge, abilities, and quantity of the candidate. Additionally, it is crucial to ensure the test is genuine legally, which means it is vital to stop the student from getting outside advice or information. The project's goal is to provide an online exam proctoring API that would allow students to take exams without being interrupted or receiving information or recommendations from outside sources. The project's goal is to create an API for online exam proctoring that would allow students to take tests without being distracted or obtaining unauthorized help. In this project, an end-to-end video-based deep learning method has been proposed to identify the degree of at-risk students' participation in electronic written exams. This method feeds sequential video frames into a Temporal Convolution Network (TCN) for action segmentation on videos to identify the degree of participation. A online application called Online Exam Proctoring API, based on the Model-View-Controller (MVC) architectural pattern, enables the management of electronic exams for specific courses. The system we created is broken down into many parts that analyse the video stream and ambient noise captured via the webcam, mouse, and keyboard actions. This system has undergone a thorough re-engineering to accommodate the difficult and complex task of conducting written exams securely from a distance, including concerns with authentication, anti-cheating techniques, and a description of their methodology to authenticate the exam done from a distance. By minimizing human labour, our technology will affect online learning even after pandemics and can be a fantastic addition to the present online proctor suite.

Key Words:  E-assessment, Online exam proctoring, Temporal convolution network, Electronic examination, templates, journals