A Systematic Review of Deep Learning Based Online Exam Proctoring Systems for Abnormal Student Behaviour Detection
Year: 2022
Type: review
Study design: Systematic Review
Abstract: In the last years, educational technology has advanced tremendously. Increasing numbers of schools and universities are embracing online learning to serve their students better. As a result of the COVID-19 epidemic, students now have more flexibility in their study schedules and may work at their speed to better themselves. AI-based proctoring solutions have also grabbed the industry by storm. Online proctoring systems (OPS) generally employ online technologies to ensure that the examination is conducted in a secure environment. A survey of current proctoring systems based on artificial intelligence, machine learning, and deep learning is presented in this work. There were 41 publications listed from 2016 to 2022 after a comprehensive search on Web of Science, Scopus, and IEEE archives. We focused on three key study questions: current approaches for AI-based proctoring systems, techniques/algorithms to be employed, datasets used, and cheating detection methods suggested in such systems. Analysis of AI-based proctoring systems demonstrates a lack of training in using technologies, methodologies, and more. To our knowledge, Machine Learning or Deep Learning-based proctoring systems have not been subjected to such a study. From a technology standpoint, our research focuses on detecting cheating in AI-based proctoring systems. New recently launched technologies are included in this review, where these technologies potentially substantially influence online education and the online proctoring system.
Authors: Muhanad Abdul Elah Abbas, Saad Hameed
Institution: Al Mansour University College
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4295110160