CHEATING DETECTION IN ONLINE EXAMS BASED ON CAPTURED VIDEO USING DEEP LEARNING

Today, e-learning has become a reality and a global trend imposed and accelerated by the COVID-19 pandemic. However, there are many risks and challenges related to the credibility of online exams which are of widespread concern to educational institutions around the world. Online exam system continues to gain popularity, particularly during the pandemic, due to the rapid expansion of digitalization and globalization. To protect the integrity of the examination and provide objective and fair results, cheating detection and prevention in examination systems is a must. Therefore, the main objective of this thesis is to develop an effective way of detection of cheating in online exams. In this work, a system to track and prevent attempts to cheat on online exams is developed using artificial intelligence techniques. The suggested solution uses the webcam that is already connected to the computer to record videos of the examinee in real time and afterwards analyze them using different deep learning methods to find best combinations of models for face detection and classification if cheating/not cheating occurred. To evaluate the system, we use a benchmark dataset of exam videos from 24 participants who represented examinees in online exam. An object detection technique is used to detect face appeared in the image and crop the face portion, and then a deep learning based classification model is trained from the images to classify a face as cheating or not cheating. We have proposed an effective combination of data preprocessing, object detection, and classification models to obtain high detection accuracy. We believe that the suggested invigilation methodology can be used in colleges, institutions, and schools to look for and keep an eye on suspicious student behavior. Hopefully, by putting the proposed invigilation method into place, we can aid in eliminating and reducing cheating incidences as it undermines the integrity and fairness of the educational system

COVID-2019 Impacts on Education Systems and Future of Higher Education

The rapid outbreak of the COVID-19 has presented unprecedented challenges on education systems. Closing schools and universities and cancelling face-to-face activities have become a COVID-19 inevitable reality in most parts of the world. To be business-as-usual, many higher education providers have taken steps toward digital transformation, and implementing a range of remote teaching, learning and assessment approaches. This book provides timely research on COVID-19 impacts on education systems and seeks to bring together scholars, educators, policymakers and practitioners to collectively and critically identify, investigate and share best practices that lead to rethinking and reframing the way we deliver education in future

Artificial Intelligence and Education. Guidance for Policy-makers

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts