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

A mixed-method triangular approach to best practices in combating plagiarism and impersonation in online bachelor’s degree programs

This study examines the phenomenon of plagiarism and impersonation in online course assignments. Technological advancements, coupled with lower costs and accessibility, have made online courses and programs a practical option for higher education students. Unfortunately, the increasing online enrollment and advancing technology have allowed an increase in the opportunity for students to commit the act of plagiarism and impersonation in online course assignments, thus potentially compromising the academic integrity of online degree programs. This study examines the various practices and approaches of plagiarism and impersonation made available to students. Utilizing the systemic review of literature, the researcher compiles a list of 20 best practices in combating plagiarism and impersonation in online course assignments. A Delphi method approach is employed, utilizing the expertise of professors who teach in fully online bachelor’s degree programs. The 20 best practices established through the literature review will be narrowed down to ten best practices via an ordinal ranking questionnaire using a two-round format. The questionnaire distribution occurs via e-mails. Researching professors that teach in fully online bachelor’s degree programs is how the researcher will obtain the e-mails. The first-round e-mail consists of the consent form and the original set of 20 best practices. In addition, a link to the Qualtrics ranking survey will be included in the e-mail. The second-round e-mail consists of the updated 15 best practices ranked from the initial e-mail and a link to the ranking survey. After completing the second round, the establishment of the ten best practices for reducing plagiarism and impersonation in online assignments will emerge. To further validate the 10 best practices, the researcher interviews 10 professors that participated in the original Delphi study. The original consent form includes a link for the participants to access if they select to participate in the interview. After verifying the professors’ intent to participate, a consent form will be obtained. The interviews will be conducted and recorded virtually through zoom. The recordings will be deleted once they are transcribed. This study potentially benefits all online degree programs by establishing the ten best practices for reducing plagiarism and impersonation in online assignments

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment

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