• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.653-660
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• 2020

The paper examines the impact of proctoring environments on student performance in two different exam proctoring environments: online versus offline proctored exams. This study employs a set of aggregated data from 1,762 students over the eight-year period from 2009 to 2016 in a university. Although there were nine courses offered, they could have been counted more than once as students may appear several times to take exams for different courses. This study employs independent samples t-test and regression analysis to compare the means of two independent groups and to test the hypothesis. The results of the independent samples t-test and the regression analysis indicate that there is no difference in the mean scores of exams and, therefore, the findings suggest that the exam proctoring environment is unlikely related to student performance even when students take their exams either in online proctoring or offline proctoring environments. This study concludes that the proctoring environment unlikely results in a statistically significant difference of exam scores and, thus, the exam proctoring environment does not appear to cause any change in student performance. The findings suggest that the exam proctoring environments does not appear to impact on student academic achievements and assessments.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.123-130
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• 2021

A novel approach for the detection of cheating during e-Exams is presented here using convolutional neural networks (CNN) based systems. This system will help the proctors to identify any kind of uncertain event at the time of online exams, for which most of the government's across the globe are recommending due to the Covid-19 pandemic. Most of the institutions and students across the globe are badly affected by their academic programs and it is a challenging task for universities to conduct examinations using the traditional methods. Therefore, the students are attending most of their classes using different types of third party applications that are available online. However, to conduct online exams the universities cannot rely on these service providers for a long time. Therefore, in this work, a complete setup of the software tools is provided for the students, which can be used by students at their respective laptops/personal computers with strict guidelines from the university. The proposed approach helps most of the universities in Saudi Arabia to maintain their database of different events/activities of students at the time of E-Exams. This method proved to be more accurate and CNN based detection proved to be more sensitive with an accuracy of 97% to detect any kind of uncertain activity of the students at the time of e-Exam.