Improving Online Engineering Education: Predicting Technological Awareness with Neural Net and Deep Learning Techniques

Abstract

Due to lockdowns during the COVID-19 outbreak, all educational institutions throughout the world have been shuttered, and students are unable to meet their instructors in person. Online education is the most effective method for retaining students and providing access to learning. However, it should be noted that the online education system is technologically reliant, making it difficult to administer and inaccessible to some students. Faculty and students, especially in developing countries, are both unprepared for online education, and they lack the necessary technological awareness and resources, such as internet availability, mobile or android devices, desktop/laptop computers, etc. So, this study involves the prediction of technological awareness among the faculty and students of engineering technologies when moving from a conventional education system to online education at the higher education level using Neural Net and Deep Learning techniques. The data collected through the survey consisted overall 2219 students and 257 faculty responses of seven engineering technologies. Accuracy, Kappa, and Correlation are the performance metrics for these techniques. The study results concluded that both the students and faculty of Telecommunication Engineering, students of Software Engineering, and Faculty of Computer Engineering are more aware of technology and ready for online education.