A Survey in the Use of Deep Learning Techniques in The Open Classroom Approach
Ines Boutabia, Abdelmadjid Benmachiche, Ali Abdelatif Betouil, Chaouki Chemam
Abstract
In the wake of the COVID-19 pandemic, the educational system found itself in dire need of a reliable tool to continue its activities. Enter the E-Learning system, a tool that had been overlooked or forgotten by many of its users. One of the most promising trends in E-learning is the open classrooms (OCR) approach, which aims to provide an all-encompassing online education that is easily accessible to everyone, thereby enabling numerous individuals to enhance their skills and capabilities. Over the years, there have been many attempts to integrate deep-learning techniques into the OCR approach to enhance performance and improve results. To gain a better understanding of this topic, our paper presents a comprehensive literature review of researchers who have used deep-learning approaches to improve the outcomes of e-learning in Open classrooms. Our primary focus is on individuals who employ neural network-based techniques, such as Artificial Neural Networks, Convolutional Neural Networks, Deep Neural Networks, Recurrent Neural Networks, and hybrid approaches. After conducting a thorough evaluation of the selected methods, we have been able to identify their respective strengths and weaknesses. By scrutinizing these approaches, we have successfully outlined the benefits and drawbacks of each, allowing us to provide a confident and informative analysis of this important subject.