ABSTRACT
Higher educational systems are accountable for ensuring effective E-learning (EL) environments for online learners. An effective learning environment engages learners in educational activities. The chapter has three parts; in the first part, we discussed the convolutional neural network (CNN). CNN has many models, but for the purpose of this study, we have applied three models and found them to be most appropriate to measure students’ engagement (SEt) in EL assignments. We have applied all convolutional networks (All-CNN), network-in-network (NiN-CNN), and very deep convolutional network (VD-CNN) because they have simple network architectures and show efficiency in conditions and categories. These categories are based on the conditions of learners for their facial expressions in an online environment. In the second part of the chapter, we cover the methods of application and benefits of machine learning (ML) and artificial intelligence (AI) in King Khalid University’s (KKU) E-learning. The third part of the chapter covers the role of Internet of Things (IoT) in the education sector and defines the advantages, types of security concerns, and challenges of deployment of IoT. This research is descriptive in nature; results for the application of three models of CNN are referred for their advantages and challenges for online learners, and results for ML and AI are based on qualitative analysis done 262through tools and techniques of EL applied in KKU’s learning management services (LMS) and blackboard (BB). Results for IoT show the benefits for both students and educators.
