Real-time lecture's micro-quality assessment for diabetic students based on IoTs and real-time cloud computing technologies

Abstract

Students with diabetes mellitus require more academic attention, intensive flow up, and a moderate temperature environment during classes. In this paper, the learning process for those students is improved by using the internet of things (IoTs) and cloud computing technologies. The proposed system improves lecture quality using lecture micro quality which is based on lecture time segmentation. Therefore, the designed system provides each student with a microcontroller-based node to collect information about his understanding status in a real-time fashion. Based on this information, the system provides the lecturer with real-time statistical calculations about vital parameters such as the understanding status of each student, the level of the classroom's understanding ratio, and the total number of students who did not understand the topic which is being explained. The designed system is practically tested in a real classroom environment. The implemented system is practically tested in a real classroom environment and the obtained results showed up an improvement in lecture quality due to the real-time feedback which informed the lecturer to adapt the learning technique.