Constructing Differentiated Educational Materials Using Semantic Annotation for Sustainable Education in IoT Environments

Abstract

Recently, Internet of Things (IoT) technology has become a hot trend and is used in a wide variety of fields. For instance, in education, this technology contributes to improving learning efficiency in the class by enabling learners to interact with physical devices and providing appropriate learning content based on this interaction. Such interaction data can be collected through the physical devices to define personal data. In the meanwhile, multimedia contents in this environment usually have a wide variety of formats and standards, making it difficult for computers to understand their meaning and reuse them. This could be a serious obstacle to the effective use or sustainable management of educational contents in IoT-based educational systems. In order to solve this problem, in this paper, we propose a semantic annotation scheme for sustainable computing in the IoT environment. More specifically, we first show how to collect appropriate multimedia contents and interaction data. Next, we calculate the readability of learning materials and define the user readability level to provide appropriate contents to the learners. Finally, we describe our semantic annotation scheme and show how to annotate collected data using our scheme. We implement a prototype system and show that our scheme can achieve efficient management of various learning materials in the IoT-based educational system.