Semantic Enrichment of Twitter News for Differentiated STEAM Education

Recently, STEAM education is attracting much attention as a new educational method. In the STEAM education lesson, the latest science/technology materials are usually used to arouse or increase the learner's interest. Twitter is one of the most effective mediums for learners to easily access such materials. A large amount of news is generated every second on Twitter and disseminated quickly to the public. However, such news is not appropriate for learning because it does not take into account various levels of learners or relevance to the class subjects. In this paper, to solve these problems, we propose a semantic enrichment scheme for the latest science/technology Twitter news for differentiated STEAM education lessons. Our proposed scheme enables the learners to browse news of desired topics and their relevant materials, even filtered by the user level.