IoT-based personalized NIE content recommendation system

Abstract

Recently, the Internet of Things (IoT) has become a popular topic and a dominant trend in various fields, such as healthcare, agriculture, manufacturing, and transportation. In particular, in the field of education, it has become a popular tool to improve learners' interests and achievements by making them interact with various devices in and out of the classroom. Lessons in newspaper in education (NIE), which uses newspapers as an educational resource, have started to utilize it. For instance, by analyzing the data generated from a learner's device, such as Raspberry Pi, appropriate news and related multimedia data can be provided to the learners as learning materials to support the lesson. However, as news and multimedia data are scattered in a wide variety of forms, it is very difficult to select appropriate ones for the learner. In this paper, we propose a news and related multimedia recommendation scheme based on IoT for supporting NIE lessons. Specifically, news and related multimedia data are collected from the Web, and they are integrated and stored into the server. After that, the learner can easily browse such contents using a mobile device through personalized visualization, which increase the efficiency of NIE lessons. To show the effectiveness of our scheme, we implemented a prototype system and performed various experiments. We present some of the results.