An Intelligent Learning Support System

Abstract

Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere else, for example, in massive open online courses. A person, who is learning some particular content from outside of the university, could have some knowledge gaps which should be recognized. We argue that it is possible to respond to these challenges with adaptive, intelligent, and personalized learning systems that utilize data analytics, machine learning, and Semantic Web technologies. In this paper, we propose a model for an Intelligent Learning Support System that guides learner during the whole lifecycle using semantic annotation methodology. Semantic annotation of learning materials is done not only on the course level but also at the content level to perform semantic reasoning about the possible learning gaps. Based on this reasoning, the system can recommend extensive learning material.