Interpreting data mining results with linked data for learning analytics

Abstract

International audienceLearning Analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a speci c data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students' enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process