Learning 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 specific 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.
In REF , an approach was presented using additional information from the LOD cloud to interpret the results of a sequential pattern mining process with an example case of data about students' enrollment in course modules.
Interpreting data mining results with linked data for learning analytics: motivation, case study and directions
LAK '13
Computer Science