ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Extracting relevant questions to an RDF dataset using formal concept analysis
Proceedings of the sixth international conference on Knowledge capture
Watson, more than a Semantic Web search engine
Semantic Web
Using concept lattices for text retrieval and mining
Formal Concept Analysis
Learning analytics and educational data mining: towards communication and collaboration
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Assessing the educational linked data landscape
Proceedings of the 5th Annual ACM Web Science Conference
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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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.