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Interpreting data mining results with linked data for learning analytics: motivation, case study and direction

d'Aquin, Mathieu and Jay, Nicolas (2013). Interpreting data mining results with linked data for learning analytics: motivation, case study and direction. In: Third Conference on Learning Analytics and Knowledge (LAK 2013), 8-12 Apr 2013, Leuven, Belgium.

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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 ACM
Extra Information: ISBN: 978-1-4503-1785-6
Keywords: learning analytics; data mining; interpretation; student enrolment; sequence pattern mining; formal concept analysis
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 36660
Depositing User: Mathieu d'Aquin
Date Deposited: 19 Feb 2013 09:37
Last Modified: 08 Dec 2018 15:28
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