Analyzing the relationships between learning analytics, educational data mining and AI for education

Abstract : Baker and Siemens have well explained the theoretical differences and similarities between the educational data mining (EDM) and learning analyt-ics (LA) communities in their 2012 seminal paper, in which they also wished for bridging the gap between both communities. Moreover, since its creation as an independent conference in 2009, EDM has been evolving in parallel with the intelligent tutoring systems (ITS) / artificial intelligence for education (AIED) community. But what are the actual links that exist between these three communities in terms of members and research topics: to what extent do they overlap and work together? Are they getting closer from each other or drifting apart? Is each community specific to researchers with different backgrounds, modeling and analysis techniques? Those are some of the questions we investigate using a quantitative analysis led between 2007 and 2017 through: a social network analysis of the 3 communities, involving the 1822 scientists who participated in program committees and/or appeared as authors of the associated journals (IJAIED, JEDM and JLA); and a text analysis of abstracts of articles published in these journals. Results reveal the clear differences between these communities, their topics, practices and research methods.
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Hugues Labarthe, Vanda Luengo, François Bouchet. Analyzing the relationships between learning analytics, educational data mining and AI for education. 14th international conference on Intelligent Tutoring Systems (ITS):Workshop Learning Analytics, Jun 2018, Montreal, Canada. pp.10-19. ⟨hal-02015705⟩

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