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A Dynamic Topic Model of Learning Analytics Research

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Presented at LAK Data Challenge, Linked Data Tutorial, LAK 2013 Conference

Presented at LAK Data Challenge, Linked Data Tutorial, LAK 2013 Conference
April 9, Leuven, Belgium

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    A Dynamic Topic Model of Learning Analytics Research A Dynamic Topic Model of Learning Analytics Research Presentation Transcript

    • Layers LAK Data Challange 2013 April 9, 2013 Leuven, Belgium A Dynamic Topic Model of Learning Analytics Research Past – Present – Future Michael Derntl*, Nikou Günnemann and Ralf Klamma RWTH Aachen University Advanced Community Information Systems (ACIS) Informatik 5, Aachen, Germany * derntl@dbis.rwth-aachen.deLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 1 These slides are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
    • Methodology: Dynamic Topic Layers Modeling Paper 1 text blah Relational DB blah blah blah RDF XML to RDF XML Relational DB Probabilistic Dump (ID, venue, year, … topic mining (LAK + EDM) title, authors, (LDA) abstract, fulltext, Paper N text blah hyperlink) blah blah blah Dynamic topic modeling of LAK data (20 topics, 5 epochs)Lehrstuhl Informatik 5 Figure © ACM. Source: D. M. Blei: Probabilistic topic models. Commun. ACM 55(4): 77-84 (2012)(Information Systems) Prof. Dr. M. Jarke 2
    • Layers Main Topics in LAK Dataset (bubble size: relevance in 2012)  "students model parameters skill" (A): most relevant and most volatile  "model data features prediction" (B) – high relevance and high stability  the LAK topic? – in 2012 "predicition" most relevant to this topic LAK 2011  "network community discussion analysis" (R): – quite irrelevant and volatile – rise in relevance through LAK 2011Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke  General prominence of students, learners, model, data in multiple topics 3
    • Layers Impact of LAK Conference Overall topic "river":  Turbulence in EDM era  More shifts through LAK 2011  Catharsis in 2012? EDM 08 EDM 09 EDM 10 EDM 11 EDM 12 LAK 11 LAK 12 ET&S SI Zooming into 2010-11 transition:  Three topics reach absolute peak (black up triangles) – model students data probability – network community discussion analysis – problem students model typesLehrstuhl Informatik 5  Increased focus on network,(Information Systems) Prof. Dr. M. Jarke EDM 10 EDM 11 LAK 11 community, student models 4
    • Layers Most Recent Topic Trends Highest absolute rise in relevance in 2012: 1. students data courses system 2. students interaction participants analysis 3. learning analytics social learners 4. students actions learning state 5. data user learning datasetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke From 11% cumulative relevance in 2009 to 42% in 2012 5
    • Layers Summary Past Turbulent topic currents in EDM-only era LAK and EDM have a shared topic foundation (data modeling, classification, clustering, …) Present Emphasis on learner modeling, data modeling, analysis, prediction. 1st LAK conference brought topic shifts (e.g. networks, community) Future Topic shifts 2012 moderate; convergence of LAK research topics Social and interaction aspects; students as research subjectsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 6
    • Do It Yourself: Layers Topic Analytics with D-VITALehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 7 http://is.gd/laktopics