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European Conference on Technology Enhanced Learning

EC-TEL 2012: 21st Century Learning for 21st Century Skills pp 140–152Cite as

Tweets Reveal More Than You Know: A Learning Style Analysis on Twitter

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Part of the Lecture Notes in Computer Science book series (LNPSE,volume 7563)

Abstract

Adaptation and personalization of e-learning and technology-enhanced learning (TEL) systems in general, have become a tremendous key factor for the learning success with such systems. In order to provide adaptation, the system needs to have access to relevant data about the learner. This paper describes a preliminary study with the goal to infer a learner’s learning style from her Twitter stream. We selected the Felder-Silverman Learning Style Model (FSLSM) due to its validity and widespread use and collected ground truth data from 51 study participants based on self-reports on the Index of Learning Style questionnaire and tweets posted on Twitter. We extracted 29 features from each subject’s Twitter stream and used them to classify each subject as belonging to one of the two poles for each of the four dimensions of the FSLSM. We found a more than by chance agreement only for a single dimension: active/reflective. Further implications and an outlook are presented.

Keywords

  • Learning Style
  • Learning Preference
  • Twitter User
  • Chance Agreement
  • Twitter Stream

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-33263-0_12
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Author information

Authors and Affiliations

  1. Delft University of Technology, The Netherlands

    Claudia Hauff & Geert-Jan Houben

  2. Knowledge Management Institute, Graz University of Technology, Austria

    Marcel Berthold, Christina M. Steiner & Dietrich Albert

Authors
  1. Claudia Hauff
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  2. Marcel Berthold
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  3. Geert-Jan Houben
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  4. Christina M. Steiner
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  5. Dietrich Albert
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Editor information

Editors and Affiliations

  1. CASS School of Education and Communities, University of East London (UEL), Stratford Campus, Water Lane, E15 4LZ, London, UK

    Andrew Ravenscroft

  2. Knowledge Management Institute and Know-Center GmbH, Graz University of Technology (TUG), Inffeldgasse 21a,, 8010, Graz, Austria

    Stefanie Lindstaedt

  3. Departamento Ingenieria Telemática, Universidad Carlos III de Madrid, Avenida Universidad 30,, 28911, Leganés (Madrid), Spain

    Carlos Delgado Kloos

  4. Information and Communitations Technologies Department, Universitat Pompeu Fabra,, Roc Boronat 138, 08018, Barcelona, Spain

    Davinia Hernández-Leo

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© 2012 Springer-Verlag Berlin Heidelberg

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Hauff, C., Berthold, M., Houben, GJ., Steiner, C.M., Albert, D. (2012). Tweets Reveal More Than You Know: A Learning Style Analysis on Twitter. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds) 21st Century Learning for 21st Century Skills. EC-TEL 2012. Lecture Notes in Computer Science, vol 7563. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33263-0_12

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  • DOI: https://doi.org/10.1007/978-3-642-33263-0_12

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