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European Conference on Technology Enhanced Learning

EC-TEL 2017: Data Driven Approaches in Digital Education pp 504–508Cite as

Lesson Observation Data in Learning Analytics Datasets: Observata

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10474))

Abstract

Observational data can be used to illuminate different areas of teaching and learning process and enrich Learning Analytics data. Majority of lesson observation tools provide observational data that is not compliant with LA datasets. The paper presents Observata – a tablet computer application for context-aware semantic annotations of significant events during real time lesson observations. During the demo-session we expect the participants to engage in the discussion and provide feedback on the prototype.

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Notes

  1. 1.

    https://beta.leplanner.net/#/.

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Author information

Authors and Affiliations

  1. School of Digital Technologies, Tallinn University, Tallinn, Estonia

    Maka Eradze & Mart Laanpere

Authors
  1. Maka Eradze
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  2. Mart Laanpere
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Corresponding author

Correspondence to Maka Eradze .

Editor information

Editors and Affiliations

  1. Jean Moulin Lyon 3 University, Lyon, France

    Élise Lavoué

  2. German Institute for International Educational Research, Goethe University, Frankfurt, Germany

    Hendrik Drachsler

  3. KU Leuven, Leuven, Belgium

    Katrien Verbert

  4. University of Toulouse, Toulouse, France

    Julien Broisin

  5. Pontificia Universidad, Santiago de Chile, Chile

    Mar Pérez-Sanagustín

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© 2017 Springer International Publishing AG

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Eradze, M., Laanpere, M. (2017). Lesson Observation Data in Learning Analytics Datasets: Observata. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science(), vol 10474. Springer, Cham. https://doi.org/10.1007/978-3-319-66610-5_50

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  • DOI: https://doi.org/10.1007/978-3-319-66610-5_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66609-9

  • Online ISBN: 978-3-319-66610-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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