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Semi-automated Student Feedback and Theory-Driven Video-Analytics: An Exploratory Study on Educational Value of Videos

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Bridges and Mediation in Higher Distance Education (HELMeTO 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1344))

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Abstract

Learning Analytics (LA) is a relatively novel method for automated data collection and analysis with promising opportunities to improve teaching and learning processes, widely used in educational research and practice. Moreover, with the elevated use of videos in teaching and learning processes the importance of the analysis of video data increases. In turn, video analytics presents us with opportunities as well as challenges. However, to make full use of its potential often additional data is needed from multiple other sources. On the other hand, existing data also requires context and design-awareness for the analysis. Based on the existing landscape in LA, namely in video-analytics, this article presents a proof-of-concept study connecting cognitive theory-driven analysis of videos and semi-automated student feedback to enable further inclusion of interaction data and learning outcomes to inform video design but also to build teacher dashboards. This paper is an exploratory study analysing relationship between semi-automated student feedback (on several scales on the perceived educational value of videos), video engagement, video duration and theory-driven video annotations. Results did not indicate a significant relationship between different video designs and student feedback; however, findings show some correlation between the number of visualisations and video designs. The results can design implications as well as inform the researchers and practitioners in the field.

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Acknowledgements

The research has been made possible and funded under the European Union’s Erasmus + grant 2019-1-ES01-KA203-065558.

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Authors and Affiliations

  1. Department of Education and Human Sciences, University of Modena and Reggio Emilia, Viale Antonio Allegri, 9, 42121, Reggio Emilia, Italy

    Maka Eradze, Anna Dipace & Bojan Fazlagic

  2. University of Foggia, Via Antonio Gramsci, 89, 71122, Foggia, Italy

    Anastasia Di Pietro

Authors
  1. Maka Eradze
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  2. Anna Dipace
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  3. Bojan Fazlagic
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  4. Anastasia Di Pietro
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Corresponding author

Correspondence to Maka Eradze .

Editor information

Editors and Affiliations

  1. Giustino Fortunato University, Benevento, Italy

    Laura Sara Agrati

  2. Universidad Internacional de La Rioja, Logroño, La Rioja, Spain

    Daniel Burgos

  3. University of Pisa, Pisa, Italy

    Pietro Ducange

  4. University of Foggia, Foggia, Italy

    Pierpaolo Limone

  5. University of Bari Aldo Moro, Bari, Italy

    Loredana Perla

  6. eCampus University, Novedrate, Como, Italy

    Pietro Picerno

  7. eCampus University, Novedrate, Como, Italy

    Paolo Raviolo

  8. Open University in the Netherlands, Heerlen, The Netherlands

    Christian M. Stracke

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Eradze, M., Dipace, A., Fazlagic, B., Di Pietro, A. (2021). Semi-automated Student Feedback and Theory-Driven Video-Analytics: An Exploratory Study on Educational Value of Videos. In: Agrati, L.S., et al. Bridges and Mediation in Higher Distance Education. HELMeTO 2020. Communications in Computer and Information Science, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-67435-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-67435-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67434-2

  • Online ISBN: 978-3-030-67435-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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