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Automated Analysis of Lecture Video Engagement Using Student Posts

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Artificial Intelligence in Education (AIED 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10331))

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Abstract

This work explores the feasibility of a learning analytic that would provide high level engagement data to instructors based on students’ text artifacts in online learning systems. Student posts from an online lecture video system were collected and manually coded by engagement using the ICAP framework. Analyses show what features are most indicative of engagement and the performance of using a neural network to classify posts by engagement.

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Acknowledgements

This work is funded by National Science Foundation grant IIS-1318345. Any opinions, findings, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

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Authors and Affiliations

  1. University of Nebraska at Omaha, Omaha, USA

    Nicholas R. Stepanek & Brian Dorn

Authors
  1. Nicholas R. Stepanek
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  2. Brian Dorn
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Corresponding author

Correspondence to Nicholas R. Stepanek .

Editor information

Editors and Affiliations

  1. Human-Centered Multimedia, University of Augsburg, Augsburg, Germany

    Elisabeth André

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ryan Baker

  3. Department of Psychology, University of Memphis, Memphis, Tennessee, USA

    Xiangen Hu

  4. Ateneo de Manila University, Quezon City, Philippines

    Ma. Mercedes T. Rodrigo

  5. University of Sussex, Brighton, United Kingdom

    Benedict du Boulay

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© 2017 Springer International Publishing AG

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Stepanek, N.R., Dorn, B. (2017). Automated Analysis of Lecture Video Engagement Using Student Posts. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_65

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  • DOI: https://doi.org/10.1007/978-3-319-61425-0_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61424-3

  • Online ISBN: 978-3-319-61425-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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