Taskin, Y, Hecking, T, Hoppe, HU et al. (2 more authors) (2019) Characterizing Comment Types and Levels of Engagement in Video-Based Learning as a Basis for Adaptive Nudging. In: Lecture Notes in Computer Science. EC-TEL 2019: 14th European Conference on Technology Enhanced Learning, 16-19 Sep 2019, Delft, Netherlands. Springer Verlag , pp. 362-376. ISBN 978-3-030-29735-0
Abstract
Video is frequently used as a learning medium in a variety of educational settings, including large online courses as well as informal learning scenarios. To foster learner engagement around instructional videos, our learning scenario facilitates interactive note taking and commenting similar to popular social video-sharing platforms. This approach has recently been enriched by introducing nudging mechanisms, which raises questions about ensuing learning effects. To better understand the nature of these effects, we take a closer look at the content of the comments. Our study is based on an ex post analysis of a larger data set from a recent study. As a first step of analysis, video comments are clustered based on a feature set that captures the temporal and semantic alignment of comments with the videos. Based on the ensuing typology of comments, learners are characterized through the types of comments that they have contributed. The results will allow for a better targeting of nudges to improve video-based learning.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-29736-7_27. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Learning analytics; Video-based learning; Learner engagement; Adaptive nudging |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 257831 EU - European Union 257831 |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Sep 2019 09:31 |
Last Modified: | 13 Sep 2019 09:31 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-29736-7_27 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150811 |