Hecking, T, Dimitrova, V orcid.org/0000-0002-7001-0891, Mitrovic, A et al. (1 more author) (2017) Using Network-Text Analysis to Characterise Learner Engagement in Active Video Watching. In: Chen, W, Yang, J-C, Mohd Ayub, AF, Wong, SL and Mitrovic, A, (eds.) ICCE 2017 Main Conference Proceedings. 25th International Conference on Computers in Education, 04-08 Dec 2017, Christchurch, New Zealand. Asia-Pacific Society for Computers in Education , pp. 326-335. ISBN 978-986-94012-6-5
Abstract
Video is becoming more and more popular as a learning medium in a variety of educational settings, ranging from flipped classrooms to MOOCs to informal learning. The prevailing educational usage of videos is based on watching prepared videos, which calls for accompanying video usage with activities to promote constructive learning. In the Active Video Watching (AVW) approach, learner engagement during video watching is induced via interactive notetaking, similar to video commenting in social video-sharing platforms. This coincides with the JuxtaLearn practice, in which student-created videos were shared on a social networking platform and commented by other students. Drawing on the experience of both AVW and JuxtaLearn, we combine and refine analysis techniques to characterise learner engagement. The approach draws on network-text analysis of learner-generated comments as a basis. This allows for capturing pedagogically relevant aspects of divergence, convergence and (dis-) continuity in textual commenting behaviour related to different learner types. The lexical-semantic analytics approach using learner-generated artefacts provides deep insights into learner engagement. This has broader application in video-based learning environments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 Asia-Pacific Society for Computers in Education. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | video-based learning; learning analytics; network-text analysis |
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 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Jan 2018 13:28 |
Last Modified: | 04 Jan 2018 13:46 |
Published Version: | http://icce2017.canterbury.ac.nz/sites/default/fil... |
Status: | Published |
Publisher: | Asia-Pacific Society for Computers in Education |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125743 |