Abolkasim, E, Lau, L orcid.org/0000-0003-1062-9059, Dimitrova, V et al. (1 more author) (2018) Diversity Profiling of Learners to Understand Their Domain Coverage While Watching Videos. In: Lifelong Technology-Enhanced Learning. EC-TEL 2018: 13th European Conference on Technology Enhanced Learning, 03-06 Sep 2018, Leeds, UK. Lecture Notes in Computer Science, 11082 . Springer , pp. 561-565. ISBN 978-3-319-98571-8
Abstract
Modelling diversity is especially valuable in soft skills learning, where contextual awareness and understanding of different perspectives are crucial. This paper presents an application of a diversity analytics pipeline to generate domain diversity profiles for learners as captured in their comments while watching videos for learning a soft skill. The datasets for analysis were collected from a series of studies on learning presentation skills with Active Video Watching system (AVW-Space). Two user studies (with 37 postgraduates and 140 undergraduates, respectively) were compared. The learners’ diversity and personal profiles are used to further understand and highlight any notable patterns about their domain coverage on presentation skills.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018, Springer Nature Switzerland AG . This is a post-peer-review, pre-copyedit version of an article published in Lifelong Technology-Enhanced Learning. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-98572-5_45. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Diversity profiling; Domain coverage; Diversity analytics pipeline; Video-based learning; Presentation skills |
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: | 02 Aug 2018 12:31 |
Last Modified: | 14 Aug 2019 00:43 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-319-98572-5_45 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134064 |