Abolkasim, E, Lau, LMS orcid.org/0000-0003-1062-9059 and Dimitrova, V (2016) A semantic-driven model for ranking digital learning objects based on diversity in the user comments. In: Adaptive and Adaptable Learning (Lecture Notes in Computer Science). 11th European Conference on Technology Enhanced Learning, 13-16 Sep 2016, Lyon, France. Springer Verlag , pp. 3-15.
Abstract
This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing Switzerland 2016. This is an author produced version of a proceedings paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-45153-4_1. |
Keywords: | Diversity model for learning; Semantics; User comments analytics; Video rating |
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) > Artificial Intelligence & Biological Systems (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jul 2016 11:12 |
Last Modified: | 04 Nov 2016 17:17 |
Published Version: | https://dx.doi.org/10.1007/978-3-319-45153-4_1 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-319-45153-4_1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102039 |