Abolkasim, E, Lau, L orcid.org/0000-0003-1062-9059, Mitrovic, A et al. (1 more author) (2018) Ontology-based Domain Diversity Profiling of User Comments. In: Lecture Notes in Computer Science 10948. 19th International Conference, AIED 2018 – International Conference on Artificial Intelligence in Education, 27-30 Jun 2018, London. Springer Nature , pp. 3-8. ISBN 9783319938455
Abstract
Diversity has been the subject of study in various disciplines from biology to social science and computing. Respecting and utilising the diversity of the population is increasingly important to broadening knowledge. This paper describes a pipeline for diversity profiling of a pool of text in order to understand its coverage of an underpinning domain. The application is illustrated by using a domain ontology on presentation skills in a case study with 38 postgraduates who made comments while learning pitch presentations with the Active Video Watching system (AVW-Space). The outcome shows different patterns of coverage on the domain by the comments in each of the eight videos.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG, part of Springer Nature 2018. This is an author produced version of a paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final authenticated publication is available online at https://doi.org/10.1007/978-3-319-93846-2_1 |
Keywords: | diversity analytics, ontology, semantic techniques, video-based learning, active video watching, soft skills learning |
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: | 27 Jun 2018 12:20 |
Last Modified: | 25 Jul 2018 14:55 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1007/978-3-319-93846-2_1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132345 |