Mohammed, A (2022) Generating Narratives of Video Segments to Support Learning. In: Lecture Notes in Computer Science. AIED 2022: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 27-31 Jul 2022, Durham, UK. Springer Nature , pp. 22-28.
Abstract
The predominance of using videos for learning has become a phenomenon for generations to come. This leads to a prevalence of videos generating and using open learning platforms (Youtube, MOOC, Khan Academy, etc.). However, learners may not be able to detect the main points in the video and relate them to the domain for study. This can hinder the effectiveness of using videos for learning. To address these challenges, we are aiming to develop automatic ways to generate video narratives to support learning. We presume that the domain for which we are processing the videos has been computationally presented (via ontology). We are proposing a generic framework for segmenting, characterising and aggregating video segments VISC-L which offers the foundation to generate the narratives. The narrative framework designing is in progress which is underpinned with Ausubel’s Subsumption theory. All the work is being implemented in two different domains and evaluated with people to test their awareness of the domains-aspects.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper published in the Lecture Notes in Computer Science book series. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Domain ontology; Learning videos; Video aggregation; Video characterisation; Video narratives; Video segmentation |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jan 2023 13:25 |
Last Modified: | 14 Sep 2023 14:40 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1007/978-3-031-11647-6_4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195707 |