Dimitrova, V orcid.org/0000-0002-7001-0891, Mitrovic, A, Piotrkowicz, A orcid.org/0000-0002-7723-699X et al. (2 more authors) (2017) Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching. In: UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP2017 - 25th Conference on User Modeling, Adaptation and Personalization, 09-12 Jul 2017, Bratislava, Slovakia. Association for Computing Machinery (ACM) , New York, NY, USA , pp. 22-31. ISBN 978-1-4503-4635-1
Abstract
Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Copyright is held by the owner/author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
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: | 03 May 2017 14:09 |
Last Modified: | 10 Sep 2019 14:42 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3079628.3079683 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:115918 |