Piotrkowicz, A orcid.org/0000-0002-7723-699X, Dimitrova, V orcid.org/0000-0002-7001-0891, Mitrovic, A et al. (1 more author) (2018) Self-Regulation, Knowledge, Experience: Which User Characteristics Are Useful for Predicting Video Engagement? In: Proceedings UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. UMAP 2018, 08 Jul 2018, Nanyang Technological University, Singapore. Association for Computing Machinery , pp. 63-68. ISBN 978-1-4503-5784-5
Abstract
The use of videos in education has attracted considerable research attention. However, in order to gain the most benefits, learners need to actively engage with videos. It is an important, yet challenging, task to disentangle the relation between engagement with videos and learning, and at the same time to take into account relevant individual differences in order to offer personalised support. In this paper we investigate the question: `Can user characteristics relating to self-regulation, knowledge, and experience be leveraged for predicting user engagement with videos?'. Our results show that users' domain knowledge and self-regulation abilities can inform overall engagement prediction (inactive, passive and constructive learners), which makes them useful for adaptation and personalisation.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is an author produced version of a paper uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | user engagement; videos; active video watching; user modelling |
Dates: |
|
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 11:04 |
Last Modified: | 12 Jul 2018 05:32 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/3213586.3226196 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132615 |