Self-Regulation, Knowledge, Experience: Which User Characteristics Are Useful for Predicting Video Engagement?

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.