Mathur, A;
Lane, ND;
Kawsar, F;
(2016)
Engagement-aware computing: Modelling user engagement from mobile contexts.
In:
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
(pp. pp. 622-633).
ACM: Heidelberg, Germany.
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Abstract
In this paper, we examine the potential of using mobile context to model user engagement. Taking an experimental approach, we systematically explore the dynamics of user engagement with a smartphone through three different studies. Specifically, to understand the feasibility of detecting user engagement from mobile context, we first assess an EEG artifact with 10 users and observe a strong correlation between automatically detected engagement scores and user's subjective perception of engagement. Grounded on this result, we model a set of application level features derived from smartphone usage of 10 users to detect engagement of a usage session using a Random Forest classifier. Finally, we apply this model to train a variety of contextual factors acquired from smartphone usage logs of 130 users to predict user engagement using an SVM classifier with a F1-Score of 0.82. Our experimental results highlight the potential of mobile contexts in designing engagement-aware applications and provide guidance to future explorations.
| Type: | Proceedings paper |
|---|---|
| Title: | Engagement-aware computing: Modelling user engagement from mobile contexts |
| Event: | 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) |
| ISBN-13: | 9781450344616 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/2971648.2971760 |
| Publisher version: | https://doi.org/10.1145/2971648.2971760 |
| Language: | English |
| Additional information: | © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing https://doi.org/10.1145/2971648.2971760 |
| Keywords: | Mobile Sensing; Engagement; Behaviour Modelling; EEG |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1535344 |
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