Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21228
Title: Using olfactory media cues in e-learning – perspectives from an empirical investigation
Authors: Alkasasbeh, AA
Ghinea, G
Keywords: olfactory media;olfactory cues;traditional digital media;learner performance;quality of experience
Issue Date: 19-Mar-2020
Publisher: Springer
Citation: Alkasasbeh, A.A. and Ghinea, G. (2020) 'Using olfactory media cues in e-learning – perspectives from an empirical investigation', Multimed Tools Appl, 79, 19265-19287 . https://doi.org/10.1007/s11042-020-08763-3
Abstract: People interact with computers using their senses. Currently, in a digital context, traditional digital media like videos and images used to convey information to users, and these media can be used as a source of information. However, relatively few studies have been conducted on olfactory media as a source of information in a digital context. In this paper, we report on a study that examined the possibility of using olfactory media as a source of information and whether its usage as informational cues enhances learning performance and user Quality of Experience (QoE). To this end, an olfactory-enhanced quiz (web-based) was developed about four countries. The quiz contained different types of questions employing four types of digital media in their contents: text, image, audio and olfactory media. Four scents were used that were considered to be related to the respective countries. Sixty-four participants were invited to our experiment to evaluate this application. Our results revealed that usage of olfactory media synchronised with traditional digital media had a significant impact on learner performance compared to the case when no olfactory media was employed. In respect of user QoE, it was found that olfactory media influenced users positively; moreover, they were passionate about engaging with enhanced olfactory applications in the future.
URI: https://bura.brunel.ac.uk/handle/2438/21228
DOI: https://doi.org/10.1007/s11042-020-08763-3
ISSN: 1380-7501
Appears in Collections:Dept of Computer Science Research Papers

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