Emotional Preferences Towards E-Learning Based on Analytic Hierarchy Process and Kansei for Decision Making


  •   Ana Hadiana


Users in software world play key role in determining whether a software, including e-Learning system, has a long time of use or not. Past literatures have highlighted the importance of incorporating emotional requirement into software systems. It is important to consider what users need related to software. One of the critical component of a software systems that directly interacts with users is the interface. Users interface design (UID) could induce critical emotional experience and impression to users the first time they execute a software system. Kansei Engineering is adopted as a methodology to analyze users emotional experience towards the software UID. This research implemented a combination approach of Kansei Engineering and Analytic Hierarchy Process in order to analyze students’ emotional experience as users of e-Learning in higher learning institution, and then determines which of an open source e-Learning system that suits their positive emotional experience. This paper reports an attempt to discover the relationship between UID and users’ emotional experience in e-Learning systems. The research found that there were two critical students’ emotional factors, which are “clear” and “pleasant”. These two factors had a big impact in the selection of an e-Learning system, with factor of clear has larger impact. The research result then suggests the preferred e-Learning system for students based on those that evoked positive emotional experience to students. The result will benefit higher learning institution in promoting e-Learning, to extend the outreach potential of e-Learning among students.

Keywords: Kansei Engineering, Emotional experience, AHP, UID


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How to Cite
Hadiana, A. 2020. Emotional Preferences Towards E-Learning Based on Analytic Hierarchy Process and Kansei for Decision Making. European Journal of Engineering Research and Science. 5, 10 (Oct. 2020), 1186-1190. DOI:https://doi.org/10.24018/ejers.2020.5.10.2127.