Publication: Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application
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Date
2017
Authors
Cheng L.K.
Selamat A.
Mohamed Zabil M.H.
Selamat M.H.
Alias R.A.
Puteh F.
Mohamed F.
Krejcar O.
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Press
Abstract
This paper highlights the current literatures in usability studies, performance metrics and machine learning algorithm. A literature review is done in these three areas of studies to find a research gap that can be explored further. The paper will then propose a research methodology to attend to the issues of machine learning and usability. An experiment is proposed to compare the efficiency results in between data consistency, correlation between performance metrics and selfreported metrics of a Mobile Augmented Reality learning application. The methodology proposes hierarchical agglomerative clustering technique as a solution in differentiating usability issues according to priority in order to help with usability re-engineering decisions. This paper proposes two objectives through the proposed framework and present evidence on how to achieve them. Lastly, this paper will discuss the results, conclusion and future works of the proposed study. � 2017 The authors and IOS Press. All rights reserved.
Description
Artificial intelligence; Augmented reality; Cluster analysis; Computer aided instruction; Learning systems; Usability engineering; Agglomerative clustering; English language teaching; Mobile augmented reality; Unsupervised machine learning; Usability; Learning algorithms