Publication: Feasibility comparison of HAC algorithm on usability performance and self-reported metric features for MAR learning
Date
2018
Authors
Cheng L.K.
Selamat A.
Zabil M.H.M.
Selamat M.H.
Alias R.A.
Puteh F.
Mohamed F.
Krejcar O.
Herrera-Viedma E.
Fujita H.
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Press
Abstract
This paper highlights the current literatures in usability studies, performance metrics, self-reported metrics and hierarchical agglomerative clustering algorithms. 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 study comparatively feature selection based on performance and self-reported usability data. This paper will highlight methods used to compare the feasibility and performance of hierarchical agglomerative clustering algorithms on both performance and self-reported data. The results of the experiment will then be presented and discussed before proceeding to the conclusion and future works of this study. � 2018 The authors and IOS Press. All rights reserved.
Description
Augmented reality; Cluster analysis; Learning systems; Usability engineering; Agglomerative clustering; English language teaching; Mobile augmented reality; Unsupervised machine learning; Usability; Clustering algorithms