Publication:
Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application

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dc.citedby9
dc.contributor.authorCheng L.K.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorMohamed Zabil M.H.en_US
dc.contributor.authorSelamat M.H.en_US
dc.contributor.authorAlias R.A.en_US
dc.contributor.authorPuteh F.en_US
dc.contributor.authorMohamed F.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid57215520379en_US
dc.contributor.authorid25928253600en_US
dc.contributor.authorid57202529348en_US
dc.contributor.authorid55416008900en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T06:40:21Z
dc.date.available2023-05-29T06:40:21Z
dc.date.issued2017
dc.descriptionArtificial 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 algorithmsen_US
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/978-1-61499-800-6-731
dc.identifier.epage744
dc.identifier.scopus2-s2.0-85029220080
dc.identifier.spage731
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85029220080&doi=10.3233%2f978-1-61499-800-6-731&partnerID=40&md5=4c8aa48bc07a1160601b146794b2c202
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23420
dc.identifier.volume297
dc.publisherIOS Pressen_US
dc.sourceScopus
dc.sourcetitleFrontiers in Artificial Intelligence and Applications
dc.titleUsability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning applicationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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