Publication:
Triangulating the implementation of hierarchical agglomerative clustering on MAR-learning usability data

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dc.contributor.authorLim K.C.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorMohamed Zabil M.H.en_US
dc.contributor.authorYusoff Y.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.authorid56921898900en_US
dc.contributor.authorid57215520379en_US
dc.contributor.authorid25928253600en_US
dc.contributor.authorid57202529348en_US
dc.contributor.authorid55416008900en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T07:23:57Z
dc.date.available2023-05-29T07:23:57Z
dc.date.issued2019
dc.descriptionAugmented reality; Clustering algorithms; Machine learning; Usability engineering; English language teaching; Hierarchical agglomerative clustering; Mobile augmented reality; Performance metrics; Self-reported metrics; Usability; Hierarchical clusteringen_US
dc.description.abstractThis paper presents fractions of research outcome from a bigger project involving machine learning, Hierarchical Agglomerative Clustering (HAC) Algorithms on usability data gathered through performance and self-reported data. This paper highlights the common problems in usability studies where the conventional analysis was frequently utilized while prioritizing usability issues. The utilization of clustering techniques is limited in the area of this study. A previous publication has shown how HAC was used in clustering usability problems in Mobile Augmented Reality (MAR) learning applications. However, there has not been a triangulation effort to confirm the first gathered results due to small datasets. This research presents a methodology adopted from previous studies in confirming earlier usability analysis results. The experiments found consistent evidence approving the feasibility of HAC in clustering and prioritizing Usability performance and self-reported data. � 2019 The authors and IOS Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/FAIA190075
dc.identifier.epage511
dc.identifier.scopus2-s2.0-85082080723
dc.identifier.spage500
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082080723&doi=10.3233%2fFAIA190075&partnerID=40&md5=4b64006fced34966cd199f9e082d1328
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24489
dc.identifier.volume318
dc.publisherIOS Pressen_US
dc.sourceScopus
dc.sourcetitleFrontiers in Artificial Intelligence and Applications
dc.titleTriangulating the implementation of hierarchical agglomerative clustering on MAR-learning usability dataen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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