Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application
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
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IOS Press
2023
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my.uniten.dspace-234202023-05-29T14:40:21Z Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application Cheng L.K. Selamat A. Mohamed Zabil M.H. Selamat M.H. Alias R.A. Puteh F. Mohamed F. Krejcar O. 57188850203 24468984100 35185866500 57215520379 25928253600 57202529348 55416008900 14719632500 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 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. Final 2023-05-29T06:40:21Z 2023-05-29T06:40:21Z 2017 Conference Paper 10.3233/978-1-61499-800-6-731 2-s2.0-85029220080 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029220080&doi=10.3233%2f978-1-61499-800-6-731&partnerID=40&md5=4c8aa48bc07a1160601b146794b2c202 https://irepository.uniten.edu.my/handle/123456789/23420 297 731 744 IOS Press Scopus |
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| 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 |
| author2 |
57188850203 |
| author_facet |
57188850203 Cheng L.K. Selamat A. Mohamed Zabil M.H. Selamat M.H. Alias R.A. Puteh F. Mohamed F. Krejcar O. |
| format |
Conference Paper |
| author |
Cheng L.K. Selamat A. Mohamed Zabil M.H. Selamat M.H. Alias R.A. Puteh F. Mohamed F. Krejcar O. |
| spellingShingle |
Cheng L.K. Selamat A. Mohamed Zabil M.H. Selamat M.H. Alias R.A. Puteh F. Mohamed F. Krejcar O. Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application |
| author_sort |
Cheng L.K. |
| title |
Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application |
| title_short |
Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application |
| title_full |
Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application |
| title_fullStr |
Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application |
| title_full_unstemmed |
Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application |
| title_sort |
usability prioritization using performance metrics and hierarchical agglomerative clustering in mar-learning application |
| publisher |
IOS Press |
| publishDate |
2023 |
| _version_ |
1806425751195484160 |
| score |
13.18916 |