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

Kok Cheng Lim, Ali Selamat, Mohd Hazli Mohamed Zabil, Md Hafiz Selamat, Rose Alinda Alias, Fatimah Puteh, Farhan Mohamed, Ondrej Krejcar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNew Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017
EditorsHamido Fujita, Ali Selamat, Sigeru Omatu
PublisherIOS Press
Pages731-744
Number of pages14
ISBN (Electronic)9781614997993
DOIs
Publication statusPublished - 01 Jan 2017
Event16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017 - Kitakyushu, Japan
Duration: 26 Sep 201728 Sep 2017

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume297
ISSN (Print)0922-6389

Other

Other16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017
CountryJapan
CityKitakyushu
Period26/09/1728/09/17

Fingerprint

Learning systems
Augmented reality
Learning algorithms
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Lim, K. C., Selamat, A., Mohamed Zabil, M. H., Selamat, M. H., Alias, R. A., Puteh, F., ... Krejcar, O. (2017). Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application. In H. Fujita, A. Selamat, & S. Omatu (Eds.), New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017 (pp. 731-744). (Frontiers in Artificial Intelligence and Applications; Vol. 297). IOS Press. https://doi.org/10.3233/978-1-61499-800-6-731
Lim, Kok Cheng ; Selamat, Ali ; Mohamed Zabil, Mohd Hazli ; Selamat, Md Hafiz ; Alias, Rose Alinda ; Puteh, Fatimah ; Mohamed, Farhan ; Krejcar, Ondrej. / Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application. New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. editor / Hamido Fujita ; Ali Selamat ; Sigeru Omatu. IOS Press, 2017. pp. 731-744 (Frontiers in Artificial Intelligence and Applications).
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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.",
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Lim, KC, Selamat, A, Mohamed Zabil, MH, Selamat, MH, Alias, RA, Puteh, F, Mohamed, F & Krejcar, O 2017, Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application. in H Fujita, A Selamat & S Omatu (eds), New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. Frontiers in Artificial Intelligence and Applications, vol. 297, IOS Press, pp. 731-744, 16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017, Kitakyushu, Japan, 26/09/17. https://doi.org/10.3233/978-1-61499-800-6-731

Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application. / Lim, Kok Cheng; Selamat, Ali; Mohamed Zabil, Mohd Hazli; Selamat, Md Hafiz; Alias, Rose Alinda; Puteh, Fatimah; Mohamed, Farhan; Krejcar, Ondrej.

New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. ed. / Hamido Fujita; Ali Selamat; Sigeru Omatu. IOS Press, 2017. p. 731-744 (Frontiers in Artificial Intelligence and Applications; Vol. 297).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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Lim KC, Selamat A, Mohamed Zabil MH, Selamat MH, Alias RA, Puteh F et al. Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application. In Fujita H, Selamat A, Omatu S, editors, New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. IOS Press. 2017. p. 731-744. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/978-1-61499-800-6-731