Do not judge an interactive book by its cover

Uras, Selene; Ardu, Daniele; Paddeu, Gavino; Deriu, Massimo

doi:10.1145/2428955.2428966

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…The reliability of a single technique, as mentioned in G4, can be questionable, with common problems of user performances where the audience can be small and self-reported approaches that carry risks of invalid data inconsistencies. Despite the disadvantages of both approaches, some authors like [137] and [79] could still produce results with big sample audiences (50 samples or more) and report on tangible results using hybrid usability techniques.…”

Section: Limited Quality Versus Large Sample Convenience (G4)mentioning

confidence: 99%

Usability Measures in Mobile-Based Augmented Reality Learning Applications: A Systematic Review

et al. 2019

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The implementation of usability in mobile augmented reality (MAR) learning applications has been utilized in a myriad of standards, methodologies, and techniques. The usage and combination of techniques within research approaches are important in determining the quality of usability data collection. The purpose of this study is to identify, study, and analyze existing usability metrics, methods, techniques, and areas in MAR learning. This study adapts systematic literature review techniques by utilizing research questions and Boolean search strings to identify prospective studies from six established databases that are related to the research context area. Seventy-two articles, consisting of 45 journals, 25 conference proceedings, and two book chapters, were selected through a systematic process. All articles underwent a rigorous selection protocol to ensure content quality according to formulated research questions. Post-synthesis and analysis, the output of this article discusses significant factors in usability-based MAR learning applications. This paper presents five identified gaps in the domain of study, modes of contributions, issues within usability metrics, technique approaches, and hybrid technique combinations. This paper concludes five recommendations based on identified gaps concealing potential of usability-based MAR learning research domains, varieties of unexplored research types, validation of emerging usability metrics, potential of performance metrics, and untapped correlational areas to be discovered.

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Section: Limited Quality Versus Large Sample Convenience (G4)mentioning

confidence: 99%