Goal Question Metric as an Interdisciplinary Tool for Assessing Mobile Learning Application

Yee Wai, Sim and WaiShiang, Cheah and Piau, Phang and Kai Chee, Lam and Eaqerzilla, Phang and Nurfauza, Jali (2022) Goal Question Metric as an Interdisciplinary Tool for Assessing Mobile Learning Application. (IJACSA) International Journal of Advanced Computer Science and Applications, 13 (5). pp. 146-152. ISSN 2156-5570

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Abstract

Assessing the mobile learning application among interdisciplinary researchers is a non-trivial task. Mandarin Learning App is a Mandarin 3D game tailor-made for students who choose PBC1033 Mandarin Language Level 1 as an elective course. It is an interdisciplinary project which it involves researchers from software engineering, computational science/mathematics and the Faculty of Language studies. In the project, the software engineer focuses on producing a quality application mostly through usability studies; the language teacher focuses on students’ study performance upon using the Mandarin Learning App and the mathematician focuses on finding the statistical data dependency of collected data through the various statistical packages. Hence, we are facing issues like how to reach a consensus in working on assessing the Mandarin 3D games? How to enable the discussion among the researchers; how to consolidate the results so that we can understand? We introduce Goal Question Metric to tackle these issues. In this paper, we demonstrate how Goal Question Metric is used to form a holistic view of assessing requirements on mobile applications and guide the discussion and reach consensus in analyzing the results of the evaluation. The contribution of this paper is to introduce Goal Question Metric as an interdisciplinary tool while assessing the mobile learning application. With Goal Question Metric, we demonstrate how it can structure the assessment from a different viewpoint in a comprehensive and systematic manner; 1) better structure of the experiments, 2) able to reach consensus among researchers from different disciplines, 3) able to analyze the dependencies among various experiments and 4) able to find hidden results.

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