Development and Alpha Testing of EzHifz Application: Al-Quran Memorization Tool

Mustafa, Nor Musliza and Mohd Zaki, Zulkifly and Mohamad, Khairul Anuar and Basri, Mokmin and Ariffin, Sedek and Troussas, Christos (2021) Development and Alpha Testing of EzHifz Application: Al-Quran Memorization Tool. Advances in Human-Computer Interaction, 2021. pp. 1-10. ISSN 1687-5893

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Abstract

Learning to memorize the Quran presents a challenge. This paper reports the development and alpha testing of a mobile application called “EzHifz” for Quran memorization based on the VARK learning style. The application received positive feedback for user acceptance testing and heuristic testing. The Fleiss kappa coefficient (κ) results for user acceptance testing show a very good level of agreement (κ = 0.850). Heuristic testing results show that κ = 0.731 for content, manual guide, memorization activities, performance information, and tasmik assessment attributes, while κ = 0.727 for presentation design, interactivity, multimedia elements, attraction, and motivation attributes. These results show a good level of agreement, which indicates that the EzHifz application meets the requirements of design and development based on the attributes evaluated. A combination of memorizing techniques in the application helps strengthen the use of preferred VARK learning styles. The techniques support the use of multiple senses that could facilitate the process of memorizing the Quran independently. This study contributes to the novel design and evaluation of the Quran memorization application based on the Quran memorization model. The application supports the teaching and learning of Quran memorization where it allows students to select their preferred VARK learning style with the technique of memorizing the Quran. This mobile application learning approach based on VARK’s learning style has the potential to be implemented in the process of memorizing the Quran as well as retaining memory through the use of memory senses in support of the learning materials developed.

Item Type: Article
Subjects: STM Repository > Computer Science
Depositing User: Managing Editor
Date Deposited: 16 Dec 2022 12:49
Last Modified: 01 Jul 2024 06:25
URI: http://classical.goforpromo.com/id/eprint/370

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