Efficiency Assessment of Mobile Game-based Learning based on Fingerstroke Level Model (FLM)

The development of digital games, including digital game-based learning (DGBL), has rapidly grown over the past decade. DGBL is an alternative learning model that aims to increase student motivation and analyze the learning process. However, subjective assessments are commonly used to evaluate the e...

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Bibliographic Details
Published in2023 International Seminar on Application for Technology of Information and Communication (iSemantic) pp. 107 - 111
Main Authors Alfan, Muhammad Bahauddin, Arifin, Miftahol, Rochimah, Siti, Yuhana, Umi Laili
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.09.2023
Subjects
Analytical models
Complexity theory
effectiveness
finger stroke
game-based learning
Games
Real-time systems
Seminars
Stakeholders
Trajectory
usability
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Summary:The development of digital games, including digital game-based learning (DGBL), has rapidly grown over the past decade. DGBL is an alternative learning model that aims to increase student motivation and analyze the learning process. However, subjective assessments are commonly used to evaluate the effectiveness of DGBL, which has limitations, particularly in measuring player motivation in real-time during gameplay. Objective assessment approaches, such as the Fingerstroke Level Model (FLM), which quantitatively measures the speed of a user in completing a task based on the interface design, are necessary to evaluate usability features. The FLM has been implemented in educational games on mobile touch input devices and provides objective feedback for users and game-based learning developers. This research focuses on the efficiency of digital game-based learning that has been developed, whether it meets the standards of stakeholders and game developers. The results of the study show that there are several operators that are not applied by the user ideally, but with the leveling of these operators the efficiency increases because of the learning process at each level.
DOI:10.1109/iSemantic59612.2023.10295286