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Procedia Computer Science
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License: CC BY NC ND
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Procedia Computer Science
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Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics

Authors: Seren Başaran; Yunusa Haruna;

Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics

Abstract

Abstract Growing number of mobile learning applications particularly for mathematics (MLAM) have dramatically changed the way individuals learn mathematics in recent years. However, due to abundant number of applications, MLAM users encounter with difficulty in choosing the right application for their choice. The manual selection of these applications is tedious, time consuming and in most instances effectuated premature selection. There is also lack of research about determining the quality of MLAMS and users heavily rely on the information provided either by the application store ratings or by content of the developer which serve largely commercial purposes. Therefore the aim of this study is twofold; to propose quality and user satisfaction model and to evaluate MLAMs by applying multi-criteria FAHP and TOPSIS methods together. The criteria were defined based on the combination of technical and non-technical aspects of the applications. The ISO 9126 model was used for the evaluation of technical aspects while user satisfaction was used for evaluating non-technical aspects. The weight of each criterion identified in the framework was determined through FAHP and MLAMs were ranked based on preference with TOPSIS and methods respectively. Mathsway, Malmaths, Cymaths, Mathematics and Mathspapa are the applications chosen as sample MLAMs Play Store based on high top 5 highest user ratings. According to the ranking results by TOPSIS method, the learning application Mathematics was ranked first, then Cymaths, Mathway, Malmaths and Mathspapaas last. The proposed framework could be extended to serve as a model for evaluating mobile application in general. The adoption of combining FAHP and TOPSIS methods yielded to less time consuming and more effective optimizations as a result selecting the most suitable MLAM. The integration of these methods could significantly improve the evaluation of MLAMs by minimizing the manual expert evaluation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Top 10%
Top 10%
Average
gold