Determination of Mobile Learning Application Usability: A Pattern Mining Approach that Goes Beyond the Statistical Method

In today’s world, the use of mobile learning applications in student-centered learning is quickly increasing. To offer the best academic outcome on their learning, student satisfaction must be taken into account. As a result, testing the usability of mobile learning systems (MLS) is critical. The usability of earlier MLS has been tested using a variety of statistical methodologies. The study’s main goals are to assess the usability of the mobile learning system using a data science method and to compare it to a statistical technique. Questionnaire results from 100 students were used to evaluate the proposed mobile learning system using a data science approach. Two pattern mining algorithms, Apriori and FP-Growth, were used to analyse these replies. The Apriori algorithm ensures 94 percent system usability, while the FP-Growth algorithm ensures 93 percent system usability, according to the results. The aggregate mean value 4.083 of the questionnaire responses was calculated as the system’s usability using the statistical approach. Finally, it is determined that while determining the usability of MLS, the pattern mining approach is more obvious than the statistical method.

Author (S) Details


D. D. M. Dolawattha

Department of Geography, Faulty of Social Sciences, University of Kelaniya, Sri Lanka.


H. K. S. Premadasa

Centre for Computer Studies, Sabaragamuwa University of Sri Lanka, Sri Lanka.

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