Mobile learning application usability: A pattern mining approach

Abstract

User satisfaction is very important for mobile learning applications to provide the maximum academic outcome. Hence evaluating mobile learning systems is important to test their usability. Most of the previous studies used statistical approaches to test the usability of learning systems. The main objective of this study is to evaluate the usability of the mobile learning system using a data science approach. To evaluate the proposed mobile learning system, responses for a questionnaire were obtained from 100 system users After applying several preprocessing steps, the responses were evaluated using two pattern mining algorithms: Apriori and FP-Growth. According to the results, the Apriori algorithm shows 94% system usability while the FP-Growth algorithm ensures 93% system usability. It confirms the proposed mobile learning system’s usability. Furthermore, it was observed that this pattern mining-based approach can be successfully applied in usability evaluation for learning systems.