  In the field of human-computer interaction (HCI), the usability assessment of
m-learning (mobile-learning) applications is a real challenge. Such assessment
typically involves extraction of the best features of an application like
efficiency, effectiveness, learnability, cognition, memorability, etc., and
further ranking of those features for an overall assessment of the quality of
the mobile application. In the previous literature, it is found that there is
neither any theory nor any tool available to measure or assess a user
perception and assessment of usability features of a m-learning application for
the sake of ranking the graphical user interface of a mobile application in
terms of a user acceptance and satisfaction. In this paper, a novel approach is
presented by performing a mobile applications quantitative and qualitative
analysis. Based on user requirements and perception, a criterion is defined
based on a set of important features. Afterward, for the qualitative analysis,
a genetic algorithm (GA) is used to score prescribed features for the usability
assessment of a mobile application. The used approach assigns a score to each
usability feature according to the user requirement and weight of each feature.
GA performs the rank assessment process initially by performing feature
selection and scoring the best features of the application. A comparison of
assessment analysis of GA and various machine learning models, K-nearest
neighbours, Naive Bayes, and Random Forests is performed. It was found that a
GA-based support vector machine (SVM) provides more accuracy in the extraction
of the best features of a mobile application and further ranking of those
features.
