A Genetic Algorithm-Based Support Vector Machine Approach for Intelligent Usability Assessment of m-Learning Applications

TL;DR

This paper introduces a novel genetic algorithm-based support vector machine approach for assessing and ranking usability features of m-learning applications, improving accuracy over traditional machine learning methods.

Contribution

It presents a new GA-SVM method for usability assessment of mobile learning apps, combining feature scoring and ranking based on user perceptions.

Findings

GA-SVM outperforms KNN, Naive Bayes, and Random Forests in accuracy

The approach effectively ranks usability features based on user requirements

Enhanced feature selection improves mobile app usability evaluation

Abstract

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.