Students’ Perceived M-Learning Quality: An Evaluation and Directions to Improve the Quality for H-Learning

Abstract

The COVID-19 pandemic has transformed the paradigm of the higher education sector and has instigated a speedy consumption of a diverse range of mobile learning software systems. Many universities were adhering to online modes of education during the pandemic; however, some of the universities are now following hybrid modes of learning, termed h-learning. Higher education students spent two years of taking their classes online during the COVID-19 pandemic and have experienced various challenges. Simultaneously, the main challenge for higher education institutions remains how to consistently offer the best quality of students’ perceived m-learning and maintain continuance for the new shift towards hybrid learning. Hence, it becomes essential to determine the m-learning quality factors that would contribute to maintaining superior m-learning quality in higher education during the COVID-19 pandemic and afterwards via a hybrid mode of learning. Thus, the m-learning quality (MLQual) framework was conceptualized through an extensive review of the literature, and by employing survey-based quantitative research methods, MLQual was validated via structural equation modeling (SEM) techniques. The outcome of this research yielded the MLQual framework used to evaluate the students’ perceived m-learning quality and will offer higher education practitioners the chance to upgrade their higher education policies for h-learning accordingly. With the preceding discussion, it is evident that evaluation of the students’ perceived m-learning quality factors in higher education is always a question that should be researched adequately. Determination of such m-learning quality factors is essential in order to offer significant directions to the higher education practitioners for improving both the quality and delivery of m-learning and h-learning. Consequently, the present study embraces two key objectives: First, to identify and evaluate the m-learning quality factors which could be employed to improve the quality of m-learning. Second, to propose the MLQual framework for the evaluation of students’ perceived m-learning quality.

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