Students’ Perceived M-Learning Quality

An Evaluation and Directions to Improve the Quality for H-Learning

Dados Bibliográficos

ID22042387
AutoresSyed Faizan Hussain Zaidi (0000-0003-1931-1004, Dr. A.P.J. Abdul Kalam Technical University), Atik Kulakli (0000-0002-2368-3225, American University of the Middle East), Valmira Osmanaj (0000-0002-9864-8627, Dr. A.P.J. Abdul Kalam Technical University), Syed Ahasan Hussain Zaidi (0000-0001-9284-4769, Dr. A.P.J. Abdul Kalam Technical University)
Ano2023
Volume13
Fascículo6
Páginas578
Data de publicação2023-06-04
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEducation Sciences (JOURNAL)
Identificadores do periódicoISSN: 2227-7102 • E-ISSN: 2227-7102
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci13060578
OpenAlexW4379473304
IdiomaEN
Citações recebidas1
Referências citadas68

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

Blended Learning · Continuance · Educational technology · Higher education · Knowledge management · Mathematics education · Political science · Computer Science · Educational Technology and E-Learning · Mobile Learning in Education · Psychology · Social Psychology · Technology-Enhanced Education Studies