Mobile Technology in the Classroom: What Drives Student-Lecturer Interactions?

ABSTRACT

Online learning when combined with mobile technology transforms the traditional classrooms from teacher-centered to student-centered classrooms. Despite the widespread use of mobile technology among students and educators today, limited researches have been conducted to study the effects of using mobile technology to enhance student–lecturer interactions. In addition, existing theories of technology acceptances, chiefly Information System Success Model (ISSM), Motivational Model (MM), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), and Cultural Dimension Theory (CDT) are widely recognized for their predictive power in determining adoption intentions. In this study, determinants from all five theories were unified and examined, namely system quality and information quality from ISSM, enjoyment from MM, perceived usefulness and perceived ease of use from TAM, self-efficacy from SCT, and uncertainty avoidance from CDT as predictors of adoption intention in the context of predicting student–lecturer interactions. This empirical study was conducted using an online survey. Data collected from the samples (n = 328) were analyzed using PLS-SEM. Results obtained exhibited adequate explanatory power, where information quality, system quality, enjoyment, and uncertainty avoidance significantly predict adoption intention, while perceived usefulness, perceived ease of use, and self-efficacy were insignificant. Secondly, each theory was independently analyzed, and the predictive power and relevance of ISSM, MM, TAM, and UDT confirmed the importance and relevance of these theories. Results obtained provided a comprehensive understanding of the factors that significantly affect students’ intentions to use mobile technology to interact with their lecturers on academic matters. The discussions and implications of this study are crucial for researchers and practitioners of educational technologies in higher education.

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the authors.

Funding

This work was supported by Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS/1/2016/SS109/MMU/03/2).

Additional information

Notes on contributors

Chin Lay Gan

Dr. Gan Chin Lay is a Lecturer affiliated with the Faculty of Business, Multimedia University. Dr. Gan’s main research interest is in learning analytics, particularly related to technology-enhanced student-centered learning environments. Her research domains include teaching and learning issues such as student engagement, and educational technology integration frameworks.

Vimala Balakrishnan

Dr. Vimala Balakrishnan is a Senior Lecturer and Data Scientist affiliated with the Faculty of Computer Science and Information Technology, University of Malaya. Dr Balakrishnan’s research interests are in data analytics and sentiment analysis, particularly related to social media. Her research domains include healthcare, education, and social issues such as cyberbullying.