M-Learning Acceptance Among Faculty and Students in Pakistan: A Structural Equation Modeling

M-Learning Acceptance Among Faculty and Students in Pakistan: A Structural Equation Modeling

Muhammad Bakhsh (Pakistan Academy for Rural Development, Pakistan), Amjad Mahmood (Abasyn University Islamabad, Pakistan), Nazir A. Sangi (Allama Iqbal Open University, Pakistan) and Muhammad Javed Iqbal (UET Taxila, Pakistan)
Copyright: © 2019 |Pages: 35
DOI: 10.4018/978-1-5225-8015-7.ch011
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Fast development in mobile phones has enabled higher educational institutions to adapt and initiate mobile technologies. It can also make way for learning using mobile devices and systems. This chapter investigated the present state of student and faculty perception towards m-learning at open and distance educational institutes (ODEIs) in Pakistan. The chapter presents a conceptual model based on technology acceptance model, which explains the factors influencing student and faculty perception towards m-learning acceptance. Since m-learning acceptance mainly depends on personal attitude, this study focuses on individual context. Primary data from students and faculty (N=612) was collected. The SEM results indicate that skill readiness (SK) and self-efficacy (SE) influence perceived ease of use (PEOU) and perceived usefulness (PU), where these two factors along with prior experience (PE) positively influence behavioral intension (BI) to accept mobile learning. Furthermore, the results of this study specifically provide factors which positively influence BI either directly or indirectly.
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A learning technique which uses mobile devices and wireless transmission in the learning process is called mobile learning or m-learning (Naismith, Lonsdale, Vavoula & Sharples, 2004; Yuen & Yuen, 2008; Chang, Sheu & Chan, 2003; Gwo-Jen, Ting-Ting & Yen-Jung, 2007; Mohd & Mohd, 2007; Elmorshidy, 2012; Lorna & Gwo-Jen 2013). M-learning carries many components of e-learning (Horton, 2011), but has some distinct features. The three key features of mobile devices i.e. portability, anywhere and anytime connectivity (Cochrane & Bateman, 2010), context sensitivity differentiates m-learning from e-learning (Sharples, 2000; Klopfer, Squire & Jenkins, 2002; Churchill & Churchill, 2008). These distinctive aspects of mobile devices give unique learning experience to m-learners (Wang & Higgins, 2006; Traxler, 2007, 2008, 2010). M-learning features support four type of learning approaches which are individual learning, situated learning, collaborative learning, and informal learning (Cheon, Sangno, Steven & Jaeki, 2012).

The formal method of teaching has its own importance but m-learning can facilitate the formal learning in higher education (Tin-Yu, I-Shou, Han-Chieh, 2008; Campanella, 2012). Mobile technologies facilitate learners by giving them easy and instant access to information rich digital resources. Mobile learning can also play a supplementary role within formal education (Cheon et al. 2012). The potential benefits of mobile learning include less cost, content ubiquity, flexibility in communication and learning. Furthermore, mobile applications can be used for intra-communication among students and teachers.

There have been a large number of studies to determine the technology based system readiness of different organizations and universities. The model for the e-readiness assessment of institution and perception of students and faculty about e-learning for low bandwidth areas was proposed by Suhail (2014). The proposed model lacks theoretical validity and statistical reliability. Factor analysis was used to assess the e-readiness in Nigerian universities with negative results due to low tele-density, connectivity and electricity problems (Eweni, Joseph, Victor & Simeon, 2013). The sample data used in their study was too small and no relation was assessed between the variables. Quantitative and qualitative measures were used to determine the e-readiness and e-satisfaction of students at the University of Ankara with encouraging results (Ilgaz & Gulbahar, 2015).

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