M-Readiness Assessment Model Development and Validation: Investigation of Readiness Index and Factors Affecting Readiness

M-Readiness Assessment Model Development and Validation: Investigation of Readiness Index and Factors Affecting Readiness

Muhammad Bakhsh (Department of Computer Sciences, Allama Iqbal Open University, Islamabad, Pakistan), Amjad Mahmood (Department of Computer Sciences, Allama Iqbal Open University, Islamabad, Pakistan & Department of Computer Science, College of Information Technology, University of Bahrain, Manama, Bahrain) and Nazir Ahmed Sangi (Department of Computer Science, Allama Iqbal Open University, Islamabad, Pakistan)
Copyright: © 2018 |Pages: 23
DOI: 10.4018/IJDET.2018010101

Abstract

It is important for distance learning institutions to be well prepared before designing and implementing any new technology based learning system to justify the investment and minimize failure risk. It can be achieved by systematically assessing the readiness of all stakeholders. This paper first proposes an m-readiness assessment process and subsequently describes different activities required to develop a m-readiness assessment model. The model was validated and applied to measure m-readiness index of Allama Iqbal Open University (AIOU), Pakistan. Primary data (N=612) were collected. Factor analysis was applied. Extracted factors were confirmed with confirmatory factor analysis. Based on the extracted factors, m-readiness index was calculated. Furthermore, m-readiness index association with age, gender, program of study, income, locality and province were also analyzed by applying one way ANOVA and Kruskal Wallis. The results reveal that there is a significant difference in m-readiness with respect to income, locality, age, gender, program of study and province.
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1. Introduction

The programs offered by open and distance educational institutions (ODEIs) are growing at a rapid pace primarily due to the availability and affordability of latest information and communication technologies. Adults, especially those who need higher education but do not have access to the traditional education, are attracted more towards ODEIs. These adult learners are mostly interested in technology based learning such as e-learning, m-learning and blended learning (Newhouse, Williams & Pearson, 2006; Pettit & Kukulska-Hulme, 2007; Ilgaz & Gulbahar, 2015).

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, 2006), but has some distinct features also. 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.

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