Mobile Learning (M-learning) refers to any kind of learning which takes place within and beyond the traditional learning environment via wireless mobile devices. These devices are able to move with the learner to allow learning anytime, anywhere. M-learning is considered as the next step beyond electronic learning (E-learning) and distance learning (D-learning) by using mobile wireless devices with internet connectivity to facilitate formal and informal learning. Over the past decade M-learning has become gradually popular in university settings by providing mobile access to learning resources, collaborative learning and to exchange formative evaluation and feedback between students and instructors. Therefore, M-learning involves learning activities that are not restricted to a specific time or place.
Despite the familiarity with M-learning as a new paradigm in modern education, there has been a shortage of research concerning how to deploy this technology in a successful way. The integration of M-learning in a university environment needs to involve some aspects in terms of the readiness of users and institutions, users‟ acceptance and engagement, and the sustainability of the system. There are some initial models that investigate the implementation of M-learning which provide some guidelines that work as starting point for the future of M-learning deployment. However, there is no theoretical model that provides guidelines for staged deployment of M-learning. In addition, there was no clear definition of sustainability factors that will assure continues evaluation and upgrade of M-learning systems after deployment.
The aims of this research work are to study students‟ readiness for M-learning, investigate the factors that affect students‟ acceptance and analyse M-learning literature in order to propose and evaluate a model which can be used to foster the sustainable deployment of M-learning within teaching and learning strategies in higher education institutions. The research was conducted in Brunel University, West London. Data were collected from School of Information, Computing and Mathematical Science students using three surveys: the first studied students‟ readiness for M-learning, the second investigated factors that affect students‟ acceptance of M-learning and the last one developed and evaluated a sustainable M-learning deployment model.
The outcome of this research lead to a conceptual model that gives a wide overview of all elements that need to be addressed in the M-learning environment and bridges the gap between the pre- and post-implementation phases in order to ensure sustainability. Furthermore, the model provides university educators with a planned approach to incorporate M-learning in higher education curriculums with the aim of improving teaching and learning.
M-learning deployment, M-learning sustainability, M-learning deployment model, M-learning university, Student acceptance of m-learning
This chapter provides an overview of the research background, statement of the problem, research questions and objectives of the research, followed by the significance of the research. Finally, a discussion of the research approach and the outline of the thesis are presented.
Background of the Study
Technology has a fundamental impact on the educational system. Nowadays technology plays a significant role in teaching and learning processes, whether supportive or administrative. Educational technologies have become increasingly important in the higher education environment due to the rapid proliferation of the internet and personal computers. Computers and the internet are educational tools which offer efficient use of time and ease of access to educational materials for students and staff alike. Most universities have adapted a range of management learning systems (MLS) such as Blackboard and Moodle. This revolution of information and communication technology (ICT) has facilitated communication, sharing data and collaboration between students and between students and their lecturers.
In recent years, computing wireless devices have become ubiquitous in today‟s college campuses (Motiwalla, 2007). The advent of mobile devices like smart phones, PDAs and tablet PCs give people the freedom to use what they need, where and when it is needed (Trifonova and Ronchetti, 2007). Mobile devices have become more affordable, effective and easy to use (Nassuora, 2012). These devices can extend the benefits of E-learning systems (Motiwalla,2007) by offering university students opportunities to access course materials and ICT, learn in a collaborative environment (Nassuora, 2012) and obtain formative evaluation and feedback from instructors (Crawford, 2007). Mobile devices can extend the learning process beyond university settings by providing flexible, portable and independent learning environments; they can allow students a method of communication both among themselves and between them and their lecturers (Khaddage et al., 2009). In addition, these devices also give students and lecturers an opportunity to exploit their spare time while traveling to work on an assignment or in lesson preparation (Virvou and Alepis, 2005).
Mobile learning (M-learning) is regarded as a new stage in the development of computer support and distance learning (Georgieva, Trifonva and Georgiev, 2006). M-learning is a new learning paradigm created by mobile devices and wireless networks which support accessible and collaboration education at all levels including schools, colleges and universities. It is considered as the next step of E-learning system and distance learning, further enhancing learning anytime, anywhere (Milrad, 2003; Georgieva, Trifonva and Georgiev, 2006; Motiwalla, 2007). Salmon (2004) considered it as the fourth generation of the electronic learning environment. It can be defined as any sort of learning that occurs when the learner is not bound by location or time; it can happen anytime, anywhere, with the services offered by mobile technology devices that present learning content and allow wireless communication between lecturers and students (Dye, Solstad and K‟Odingo, 2003).
M-learning provides an option for self-study (Eschenbrenner and Nah, 2007; Jacob and Issac, 2008a) by making course materials and educational resources readily available and easily accessible. In addition, M-learning facilitates the interaction between students and teachers in the classroom and allows the exchange of information outside the university (Lam et al., 2011). It is likely to become one of the most effective ways of delivering higher education materials in the future (El-Hussein and Cronje, 2010). Despite the fast spread of mobile devices and wireless networks within university campuses, and the advantages of M-learning in higher education, M-learning will not replace the traditional classroom or the electronic learning system, but it can work as additional support to complement and add value to the existing learning models (Motiwalla, 2007).
The potential of M-learning is being realised in educational environments around the world, and many studies have investigated the use of M-learning to facilitate teaching and learning in higher education (Hayes, Joyce and Pathak, 2004; Keane and Crews, 2007; Lee and Chan, 2007; Rekkedal and Dye, 2007; Cavus, 2011). Both learners and lecturers have noted the advantages of M-learning, which include flexibility, mobility and availability (Triantafillou, Georgiadou and Economides, 2006; Rekkedal and Dye, 2007; Yordanova, 2007). However, M-learning is still in the early stage of development (Motiwalla, 2007; Park, 2011). In some cases, implementation resistance and institute infrastructure shortcomings have inhibited the successful uptake of new educational technologies.
Liu and Han (2010) indicated that M-learning has not reached its maximum potential and there is a gap between what is offered and what is used. There are several issues facing the adoption of M-learning: the technical limitations regarding connectivity, small screen size, inadequate memory and slow network speeds (Wang and Higgins, 2006; Wang, Wu and Wang, 2009; Haag, 2011; Park, 2011); pedagogical issues regarding the use of mobile devices in classrooms, such as potential to disturb the learning process (Corbeil and Valdes-Corbeil, 2007; Park, 2011); and users‟ acceptance (both students and lecturers) to adopt this technology. User acceptance of new technology is an important key concern for institute management considering investment in technology. Users‟ unwillingness to adopt new technology can cause system failure and end-up being of no benefit for the institution (Taylor and Todd, 1995a; Davis and Vanketash, 1996). The success of M-learning system might may depend on users‟ willing to utilize new technology which different from what they have used before (Wang, Wu and Wang, 2009), therefore investigating factors influencing students‟ acceptance of M-learning is an essential step before the implementation stage in order to ensure that time and money invested in M-learning is used efficiently (i.e. to promote successful adoption and use). Furthermore, this will help universities to deliver high quality services to students and improve their pedagogical and learning strategic plans.
The deployment of M-learning in higher education needs a lot of effort to overcome all difficulties facing the deployment of this new technology. There are several issues facing M-learning deployment such as lack of awareness and motivation (Wang, Wu and Wang, 2009), technical aspects regarding suitable mobile devices and internet connectivity issues (Naismith and Corlett, 2006; Park, 2011), and issues related to the institutes‟ challenges and resistance to change (Vavoula et al., 2004; Cobcroft et al., 2006); some university lecturers do not want to apply this technology, or might face some difficulties in trying to use it effectively, as it may require a lot of effort on their part to ensure implementation (Abu-Al-Aish, Love and Hunaiti, 2012). In order to improve M-learning outside the classroom and lecturer theatres in both the real and virtual environments, a significant investment of time, resources and effort is required of institutions and stakeholders.
Due to the pioneering nature of M-learning deployment (Motiwalla, 2007; Liaw, Hatala and Huang, 2010), there is a shortage of academic studies investigating the phenomenon in higher education. However, deployment has risen markedly since 2007 (Ng and Nicholas, 2012). Cobcroft et al. (2006) indicated that a successful conceptual framework for designed M-learning needs to consider learners‟ creativity, collaboration, communication and critical engagement. Naismith et al. (2004) observed that educational institutes have to adopt policies that support the integration of mobile devices into the formal learning environment. Naismith and Corlett (2006) indicated some critical success factors for implementing M-learning, derived from a number of M-learning projects from 2002-2005, comprising availability of technology, institutional support, integration, connectivity and ownership. Vavoula and Sharples (2009) determined six aspects that are presented as challenges in developing M-learning initiatives. These aspects are capturing and analysing learning in context and across context, assessing M-learning system and outcomes, utility and usability assessment of mobile technology, organizational and socio-cultural context and identifying the characteristics of M-learning learning environment in terms of formal and informal learning.
Thus, the deployment of M-learning in higher education necessitates guidelines on how to build an effective and sustainable M-learning system that attracts all users and provides them with services that meet their needs while overcoming all infrastructure challenges and institutes‟ resistance to change. Therefore, there is a need to investigate all critical success factors that ensure the success deployment of M-learning system.
Statement of the Problem
There is limited understanding of the factors that influence the deployment of M-learning in higher education. In addition, there is also a shortage of resources available for all M-learning stakeholders on how to deploy and support M-learning in university education (Litchfield et al., 2007; Cherian and Williams, 2008). The availability of wireless mobile devices and connectivity to the internet do not in themselves achieve sustainable M-learning deployment. Therefore, there is a need to investigate the factors that influence the adoption and deployment of M-learning in the higher education context. By identifying the critical factors that ensure the successful deployment of M-learning, universities can align their strategic planning with the demands of students and lecturers, make meaningful integration of technology in teaching and learning and enhance better policy decisions. There is a lack of M-learning deployment models which guide the deployment of M-learning in the educational context. Furthermore, no available model provides a theoretical approach to guide the strategy of M-learning deployment. The following are some models related to M-learning deployment that can serve as starting points for the development of a sustainable M-learning model. These models are explained in chapter two:
M-learning Framework (Mostakhdemin-Hosseini and Tuimala , 2005).
Model for Framing M-learning (FRAME) (Koole, 2006). Proposed Theoretical Model for M-learning in Developing Countries (Barker et al., 2005).
A Framework for Sustainable Mobile Learning in Schools (Ng and Nicholas, 2012).
A conceptual model for the educational deployment of QR codes (Saravani and Clayton, 2009).
These models are limited in their practical applicability. No one of the previous M-learning models/frameworks has defined guidelines that consider the stages for the deployment of M-learning. In addition, they did not provide any clear definition of sustainability factors to assure continuous evaluation and upgrading of M-learning systems after deployment. Therefore, there is a need to develop and evaluate a model that clarifies M-learning pre-deployment success factors and provides post-deployment sustainability factors.
Aims of the Study This research aims to investigate the following questions:
What is the level of students‟ readiness for M-learning system?
What are students‟ expectations towards mobile learning services and the challenges that might affect the implementation of this new technology?
What are the factors influencing students‟ acceptance towards M-learning in higher education?
What are the key issues and critical success factors that are essential to ensure successful deployment of M-learning?
How can the identified factors be worked (or considered) into development of a sustainable M-learning model for the higher education environment?
Related to the research questions, the main objective of this research is to study and analyse the factors that affect the adoption and deployment of M-learning in the higher education environment in order to develop a successful and sustainable M-learning model. The model will consist of pre- and post-deployment stages including all key issues and critical success factors that are essential to ensure successful deployment.
Significance of the Research
The outcomes of this research include the development of a sustainable M-learning deployment model for higher education. This model represents a roadmap that identifies the challenges facing the deployment of M-learning in university education and also to involve all the elements that need to be in place for M-learning deployment. The findings of this research will be of interest of educators and university managers concerned with the adoption and deployment of M-learning in higher education. By developing and evaluating a sustainable M-learning deployment model with pre- and post-deployment stages, including all key issues and critical success factors that are essential to ensure successful deployment, this research provides educational professional with insight how M-learning can be harnessed in order to adapted in higher educational institutes. The outcomes of this research might also be useful to educational designers who are in charge of designing university courses.
This research is divided into three phases: exploring students‟ readiness for M-learning; investigating the factors influencing students‟ acceptance of M-learning; and developing and evaluating a model for M-learning sustainable deployment. For phase one, a questionnaire was designed to identify students‟ readiness for M-learning, their expectations of how M-learning would work and their thoughts about the obstacles that might hinder M-learning. For phase two, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), a theoretical model was constructed to investigate students‟ acceptance for M-learning. For phase three, depending on the results obtained from first and second phases and the analysis of literature review of M-learning deployment, a model for sustainable M-learning with pre- and post-deployment stages was developed and evaluated by students and lecturers. A questionnaire was designed to identify the challenges facing the deployment of this technology in higher education and also to involve all the successful elements that need to be in place for M-learning deployment. The conceptual research framework is explained in Figure 1.1.
Outline of the Thesis
This thesis comprises seven chapters. This chapter (chapter one) introduces the research topic, and presents an overview of the research background, the aims and objectives of the study, its significance and the research approach. Chapter two reviews existing literature relevant to M-learning. It describes the relationship between E-learning and M-learning, with definitions and comparison. It provides a discussion of the motivation and benefits of M-learning in higher education, and its limitations and challenges. In addition, the literature reviewed includes M-learning implementation studies and students‟ readiness towards M-learning. Furthermore, the chapter provides the theoretical background of M-learning acceptance and discusses some studies related to M-learning acceptance. Finally, the factors that affect the deployment of M-learning and a comparison of four theoretical models relevant to M-learning deployment are demonstrated.
The third chapter provides details of the general methodology applied in this research. It describes and explains the research strategies, design and methods used in this thesis. For each research method a description of research instruments, participants, procedures and ethical concerns and data analysis are illustrated. The fourth chapter describes the first study undertaken in this research to explore students‟ readiness for M-learning, and their attitudes and expectations of the future of M-learning services. It details the research methods, participants, data collection instruments, procedure and data analysis. Finally, the results of the pilot and main study are reported and discussed followed by a summary of the chapter.
The fifth chapter explores the findings of the second study of this research, which aimed to investigate the factors the determine students‟ acceptance of M-learning in higher education. The chapter provides an overview of the research model, the research dimension and the hypotheses. In addition, the data collection, the profile of respondents and the statistical analysis to test the validity and reliability are presented. Finally discussion of the results hypotheses‟ testing and summary of the chapter are also included.The sixth chapter describes the research methodology utilized for developing a sustainable M-learning deployment model as well as the users‟ evaluation. The participants, procedure and data collection are discussed. In addition, the chapter presents the results obtained from two questionnaires (for lecturers and students). The refined model is also provided followed by a discussion and chapter summary. Finally, the seventh chapter presents a summary of the research findings obtained from chapters four, five and six. The chapter discusses the contribution to the knowledge in Mlearning subject that this thesis makes, and provides the limitations of the research with recommendations for future work.
This chapter summarises the outcomes of the research conducted to achieve the objectives of this PhD thesis. Research objectives and questions are presented followed by the contributions of the research. Discussion of the limitations and future work are also explained.
The main aim of this research work is to study and analyse the factors that affect the adoption and implementation of M-learning in the higher education environment in order to develop a sustainable M-learning model successfully. Specifically, the first objective of this study is to investigate the readiness of Brunel University students toward using mobile learning in their studies and to establish what factors might influence their readiness. The second objective is to determine the factors that influence university students‟ acceptance of M-learning in higher education environment. Finally, the third objective is to propose and develop a model with pre and post stages that can be used to foster the sustainable deployment of M-learning within teaching and learning strategies in higher education institute. Revisiting the study‟s objective, this study was undertaken to seek answers the following research questions:
RQ1a: What is the level of students‟ readiness for M-learning system?
RQ1b: What are students‟ expectations towards mobile learning services and the challenges that might affect the implementation of this new technology?
RQ2: What are the factors influencing students‟ acceptance towards M-learning in higher education?
RQ3a: What are the key issues and critical success factors that are essential to ensure successful deployment of M-learning?
RQ3b: How can the identified factors be worked (or considered) into the development of a sustainable M-learning model for higher education environment?
In order to respond to all of the research questions, literature and research in M-learning aspects were reviewed. From the literature, there is evidence that E-learning system has many advantages in higher education and has successfully used as vital platform of learning media in classroom and distance learning. With the spread of mobile internet and wireless technology, these tools could add value to E-learning system by extending the capability of E-learning to provide a flexible, portable and independent learning environment. M-learning can work on and off the campus, and help distance learning students to learn while they are outside the university. Previous literature clearly demonstrates that M-learning enhances university teaching and learning and will play significant role in the future of the higher education environment. However, it remains a new technology system. The adoption and implementation of M-learning in higher education instituions needs to be investigated carefully, regarding to the capability of universities, and the perceptions and acceptance of users. This research aimed to give insight in the area of M-learning adoption and implementation in higher education.
To answer the first question (a and b), a survey was utilized in chapter four to investigate students‟ readiness for M-learning, their expectations about M-learning services and what challenges they think will face the implementation of this technology. The study found that a big proportion of participants already had smart phones. However, some students thought that these devices might not be suitable to utilize M-learning, as M-learning needs technology to convert learning materials to specific mobile device systems. Moreover, the results of the survey show that students were not familiar with M-learning and they were not fully ready to implement this technology due to the issues of the infrastructure support and the compatibility in converting courses materials to the mobile devices system. Other issues identified by the students included whether the lecturers accept the adoption of M-learning. Lecturers‟ attitudes towards this new format, and their vision and skills, play a significant role in the successful implementation of M-learning. Students might get advantages of M-learning in the near future if a strategy is tailored to their readiness and that of their lecturers.
To answer the second questions, a hypothesized model depending on UTAUT was tested in chapter five using SEM. The results showed that a 55% intention to accept M-learning in the higher education context was explained by the proposed model, which incorporates two factors: quality of service and personal innovativeness to the components of UTAUT. All factors in the proposed model where found to have significant effects on behavioral intention to use M-learning (see Figure 7.1).
The findings indicated that in order to promote student acceptance of M-learning, Mlearning systems designers should pay attention to developing mobile applications and course content for M-learning which are easy to use easy, access and enhance students‟ performance expectancy. In addition, the quality of service offered needs to be user-friendly, meet all students‟ needs and be up-to-date, as this will attract more students to use M-learning. Furthermore, personal innovativeness has been found to be a strong factor which affects behavioural intention to use M-learning, as innovative students usually have more positive beliefs about using new technology. Additionally, some students might need to be motivated to adopt M-learning. Furthermore, lecturers and faculty members have a significant influence on students‟ acceptance of M-learning. They can promote students‟ acceptance of M-learning by adding value to their traditional methods of course delivery using this format. However, lecturers need to be familiar with M-learning (conceptually and practically), and be ready to be involved in the implementation plans. To answer the third question (a and b), a study was conducted as described in chapter six with the objective to create a model that can be used as a road map for both pre- and post-deployment stages of M-learning. The factors used to construct the initial conceptual model were derived from first and second studies in addition to the literature review. The model was evaluated by 148 undergraduate students and 28 M-learning experts. Both students and lecturers agreed with the model and suggested adding other factors that modify the initial model.
Contribution of the Research
This research and its findings have some contributions and significant implications to the area of M-learning acceptance and deployment. From the first study, the results contribute to the literature by assessing the readiness of students towards M-learning. From students‟ perspective, the results revealed the challenges that might face students in utilizing M-learning in their learning. The results gave insight in the students‟ expectations of the future of M-learning services. This directs M-learning scholars to devote more effort to adapting this technology in existing teaching and learning methods.
From the second study, with regard to the theoretical contribution, the study developed and assessed an acceptance model in M-learning context based on UTAUT. Empirically, the model evaluates the impacts of perceived usefulness, perceived ease of use, lecturer influence, quality of service and personal innovativeness on behavioral intention to use M-learning. The second study added to the theory of M-learning and technology acceptance, which in addition to UTAUT constructs incorporate other factors such as quality of service and personal innovativeness. All of these aspects need to be considered when designing and developing M-learning systems. The refined conceptual model (Figure 7.2) gives a wide overview of all elements that need to be addressed in an M-learning environment and fills the gap related to linking pre- and post-implementation phases to ensure successful sustainability. Furthermore, the results were obtained from both parts of the M-learning equation and represent the concerns and ideas of both students and lecturers.
This refined conceptual deployment model can work as a road map for future deployment of M-learning projects and help both management and practitioners to make decisions and ensure a seamless shift toward this new technology in higher education. However, in order to define the final shape of the model, the designed factors need to be revised once the model has been used in a real M-learning project. This conceptual model can give guidelines for where resources should be applied. Universities can use this model as a reference to build their IT decision and strategic plan. The findings of this research may motivate other researchers to conduct further studies to investigate and explore other factors that could influence the successful deployment of Mlearning in higher education environment. Furthermore, other researchers need to pay attention to develop solutions to defeat all hurdles facing the deployment of this new technology.
Limitations of the Research
The participants of this research were taken from one single public higher education institute (Brunel University, UK). Thus the results cannot be generalised to all higher education institutes, including private and open education.
The participants were students from one school (i.e. School of Information Systems, Computing and Mathematical Science). The results cannot be generalised for all university subjects. It might be presumed that students and faculty members at a technological/mathematical higher education institution might be more familiar with mobile and learning technologies than those from arts and humanities departments.
The research has been done in a University which does not implement M-learning system in its teaching and learning methods. This issue has affected the outcomes of this research. Students used their basic knowledge and perceptions about Mlearning to comment on the research questions.
As the M-learning system is not implemented in Brunel University, this study does not investigate the actual use of M-learning; it depended on prediction of the use.
The evaluation of the conceptual model was undertaken by students and lecturers. Other stakeholders like school management, technical support and M-learning designers were not involved in this evaluation. The questions in the second study were derived from the previous literature review in M-learning acceptance. However, some of these questions were leading and were designed positively.
This research utilized the quantitative data collection procedure with some open-ended questions; qualitative methods were not widely utilized in this research.
The conceptual model was created based on studies conducted in one higher educational institution in the UK. If other institutions attempted to use this model somewhere else (e.g. in developing or non-European countries), other factors might need to be considered (e.g. the techno-cultural milieu; the UK has well-developed internet and mobile infrastructure, combined with low uncertainty avoidance and markedly widespread use of online methods of communication).
Recommendations and Future Work
Overall, participants were willing to use M-learning. This compels researchers in the M-learning field to endeavor to adapt this technology in teaching and learning methods. We recommend that more technical infrastructure should be put in university campuses to assist students‟ learning via their mobile devices. It would also be advisable to provide students and lecturers with more information about the benefits of M-learning using workshops and seminars. Moreover, a series of training courses should be organized for lecturers in order to familiarize and integrate them with M-learning administration.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:TOWARD MOBILE LEARNING DEPLOYMENT IN HIGHER EDUCATION
TOWARD MOBILE LEARNING DEPLOYMENT IN HIGHER EDUCATION
Brunel University, School of Information Systems, Computing and Mathematics
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Dept of Computer Science Theses
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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.