Elaheh Yadegaridehkordi

Influences of Demographic Information as Moderating Factors in Adoption of M-Learning

In todays world, interests of mobile devices for educational processes anytime and anywhere has been on the rise. However, adoption of this new technology by students is complicated. The purpose of this study is to examine the factors that influence the adoption of M-learning by students and to propose an appropriate model for its adoption. Three external variables, namely Perceived Mobility Value, Prior Use of Electronic Learning and Self-efficacy, were incorporated into the Technology Acceptance Model and tested in Universiti Teknologi Malaysia. Quantitative research approach was used to survey 350 students. Empirical data from multiple regression analyses indicates that Perceived Usefulness, Perceived Ease of use, Perceived Mobility Value, Prior Use of Electronic Learning, Self-efficacy, and Attitude toward using, can positively affect the adoption of M-learning. Results are explored further in this study

Customers perspectives on adoption of cloud computing in banking sector

The advent of cloud computing has transformed the role of the Internet in many businesses and organizations. Currently, banks are increasingly adopting cloud technologies to fulfil their varied purposes and to create a flexible and agile banking environment that can quickly respond to new business needs. However, past studies tend to focus more on the adoption issues of cloud computing from the organizational perspective with little attention paid on the users’ view of these cloud-based services. Therefore, this paper attempts to investigate the factors influencing cloud computing adoption in the banking sector from the customers’ perspective and to propose an adoption model for this purpose. The model is mainly developed based on the TAM-diffusion theory model (TAM-DTM) with the introduction of three new constructs namely trust, cost, and security and privacy. Questionnaires were randomly distributed to 162 bank customers in Malaysia. Survey data were analyzed using the partial least squares (PLS) method while SmartPLS was used to test the hypotheses and to validate the proposed model. The results suggest that trust, cost, and security and privacy can be successfully integrated within the TAM-TDM. The security and privacy constructs exhibited strong positive influence on perceived ease of use, perceived usefulness, and trust. The study concludes that perceived usefulness, perceived ease of use, cost, attitudes toward cloud and trust significantly influence users’ behavioral intention to adopt cloud computing. Thus, the finding of this study will enable banks to focus more on customer perspectives on cloud-based applications and identify their attitude towards their adoption.

Success factors influencing the adoption of M-learning

In today’s educational world delivering teaching and learning materials anywhere/any time has became a major focus of interest. Mobile learning is a new pedagogy/technology that breaks the limitations of classrooms and shifts educational processes from lecturer-centred to learner-centred without any limitations. As mobile learning is in its infancy, identifying success factors influencing the adoption of this pedagogy/technology is very important. The present study aims to determine the factors contributing to the adoption of M-learning. Three external variables (perceived mobility value, self-efficacy and prior use of electronic learning) were added to technology acceptance model and tested in Universiti Teknologi Malaysia. Quantitative research approach was conducted to collect required data. The sample was composed of 350 undergraduate and postgraduate students of Universiti Teknologi Malaysia who were selected by stratified sampling to answer questionnaires. Finally, the results of data analysis indicate that perceived mobility value is a more significant factor while other factors – perceived ease of use, perceived usefulness, attitude toward using, self-efficacy, prior use of e-learning – are also accepted as contributing factors for mobile learning adoption. In addition, some recommendations are given to act on these factors and to accelerate the process of M-learning adoption in higher education institutions.

Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches.

Abstract With the emergence of cloud-based technology, personalized learning mechanism has increasingly become a fundamental requirement for most learning systems. This study aimed to identify the key factors that influence user adoption of cloud-based collaborative learning technology in the educational context. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), personalization construct was linked to the behavioral intention, performance expectancy and effort expectancy. This research applied a new methodological approach combining both Fuzzy Analytic Hierarchy Process (FAHP) and Structural Equation Modelling (SEM) to determine the relative weight and importance of the factors as well as to test the proposed hypotheses in the research model. Using a survey questionnaire, data was collected from 150 students of four Malaysian public universities. The findings of FAHP demonstrated that performance expectancy, social influence, and personalization were the most important factors predicting behavioral intention to adopt cloud-based collaborative learning technology from experts’ point of view. The results of the SEM showed that users’ behavioral intention was significantly influenced by performance expectancy, effort expectancy, social influence and personalization. Although, personalization performed a direct influence on behavioral intention, its indirect influence through performance expectancy and effort expectancy was also considerable. This study and its findings can serve as a baseline by which cloud service providers, ministry of education, and educational institutions can make strategic and strong decisions about adoption of cloud-based technology in educational environments.

Mobile Learning for English Language Acquisition: Taxonomy, Challenges, and Recommendations

Mobile learning (m-learning) is increasingly becoming a popular global trend, especially among English language learners. However, despite the growing interest in mobile English language learning, there have been no reviews of research conducted on this subject. This paper represents the first attempt to provide a comprehensive analysis of the existing literature (2010–2015) to identify the taxonomy and distribution of research as well as to identify the advantages and challenges and provide some recommendations to facilitate the effective use of mobile English language learning and its applications. Following a review protocol, articles on mobile English language learning from six major databases (IEEE Xplore, ScienceDirect, Web of Science, ERIC, SpringerLink, and Wiley Online Library) were reviewed. Applying inclusion and exclusion criteria, 133 related articles were identified. The results show that the majority of studies were conducted on application m-learning technologies. Pure mobile applications were the most widely-used applications in the English m-learning context. Meanwhile, concerns regarding quality, usability, integration, financial costs, security and privacy, pedagogical practice, and safety were found to be the main challenges of mobile English language learning. Finally, some recommendations are provided for users, developers/providers, and researchers. The results of this paper can assist users, researchers, policymakers, and practitioners in the education sector to allocate the necessary resources and make plans to mitigate the challenges and facilitate the effective use of mobile English language learning in educational practices.

User perceptions of the technology characteristics in a cloud-based collaborative learning environment: a qualitative study

The purpose of this study was to assess user perceptions of technology characteristics, which is a complicated construct in task technology fit model, in a cloud-based collaborative learning environment. For this purpose, cloud computing characteristics cited in the previous related research, were categorised into cost saving, ease of implementation, flexibility, mobility, scalability, sustainability, personalization, processing capabilities, agility, collaboration, usability, risk reduction, measured service, on demand self-service, and resource pooling. Interviews were then conducted with students who had some experience in using cloud-based applications for collaborative learning. Directed content analysis was performed using ATLAS.ti software to organise the coding process. The results of coding data showed that collaboration, mobility, and personalisation, which resulted from previous related literature, are also cited by a large number of participants in interviews as being significant characteristics of cloud-based collaborative learning applications. Organisational cost saving, ease of implementation, flexibility elasticity, scalability, sustainability, processing capabilities, agility, usability, risk reduction, measured service, on demand self-service, and resource pooling were not mentioned by any of the participants at all. However, easy monitoring and assessment, time control and saving, cost saving, accessibility, ease of use, and easy connection to other applications were new themes that emerged inductively during data analysis.

Users' perceptions towards M-learning adoption: An initial study

Recently, the quick spread of mobile computing and communication leads us to this point that it is an opportune time to integrate Mobile learning (M-learning) into educational concepts. There has been a great interest towards the use of mobile devices to deliver learning materials, and for students to learn anytime and anywhere. With regard to flexibility, low cost, and new features of mobile devices, students and lecturers are willing to integrate M-learning into their educational processes. Thus, this study is conducted on a group of students and lecturers in Universiti Teknologi Malaysia (UTM) with the aim of obtaining their perceptions and issues related to the adoption of M-learning in teaching and learning activities. This study uses the survey method to achieve the aims. Overall, findings show that the interest of users towards adoption of M-learning for their teaching and learning processes is very high. Easy and convenient access to learning material is the main benefit; and concern about the lack of ability to use and organize M-learning is the main barrier for this adoption. Besides, SMS is selected as more preferable application for M-learning activities.

Mobile English Language Learning (MELL): a literature review

Abstract English has increasingly become an essential second language as well as a language for international communication. However, there is little research that examines the dimensions of mobile learning for both researchers and instructional designers and focuses on effective uses of the latest mobile learning technologies for education. There have been no reviews of research on mobile English learning. This paper aims to provide a comprehensive analysis of the research on Mobile English Language Learning (MELL) material to initiate an evidence-based discussion on the usage of mobile learning in English language education. Findings from existing literature show that studying and reviewing mobile learning leads to a deeper understanding of its effect and possibilities with respect to learning the English language. Additionally, findings also indicate that when it comes to English language skills, vocabulary is the most-used skill, and the most common problem that studies mention is that of motivation. Further studies need to investigate other terms and keywords that reflect on the use of mobile learning.

Investigating the dynamic relationships among the indicators of the Community of Inquiry model in blended learning

The dynamic relationships among teaching presence, cognitive presence, and social presence in Community Of Inquiry (COI) model in blended learning are discussed in this paper. This research offers an opportunity to find out the relationships among presences in COI model in a blended learning. The respondents of this research use Course Management System (CMS) and social network in addition to face to face learning. The researchers used a set of questionnaire to find the relationships among three presences based on the COI model. Through statistical analysis using path analysis by multiple regression analysis teaching presence has a significance impact on sustaining social and cognitive presence in the case study.

Travelers decision making using online review in social network sites: A case on TripAdvisor

Abstract Digital technology and social media have brought numerous benefits to human society. TripAdvisor, which runs on user-generated content, provides a platform for travelers to socialize their opinions on several aspects of hotels. Recommendation agents have played an important role for hotel recommendations in the tourism domain. They are valuable tools in e-tourism platforms of travel agencies to help the users in their decision-making process. The recommendation of hotels by multi-criteria Collaborative Filtering (CF) recommender systems is mainly based on their past reviews on several aspects. Hence, recommending the most appropriate hotel to the user is one of the important tasks that a multi-criteria CF needs to do in the e-tourism platform. The aim of this research is to use the multi-criteria ratings in developing a new recommendation method for hotel recommendations in e-tourism platforms. We use supervised and unsupervised machine learning techniques to analysis the customers’ online reviews. The method is evaluated on the data provided by the travelers via TripAdvisor mobile application. The results of our analysis on the dataset confirm that the use of online reviews in the proposed recommendation agent leads to precise recommendations in TripAdvisor.