Acceptance on Mobile Learning via SMS: A Rasch Model Analysis (original) (raw)
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Abstract: M-learning is a new education channel that universally assists people in acquiring knowledge and skill via the use of mobile technologies. This study attempts to create a theoretical model, in which student acceptance of Mobile learning implementation in the three Islamic universities in Malaysia are explained and predicted. The model expands the belief concept in Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) by including one more constructs namely, service quality. Title: Factors That Influence On the Acceptance of Mobile Learning Services in the Institutes of Higher Education in Malaysia Author: Mohammad Mahmoud Saleem Alzu’bi, Mohd Zalisham Bin Jail, Qais Faryadi International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Research Publish Journals
The 5th Pre-University Conference, 2018
Mobile phone has been pervasive and nifty to learners. The attractive features of the mobile phone could embolden learners to use it for learning. Thus, this quantitative research utilised the Unifed Theory of Acceptance and Use of Technology (UTAUT) as a theoretical basis to conduct a descriptive research to test the factors that influence students’ acceptance and use of mobile learning at Sunway College Johor Bahru. Performance expectancy, effort expectancy, facilitating conditions and social influence were the moderate determinants of mobile acceptance. Although social influence was signifcant to this study, it was less influencing; due to the lack of encouragement and support from the lecturers. Further research should focus on students’ acceptance of mobile technology with increasing usage and experience in learning by utilising longitudinal studies. Since learners show a high intention of utilising mobile phone for learning, the stakeholder should encourage the move.
Mobile Learning Services Acceptance Model among Higher Education Students
2013
Mobile learning (m-learning) is considered the next form of e-learning using mobile technologies to facilitate education for teachers and learners anywhere and anytime. Engaging the m-learning services in the higher education could improve the availability of education. This study aims to develop a theoretical model for explaining and predicting student acceptance and use of m-learning services in the higher education environment. Students’ perspective is very important to investigate the use behavior of m-learning in the higher education environment. Findings of the study suggest that the behavior intention to use the m-learning by students in the higher education environment have positive influence on the use behavior. Consequently, the availability of facilitating conditions is an important to influence students’ use behavior. The study suggests several factors as important determinants of the behavior intention to use the m-learning in the higher education environment. Specifically, behavior intension to use appears to be adopted and facilitated by the usefulness of m-learning services, so more usefulness of m-learning leads to more acceptances among students in the higher education. Besides, the perceived service quality is important role in determining the level of behavior intention to use.
Mobile Learning Technology Acceptance of Students from Universities of Education
Journal of Research and Innovation, 2020
The aim of this research is to construct a model for predicting actual acceptance of mobile learning technology among students in universities of education in Myanmar. The specific objectives are to examine the effect of learning expectancy, effort expectancy, social influence, facilitating conditions, mobile learning technology characteristics and self-management of learning on behavioral intention to mobile learning, and to examine the effect of facilitating conditions and behavioral intention on the actual use behavior of mobile learning technology. A total of 412 students from two universities of education participated in this study in August 2020. Based on Unified Theory of Acceptance and Use of Techhnology, Alasmari (2017)'s Mobile Learning Technology Acceptance Inventory was used to measure the participants' mobile learning technology acceptance. The objectives were executed by using structural equation modelling technique in R studio. According to the result, only learning expectancy, effort expectancy, facilitating conditions and mobile learning characteristics are significant predictors of behavioral intention to mobile learning, explaining 62.4 % of variance, while social support and self-management of learning show insignificant effect on it. And, facilitating conditions and behavioral intention can predict significantly the actual use of mobile learning technology, explaining about 8.2 % of variance. These findings were not perfectly in line with the proposed UTAUT model, indicating effort expectancy as a negative predictor and social influence as an insignificant predictor of behavioral intention to mobile learning.
M-learning is not only e-learning with handheld devices. M-learning creates new learning channel in which students can access content just in time information required at the right time and right place. Despite the fact that mlearning provides mobility and instant access to students, there are some implementation challenges and issues in transition from e-learning to m-learning. Mobile learning is still in its infancy, identifying the factors influencing the adoption of this technology is an essential issue. Researchers and developers in education sphere should consider these mobile capabilities and challenges before developing m-learning content. Students play the most important role in determining the success or failure of the systems. The students adopt or reject a new technology is an importance and complexity case. Moreover, there are numerous models and theories have been conducted for a better understanding of students-adoption, especially in the educational context. Technology Acceptance Model (TAM) is one of the best and well-known adoption models which can be used to interpret the adoption of new technologies. In order to find the factors that influence on m-learning adoption, in this study will adopt TAM model as a theoretical framework and extending this model with two external variables to propose new model. A questionnaire survey will be adopted based to collect required data. The results of data analysis will guide this study to find which of the following independent variables (Mobile Readiness, Perceived Interaction, Easy To Use, Usefulness, Attitude to Use) has a more significant effect on dependent variable (the Influence On Mlearning Adoption).Finally, the results will provide valuable implications for ways to increase college students' acceptance of mobile learning.
The rapid growth of mobile technology pushed educational institutions to adopt mobile learning (m-learning). It has the potential to allow students to more closely integrate with learning activities in their lives. However, its adoption and level of use is low in Thailand compared to other countries. This study aims to identify factors that affect the adoption of mlearning in Thailand. The Technology Acceptance Model (TAM) is used as theoretical framework towards understanding the adoption of m-learning. The quantitative method was applied to collect primary data from 200 Ramkhamhaeng University (RU) students who use m-learning. The findings reveal that only three factors contributed significantly towards adoption of m-learning among RU students. The factors were Thai Social Influence, Student Readiness, and Quality of Content. Thai Social Influence was found to be the most influential factor in the model. On the other hand, factors such as Perceived Usefulness, Perceived Ease of Use, and Network Accessibility were found to insignificantly contribute predictors on mlearning adoption. This research provides useful information in the understanding students' acceptance of m-learning in RU. Moreover, it also gives perspectives to RU and other institutions who want to integrate m-learning system into their curricula.
Investigating the determinants of mobile learning acceptance in higher education in Saudi Arabia
2016
Higher education appears to be changing in Saudi Arabia, which has made considerable progress in the adoption of more student-centered learning approaches as a reaction to the global pedagogical shift. Saudi Arabia has prioritized instructional technology integration in its educational system. The proliferation and popularity of mobile handheld devices, particularly among young students, will significantly make the future of Mobile learning, or M-learning in higher education bright and promising. However, M-learning in higher education is still in its embryonic stage of implementation, especially in developing countries such as Saudi Arabia; therefore, an in-depth study of each aspect of this issue is necessary. To ensure the success of Mlearning in higher education, it is crucial to examine the students' intention to use M-learning as the first step in the process of implementing it into higher education. A quantitative, non-experimental survey research design and descriptive research were conducted on the determinants-performance expectancy, effort expectancy, and social influence-that predict undergraduate Saudi students' intention at King Khalid University (KKU) to use M-learning, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) as the framework. Data were collected by means of a selfadministered online questionnaire. The hypothesized model was validated empirically using data collected from 1,207 undergraduate students at KKU, Saudi Arabia. A multiple linear regression was administered to test the proposed hypothesis. The proposed model was supported and explained up to 52% of the variance in behavioral intention to use M-learning. The results indicate that performance expectancy, effort expectancy, and social influence were all statistically significant predictors of behavioral intention to use M-learning. Despite the great potential of mobile handheld devices to provide students and institutions with many benefits, such as study aids, accessibility to information, and universal communication, students may be constrained by limited or no internet connectivity, limited screen size, short battery life, and low memory, all of which may dampen student interest in using M-learning. Therefore , these obstacles need to be solved for the betterment of M-learning.This study contributes to the literature on M-learning by identifying determinants that predict student's behavioral intention to adopt and use M-learning and also confirms further Venkatesh et al.'s (2003) UTAUT model as a valid model in studying technology acceptance and use. Based on the results of this study, recommendations were made for instructional practice and future research to implement Mlearning for academic purposes.
Telematics and Informatics, 2018
This paper analyzed the adoption of using mobile learning (m-learning) in higher education. Mobile Learning as a model of e-learning refers to the acquisition of knowledge, skills and attitudes by utilizing mobile technologies. With the increasing coverage of mobile networks, learning services can play the increasing and effective role in education at any time and place. Continuous access via mobile devices creates special facilities like sending and saving the learning content to learners etc, it is accompanied with continuous education. This paper, with a study of "what is the impact of the mobile phone usage in education?" provides approaches and theories of mobile learning in training. The paper aimed to evaluate the essential factors for the adoption and application of education information system that has been created by students. The survey was based on the history of technology adoption and covered students. Furthermore, a case study from students of K. N. Toosi University in Iran was presented to indicate the performance of this method in practice. The statistical society included 300 members from Information Technology students of Iran's K. N. Toosi University of Technology. The factors related to adoption of mobile learning in higher education was classified into seven main categories as: ease of use, trust, characters and personal qualities, context, perceived usefulness of using, behavioral intention, and culture of using a research model. From the results, mobile learning could be one of the promising educational technologies for development in educational environments and culture of using. The context of applications has a significant positive effect on the ease of use factor and usefulness and the ease of use has a positive effect on the usefulness factor. The trust factor has a positive and significant effect on the behavioral intention. The personal features and characters factor have a significant positive effect on the culture of using and the culture of using the application has a significant positive effect on the behavioral intention.
As many Korean universities have recommended the implementation of mobile learning (m-learning) for various reasons, the number of such tertiary learning opportunities has steadily grown. However, little research has investigated the factors affecting university students' adoption and use of m-learning. A sample of 288 Konkuk university students participated in the research.The process by which students adopt m-learning was explained using structural equation modeling technique and the Linear Structural Relationship (LISREL) program.The general structural model based on the technology acceptance model included m-learning self-efficacy, relevance for students' major (MR), system accessibility, subjective norm (SN), perceived usefulness, perceived ease of use, attitude (AT), and behav-ioral intention to use m-learning.The study results confirmed the acceptability of the model to explain students' acceptance of m-learning. M-learning AT was the most important construct in explaining the causal process in the model, followed by students' MR and SN.