Adoption Of M-learning Research Articles

Investigation of the awareness of rural students on M-learning using smartphones

The outbreak of the COVID-19 pandemic has led to a sudden transition from face to face to online teaching and learning practices in various parts of the world. Meanwhile, the level of awareness and adoption of mobile learning (M-learning) by means of smartphones in many rural parts of Africa tends to be considered low. The reasons for this include various factors, such as poor access to internet facilities, a lack of funds, the cost of smartphones, amongst others. This study investigates the level of awareness of South African rural higher institution students (SARHISs) on M-learning using smartphones. A quantitative method was adopted for the study. Convenience sampling was used to select the institution and the 75 respondents who took part in the study. Data were collected by means of a questionnaire, entitled “Smartphone questionnaire (SQ)”. The collected data were analysed using the Statistical Package for the Social Sciences (SPSS), Version 25. The findings of the study show that the adoption of M-learning using smartphones by the respondents who are students from the South African Rural Higher Institution (SARHIS) is moderate – a little above the average. Based on the study, it is recommended, that students’ awareness should be raised and that M-learning should be supported given the recent transition from onsite to online teaching and learning practices

Unified theory of acceptance and use of technology (UTAUT) in mobile learning adoption : Systematic literature review and bibliometric analysis

Various literature studies have been conducted to provide valuable information regarding the current research trend of Unified Technology Acceptance and Use Theory (UTAUT). When the literature was examined, it was seen that the UTAUT research on the adoption of mobile learning (M-learning) was ignored. Therefore, it was deemed necessary to conduct a literature study on the adoption of mobile learning. In this context, 31 research articles on the adoption of M-learning with UTAUT, published from 2003 to 2020, have been discussed for systematic literature research. These 31 specific research publications were discussed under four categories Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. 63 different factors were identified after systematic literature review, except for UTAUT factors. These factors were grouped under 10 main factors. In addition, the authors in this field were identified by bibliometric analysis and the relationships between each other were determined by citation analysis. In addition to these, prominent terms have been determined according to the keywords and abstracts in the relevant articles. The connections between these terms have been created by the method of co-occurrence. Finally, the links between prominent terms and terms were examined with bibliometric analysis. According to the findings obtained, it has been determined that most UTAUT studies involving M-learning focus on extending UTAUT with external variables. It has been observed that the analyzed studies generally took place in the Asian countries. These studies have been carried out as multidisciplinary. In addition, it has been reported that most of these studies on M-learning take place in higher education settings. It is thought that the findings obtained at the end of the systematic literature review and bibliometric analysis study on the adoption of M-learning with UTAUT will constitute an important reference for academicians in this field.

Saudi Higher Education Student Acceptance of Mobile Learning

This research defines the mobile learning (m-learning) acceptance and outlines the critical factors affecting m-learning adoption among higher education students in Saudi universities. This research employed qualitative and quantitative approaches with 683 randomly sampled student participants from public and private universities. This research used questionnaire for collecting data, and the research framework was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The model was used to outline the factors affecting student intention to use m-learning. The output of statistical analysis showed high student acceptance of m-learning.

Technology Based Model of a Mobile Knowledge as a Service to Facilitate Education Community

Technological enhancements as well as the demand of students to access learning information on time and quickly has resulted to the development of e-learning across the world. Mobile learning has been adopted by most learning institution as a mobile technology that allows learners to access learning materials and share information among themselves and respective educators easily and quickly. This article discusses the intention of users in learning environment adopting m-learning, their perceptions as well as factors that hinder implementation of m-learning in the gulf region. Effective mobile technology adoption also enhances knowledge management through mobile applications that allow information capture, storage, retrieval and sharing. This study uses systematic literature review to collect information from post 2017 studies previous conducted by other researchers. Articles were searched through highly ranked databases from which 657 journals were identified. After the screening and eligibility assessment, 24 journals were retrieved. The back and forward search retrieved 4 more journals bringing the total to 28 journals that were included in the study. Based on this information, a conceptual model is developed to help assess the m-learning perceptions, adoption intentions and factors influencing its adoption among learning institutions in the gulf region. This model is built based on the Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model, main constructs such as performance expectancy, effort expectancy and social influence are greatly impacted by other factors like learner’s creativity and mobility.

Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model

The goal of this study was to develop a new model and conduct confirmatory factor analysis to learn more about how students use M-learning in higher education. The study is theoretically based on the unified theory of acceptance and use of technology (UTAUT) theory and the technology acceptance model (TAM). Theoretically, the factors related to the adoption of M-learning in higher education, identified as contributory to perceived ease of use, perceived usefulness, and attitudes towards M-learning and actual use of M-learning, were analyzed. A questionnaire survey was distributed to 362 university students who were randomly selected. Structural Equation Modeling (SEM)-AMOS was used for data analysis. Based on the findings, M-learning appears to be one of the most promising educational technologies for development in educational environments. Perceived facilitating conditions, performance expectancy, effort expectancy, social influence, and perceived enjoyment have a significant positive effect on the perceived ease of use and perceived usefulness, while performance expectancy has a negative effect on the perceived ease of use. Perceived ease of use and perceived usefulness have a positive and significant effect on attitudes towards using M-learning and actual use of M-learning. Therefore, we recommend lecturers encourage students to utilize M-learning for educational purposes in higher education.

Mobile Learning in Business English its Effect to South American Students’ Learning Styles in the COVID 19 Pandemic Era: Its Economic Implications

The adoption of M-learning and perceptual learning style preferences of South American EFL learners in a business English course were studied empirically. A descriptive correlational research design was used in this study. Data were gathered from 125 South American EFL students who were carefully sampled among a total of 184 students with mobile phones enrolled in a business English classes of selected South American university. The Mobile Learning Questionnaire (MLQ) and Perceptual Learning Style Preference Questionnaire were used in the study (PLSPQ). The data revealed that students in a business English course have a high level of M-learning adoption. It was discovered that respondents favor visual learning, group learning, individual learning, tactile learning, and auditory learning, with kinesthetic learning receiving the least attention. When students are grouped by gender and age, test results show that male respondents had stronger appeal to the utility, acceptability, enjoyment, facilitating conditions, and behavioral intention of M-learning than female respondents. Regardless of their age, respondents acknowledged the benefits of using M-learning in their business English course. Similarly, when the respondents are grouped by gender, no significant differences in learning style preferences are detected. However, the younger respondents prefer group learning, whilst the older respondents prefer individual learning. Finally, a link was discovered between M-learning usage and students' preferred learning styles. The positive link implies that the more M-learning students use, the more learning style preferences they have for group, tactile, visual, auditory, kinesthetic, and group learning tasks. This paper discusses the theoretical and educational consequences.

Determinants of mobile learning adoption: extending the unified theory of acceptance and use of technology (UTAUT)

PurposeAlthough several different learning technologies have been integrated into the face-to-face (F2F) learning approach, the effective implementation of mobile learning (m-learning) is still at an early stage. This may be due to the lack of understanding factors that affect learners' acceptance of m-learning.Design/methodology/approachConsidering the unified theory of acceptance and use of technology (UTAUT) as a theoretical background, this research investigates m-learning adoption in Saudi Arabia. The research framework extends the UTAUT by including intrinsic motivation, mobile learning self-efficacy and perceived satisfaction. A total of 200 higher education students voluntarily participated in the research. The application of the partial least square technique indicated that the proposed model can predict m-learning adoption adequately in Saudi Arabia (R2 = 0.73.8).FindingsThe research outcomes are significant for educational institutions and teachers to implement m-learning effectively. Many recommendations can be suggested to help enhance learners' willingness to adopt m-learning technology: Teachers need to design their courses in an interactive way and provide several different activities to ensure that learners obtain real benefits in their learning outcomes. When learners have positive perceptions about the use of this technology in the learning process, such perceptions should be supported by providing beneficial courses.Originality/valueThis research examines the effectiveness of the extended UTAUT to predict m-learning adoption in Saudi Arabia higher education. This could provide a significant contribution to the existing evidence about the validity of the model in different learning cultures and contexts.

Investigating Educators' Intention to Adopt M-Learning

The technological spread has brought business schools in Arab countries into the m-learning age. Teachers represent one of the most important pillars of the ubiquitous learning implementation. This research aims to examine educators' intention of m-learning adoption based on the TAM, dispositional resistance to change (RTC), and perceived playfulness. One hundred seventy-nine educators from business schools in Tunisia and Saudi Arabia answered the survey. Results revealed that playfulness is the most predictor of the educators' intention adoption of m-learning in both cultures. Cluster analysis has revealed three different profiles of educators in business schools: opposing, averse, and pioneers.

Mobile learning acceptance in social distancing during the COVID-19 outbreak: The mediation effect of hedonic motivation.

M‐learning is a trending field in educational organizations, companies, and also for individual study. However, in some regions the ampleness of the phenomenon is not quantifiable or comparable due to the lack of an adequate framework and reliable metrics. Our research intends to make a little light by assessing the degree of m‐learning adoption in students at a moment when face‐to‐face education moved suddenly online due to the COVID‐19 outbreak's rapid and unpredictable spread. A new model relying on the Unified Theory of Acceptance and Use of Technology (UTAUT) was built to investigate and explain relationships between constructs. It reveals the key factors affecting technology adoption by considering hedonic motivation a mediator instead of an exogenous variable as in UTAUT2. Based on an analysis of 311 higher education learners, the way how performance expectancy, effort expectancy, social influence, and facilitating conditions influence directly or indirectly the behavioral intention is researched. The analysis was conducted employing partial least squares structural equation modeling. The strongest relationship is between hedonic motivation and behavioral intention followed by the one between perceived effectiveness and hedonic motivation. Age, gender, and experience moderate the model's relationships. Research contributes to theory development by successfully adjusting the original UTAUT model. Results indicate that universities may offer learners an enjoyable m‐learning experience by activating social support groups and inserting gameplay elements into the learning system.

Enhancing Student Involvement Based on Adoption Mobile Learning Innovation as Interactive Multimedia

The low involvement of students in online learning resulted in a decrease in their academic performance. This research aims to figure out the effect of adopting online learning innovations such as mobile learning (m-learning) on students’ involvement with digital readiness as a mediator. A total of 89 graduate students from all over Indonesia enrolled at Universitas Negeri Yogyakarta participated in the study. To determine the sample, we used purposive sampling. Analysis techniques to test research models used partial least square structural equation model (PLS-SEM) with SmartPLS 3.3.2 software. The results are significant for digital readiness as a mediator in student perception of the influence of the adoption of m-learning innovation on student engagement. Although students positively perceive that the adoption of m-learning has a positive effect, they must also have strong digital skills to complete their academic work. Besides, they must also be committed to fully engaged in learning activities using m-learning— this finding provides practical implications for improving effective and interactive online learning in college.

Mobile Learning Adoption: A Systematic Review of the Technology Acceptance Model from 2017 to 2020

Numerous review studies were conducted in the past to understand the applicability of the technology acceptance model (TAM) in m-learning context by examining several issues. Although each of those studies provided a valuable synthesis of TAM, further issues are still uncovered and call for further research. Therefore, this research aims to systematically review the existing TAM-based m-learning studies through the analysis of various issues, including the factors affecting the m-learning adoption, research methods, TAM progress over publication years, online databases, active countries, and sample size. Out of 458 articles collected, a total of 64 studies published between 2017 and 2020 were critically analyzed. The main results indicated that self-efficacy is the most frequent factor affecting the m-learning adoption, followed by subjective norm, enjoyment, mobile anxiety, facilitating conditions, social influence, innovativeness, and satisfaction, respectively. Additionally, most of the analyzed studies have relied on questionnaire surveys in collecting their empirical data. Although it was developed in 1989, the results showed that the number of TAM-based m-learning studies is increasing year by year, which in turn, increases the credibility of the model in explaining the users’ intentions towards technology adoption. We have also discussed the contributions of this systematic review and the implications that it could yield for future attempts.

Predicting mobile learning acceptance: An integrated model and empirical study based on higher education students' perceptions

This research extends the Unified Theory of Acceptance and Use of Technology (UTAUT) model based on expectation-confirmation and self-determination theories. It aims at exploring students' perspectives regarding the acceptance of mobile learning (m-Learning) in higher education. Although UTAUT receives considerable attention in technology acceptance research, this study unlike previous work as it is among the first to integrate self-determination and expectation-confirmation theories with this model in understanding m-Learning adoption, particularly in a case of developing countries. A total of 246 undergraduate students responded voluntarily to an online questionnaire. The findings suggest that integrating the UTAUT model with variables represent learners' basic psychological needs can highly affect their acceptance of m-Learning technology. The findings are further being discussed for theoretical and practical implications.

Developing an educational framework for using mobile learning during the era of COVID-19

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally.

Investigating of Mobile Learning Technology Acceptance in Companies

Technology plays a prominent role enhancing and innovating the quality of work nowadays. Currently, the world is witnessing a new wave of industrial revolution driven by the development and diffusion of digital technology; for instance, the Internet of Things (IoT) is being used, adopted, and incorporated into people’s everyday lives. Mobile learning, as one of the modern innovations, affects employee learning in the corporate world. Tools like Course Mill® have critical significance due to its various benefits, such as enhance personal and convenience learning as information sharing, promote cooperation and collaboration in learning increasing management, bridge digital divide, and enhance job performance. However, numerous limitations of adopting mobile technologies were identified, such as high cost of implementation, unreliable broadband wireless connection, and shortcomings in mobile devices features like the small screen size. The study utilized both the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology model (UTAUT) to develop a research model to be used in the study. The multivariate exploratory factor analysis identified four main barriers that affect M-learning adoption, which is perceived usefulness (PU), perceived ease of use(PEU), social influences (SI), and facilitating conditions (FC).These factors have a significant positive influence on the acceptance and actual use of M-learning technology in companies.

Factors Affecting Mobile Learning Acceptance in Higher Education: An Empirical Study

The use of mobile tools to support learning and teaching activities has become a significant part of the informal learning process. Mobile learning (M-learning) is used to considerably develop the forms of learning activities made by learners, and support the learning process. The effective application of M-learning in higher educational institutions, however, is based on the learners’ adoption. It is therefore essential to define and investigate the factors affecting the desire of learners to use and adopt M-learning. Thus, this research investigates the factors affecting students’ intention to adopt M-learning in institutions of higher education. To achieve the objectives of this research, a model is proposed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology Acceptance Model (TAM). The instrument is developed using validated items from previous studies and shreds of literature. Data for this quantitative study are collected from undergraduate and postgraduate students. A Structural Equation Model (SEM) is used to analyze the data collected from 218 participants using a survey questionnaire. The findings show that students’ intention to adopt M-learning is shaped by various variables consisting of personnel innovativeness, self-management, facilitating conditions, social influence, relative advantage, and effort expectancy. The research results also present several practical contributions and implications for M-learning adoption in terms of research and practice. Investigation of the required determinants may contribute to gain learners’ adoption and is important to enhance the learning experience of students and help them improve their knowledge and academic achievement. The contribution of this paper lies in defining the factors influencing the acceptance and use of M-learning systems by students of higher education in Palestine. Hopefully, the results of the study are valuable for policy-makers in designing comprehensive M-learning systems.

Model for Considering the Propensity of Students to Accept M-learning

Research question: The aim of the study is to examine the inclination of engineering students in Serbia to adopt M-learning. Motivation: Mobile technologies provide new solutions in the current concept of learning. Students today have a new approach to learning since they live in the era of digital technology. The use of mobile technology in higher education came along with the technological enlightenment of professors and students, eradicating geographical boundaries and enabling cooperative learning. Idea: Mobile learning (M-learning) is unthinkable without the use of mobile devices and it plays an increasingly important role in the development of teaching methods in higher education. The research refers to the analysis and assessment of M-learning in higher education with the aim to identify problems and weaknesses related to the application of M-learning in order to set alternatives and criteria for the proposed model. Data: The survey was conducted through a questionnaire, and 341 correctly filled out questionnaires were collected. The obtained data were processed by the Visual Promethee software package. Tools: The research methodology was based on the Multi-criteria decision analysis (MCDA) method which was used in the form of PROMETHEE II/GAIA technique. In order to rank the student programmes from different study areas, groups of questions were used as criteria, and study programmes were used as alternatives. Findings: The acquired results indicated that M-learning was mostly used by the students of Engineering Management, since they frequently used mobile technology in certain required courses, as well as for obligatory business practice. It was also concluded that the study programme Metallurgical Engineering had the lowest performance concerning the implementation of M-learning among all other observed study programmes. Contribution: The conclusions of this study can add to the awareness-raising of the higher education institutions' interested parties and alert them to improve the quality of M-education by pointing out that there are differences in the adoption of M-learning depending on the type of engineering sciences.

USING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) MODEL TO DETERMINE FACTORS AFFECTING MOBILE LEARNING ADOPTION IN THE WORKPLACE: A STUDY OF THE SCIENCE MUSEUM GROUP

It has been observed that mobile learning (mLearning) in institutions like Museums in the United Kingdom (UK) has been underutilized. mLearning usage could potentially increase productivity by delivering just-in-time technical knowledge to the science museum group (SMG) staff. This study uses the unified theory of acceptance and use of technology (UTAUT) model to determine factors affecting mLearning adoption at the SMG. Two research questions were formulated based on an adaptation of the UTAUT model. 1) What are the determinants of behavior intentions to use mLearning at the SMG? 2) Does gender or age have a moderating effect on the factors that determine behavior intentions to use mLearning at the SMG? 118 respondents were surveyed from the SMG. Data obtained were analyzed using Structured Equation Modelling on IBM SPSS 20 and Amos version 25. Results indicate that the UTAUT constructs, performance expectancy, effort expectancy, social influence and facilitating conditions are all significant determinants of behavioral intention to use mLearning. A newly proposed construct, self-directed learning was not a significant determinant of behaviour intentions. Further examination found age and gender moderate the relationship between the UTAUT constructs. These findings present several useful implications for mLearning research and practice for ICT service desk at the SMG. The research contributes to mLearning technology adoption and strategy.

Influencing factors in M-learning adoption in higher education

The tremendous and rapid developments in the information and communications technology sector as well as mobile devices have resulted in modern technologies, one of which is Mobile Learning (M-learning). M-learning is a new technique for learning that helps students to do their educational activities and access the learning materials easily without temporal or spatial restrictions, with the help of mobile devices. It is a robust component to make learning easy and flexible. Recently, many applications and services related to it have been developed. Despite the large number of researchers who have dealt with the topic of M-learning, the issue of factors affecting the adoption of M-learning has not been dealt with adequately, especially in Palestine. Therefore, it becomes necessary to explore the factors influencing the intentions of the students of higher education institutions to adopt M-learning. Hence, the goal of this study is to inspect the factors that influence higher education students’ intentions in Palestine to adopt M-learning system in the learning process and use its applications based on Technology Acceptance Model (TAM) and some external factors. Wherefore, built on the Technology Acceptance Model (TAM) integrated with some external factors (mobility, self-efficacy and enjoyment), this paper proposes a hypothesized model of M-learning in Higher education institutes in Palestine. Relevant data were gathered from a sample of 388 students. Participants, using a self-report questionnaire, reported data. Pearson correlation, multiple linear regression and structural equation modeling (SEM) were employed to analyze the collected data. Results indicate that perceived usefulness and attitude have significant influence on M-learning adoption intention, while perceived usefulness, perceived ease of use and perceived self-efficacy significantly affect the attitude to use M-learning. Perceived enjoyment and perceived self-efficacy are predictors of perceived ease of use. While mobility and perceived ease of use have significant effect on perceived usefulness. These results validate the capacity of TAM constructs and the external variables used in this research for predicting acceptance of M-learning. Limitations and future work are highlighted.

M-learning adoption of management students’: A case of India

The present study aims to interpret management student’s motivation to adopt m-learning and assesses the determinates impacting the behavioral intent of m-learning adoption. A comprehensive research archetype is proposed by integrating two prominent theoretical models, namely UTAUT and UGT. The research model is tested using multi-analytic structural equation modeling (SEM) and advanced neural network (ANN) approach. The quantitative data was gathered and measured from 220 management students. The study outcomes reported that affective need, performance expectancy, effort expectancy, social influence and facilitating conditions positively impacted the student’s intent to use m-learning, whereas cognitive need was found to be insignificant in predicting and explicating the m-learning adoption. The results of sensitivity analysis revealed that effort expectancy showed the highest normalized importance (100%) followed by performance expectancy (97.2%) in explicating the m-learning adoption. The research archetype was able to elucidate 66% of variance in student’s intent towards m-learning adoption. In addition to that, Cohen’s f-square statistic resulted in effect size as 0.771 indicating that the study findings were relevant and substantial with the empirical data collected. Conclusively, the theoretic and managerial implications are described for the proposed model.

The Challenges of Adopting M-Learning Assistive Technologies for Visually Impaired Learners in Higher Learning Institution in Tanzania

In the past decades, the world has experienced major changes in the advancement of learning technologies which has enabled learners to engage in their learning activities anywhere. The penetration of mobile phone internet users in Tanzania has been increasing from 2 million in 2011 to 23mil in 2017 The adoption of mobile-based learning (M-learning) for students who are visually impaired in Tanzania has become a major bottleneck since most of the e-learning contents assume that learners have sight and thus include a lot of visualizations. This causes visually impaired students in higher learning Institutions (HLIs) to face challenges such as technical knowledge gaps. Lack of skills and inaccessibility of online contents, which then lead to drop out of the university. The aim of this study is to determine the awareness and usage levels of existing mobile assistive technologies for visual impairment, and the remaining challenges that visually impaired students face, when using such tools on smartphones to access m-learning content from HLIs. in Tanzania. The research was conducted an observational and contextual inquiry study at three major HLIs. We found that 67% of respondents did not have knowledge of m-learning assistive technologies, and their technology barriers for visually impaired students. Also, knowledge, accessibility of Assistive technology and affordability can hinder the adoption of m-learning in Higher Learning Institutions