Acceptance Of M-learning Research Articles

PREDICTING RURAL STEM TEACHERS’ ACCEPTANCE OF MOBILE LEARNING IN THE FOURTH INDUSTRIAL REVOLUTION

In South Africa, high schools’ Science, Technology, Engineering, and Mathematics (STEM) education is faced with many challenges. However, previous studies have shown that mobile learning (m-learning) can be used to lessen the challenges faced in STEM education. Despite the benefits that m-learning can bring into STEM classrooms, its adoption is still below the expected rate. The acceptance of m-learning depends on the attitude of its users. Most studies focused on learners’ acceptance of m-learning. However, very little is known about rural high school STEM teachers’ acceptance of m-learning in the Fourth industrial revolution (4IR) era. This study proposes a model, which extends the Technology Acceptance Model by introducing perceived social influence and perceived resources. Stratified random sampling was used to select 150 teachers to participate in the survey. A total of 114 valid questionnaires were collected, and data were analysed using partial least squares structural equation modelling. The proposed model explained 37.9 % of the variance in teachers’ behavioural intention to use m-learning in the 4IR era. Perceived attitude towards the use was found to be the best predictor of teachers’ behavioural intention, followed by perceived ease of use, perceived resources, perceived social influence, and lastly perceived usefulness

Is M-learning acceptance influenced by knowledge acquisition and knowledge sharing in developing countries?

Researchers have shown that knowledge acquisition and sharing have considerably influenced the acceptance of various technologies. However, there is a scarce of knowledge on how these two factors affect the acceptance of Mobile learning (M-learning). Thus, this research is believed to be one of the few attempts that aims to understand the impact of knowledge acquisition and knowledge sharing on M-learning acceptance through the extension of technology acceptance model (TAM) by these factors. The data were collected from 735 IT undergraduate students enrolled in two different academic institutions in two different developing countries, namely Malaysia and Oman, using questionnaire surveys. The partial least squares-structural equation modeling (PLS-SEM) is used to validate the extended theoretical model. The findings indicated that knowledge acquisition has a significant positive influence on perceived ease of use and perceived usefulness of M-learning in both samples. Moreover, the findings revealed that knowledge sharing has a significant positive impact on perceived usefulness with respect to the Omani sample, whereas this relation was not supported in terms of the Malaysian sample. Theoretical and practical implications, limitations, and future research directions are also discussed.

Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms

ABSTRACT Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in analyzing the structural models. To address these limitations, this study extends the technology acceptance model (TAM) with the expectation-confirmation model (ECM) and social influence to predict the actual use of m-learning systems. A comparative approach using the partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms was employed to test the proposed model with data collected from 448 students. The results revealed that both techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases. The employment of a comparative analytical approach is believed to add a significant contribution to the information systems (IS) literature in general, and the m-learning domain in specific.

Rural-based Science, Technology, Engineering and Mathematics teachers’ and learners’ acceptance of mobile learning

Background: Science, Technology, Engineering and Mathematics (STEM) is faced with many challenges resulting in learners’ poor performance at matriculation level in South Africa. However, prior research has shown that mobile learning (m-learning) can be used to alleviate the challenges of STEM education. Prior research focused on tertiary institutions’ students and lecturers, in developed countries. However, very little is known about rural school STEM teachers’ and learners’ acceptance of m-learning. Objectives: The article investigates factors that rural-based STEM teachers and learners consider important when adopting mobile learning. Furthermore, the study also seeks to examine if there is a statistically significant difference between teachers’ and learners’ acceptance of mobile learning. Method: The research employed a quantitative approach. Stratified random sampling was used to select 350 teachers and learners to participate in the survey. Valid questionnaires received were 288 (82%), and data were analysed using partial least squares structural equation modelling. Results: The proposed model explained 64% of the variance in rural-based STEM teachers’ and learners’ behavioural intention to use m-learning. Perceived attitude towards use was found to be the best predictor of teachers’ and learners’ behavioural intention. The results also showed no significant difference between teachers’ and learners’ path coefficients. Conclusion: The research recommends that awareness campaigns, infrastructure, mobile devices and data need to be made available for m-learning to be successfully adopted in rural areas.

Fun first, useful later: Mobile learning acceptance among secondary school students in Indonesia

As mobile devices become more ubiquitous, the benefits of mobile learning (m-learning) can be a great opportunity for educating a vast populace, especially in developing countries. Acceptance of m-learning by individuals is critical for these nations while aiming for a successful implementation of m-learning. A total of 1156 middle and high school students in Indonesia participated in this study to investigate factors determining m-learning acceptance among adolescents and to discover the effects of sex, age group, and location differences with the technology acceptance model (TAM) as a theoretical framework. The results indicate that all seven factors in the model are significant determinants of m-learning acceptance with some moderation effects by sex, age, and location differences at play. While the literature showed the importance of perceived usefulness in technology acceptance, including in the m-learning case, this study found that its effect is far less influential than perceived enjoyment and social influence, inferring the irrationality of adolescents in their intention of using m-learning. Perceived mobility value and perceived usefulness, while showing much smaller effects than shown in the literature, are still influential in m-learning acceptance, especially for female and high school students and not necessarily for male and middle school students. Meanwhile, facilitating conditions are crucial in helping female, middle school students, and students in rural areas adopt m-learning. This study helps understand unique characteristics of adolescents as a younger generation that separate them from adults when it comes to their acceptance of m-learning.

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.

Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance

A vast amount of research has been devoted to examine the determinants influencing the acceptance of Mobile learning (M-learning). Nevertheless, little is known about studying the effect of knowledge management (KM) factors on M-learning acceptance. Therefore, the core objective of the present study is to develop a conceptual model by extending the technology acceptance model (TAM) with KM factors (acquisition, sharing, application, and protection) to examine the M-learning acceptance. This study employs the Partial Least Squares-Structural Equation Modeling (PLS-SEM) to validate the developed model. Data were gathered from 416 IT undergraduate students registered at Universiti Malaysia Pahang (UMP) in Malaysia. The results triggered out that knowledge acquisition, application, and protection have positive impacts on perceived ease of use and perceived usefulness. However, knowledge sharing was observed to be partially supported with regard to perceived ease of use and perceived usefulness. The implications to theory and practice, limitations, and future work are also discussed.

M-learning technology in Arab Gulf countries: A systematic review of progress and recommendations

With the recent development in mobile devices and mobile services, mobile learning (m-learning) has become one of the most common research topics across Arab Gulf countries (AGC). The literature showed an inconsistent usage of m-learning in these countries. This paper sheds light on the progress of m-learning use in AGC, as well as offering recommendations to improve its adoption success. A systematic literature review was conducted to identify the current evidence on the use of m-learning in AGC across several groups of instructors and students. The results from reviewing 31 previous studies showed that students’ and instructors’ acceptance and use of m-learning were the main topics of concerned. In addition, the lack of research on leadership and policy practices in AGC may lead to technology failure. The key recommendations on best practices related to the use of m-learning in AGC were presented and discussed.

Investigating factors affecting on medical sciences students\u2019 intention to adopt mobile learning

BackgroundMobile learning (m-learning) provides a good opportunity for students’ lifelong learning. The design and implementation of effective and successful mobile learning requires identification of factors that affect m-learning. The aim of this study was to investigate the factors that affect the intention of students of medical sciences to adopt mobile learning based on theory of planned behavior (TPB).MethodsIn this cross-sectional study, 332 students of medical sciences were randomly selected. The study tool was a based a questionnaire that had been designed based on TPB model. Descriptive statistics (mean, standard deviation, frequency and percentage) were calculated. In order to determine the standardized factor loading and assess the study hypotheses, structural equation modeling was used. Composite reliability, average variance extracted, and standardized factor loading were used to determine the convergent validity.ResultsThe mean of mobile learning readiness was 3.59 ± 0.83. Among the TPB structures, the structures of attitude (β = 0.525) and behavioral control (β = 0.318) had positive and significant effect on the intention to adopt m-learning (P ≤ 0.01). However, the structure of subject norm did not have a significant effect on the intention to adopt m-learning. In general, attitude, behavioral control and subject norm structures were 0.675 determinants of the intention to adopt m-learning (r2 = 0.675).ConclusionsIn this study Mobile learning readiness of the students was at moderate level. Also the results indicated Positive and significant effect of attitude and behavioral control on the intention of students to accept m-learning. The TPB-based model was a suitable model for identifying psychological factors that affect the intention of students of medical sciences to adopt m-leaning. In order to increase the students’ acceptance of mobile learning, we suggest that, other psychological, behavioral, social, and cultural factors that affect the acceptance of m-learning should be identified. Educational programs are also suggested to be introduced to students to familiarize them with the m-learning and its application in learning process.

Extending UTAUT2 toward acceptance of mobile learning in the context of higher education

The use of smartphones as a learning tool in education is on the rise, causing a rapidly developing use of mobile learning (m-learning) in both developed and developing countries. The key features of smartphones, i.e., mobility, ubiquity, lightweight, low-cost and connectivity from anywhere and anytime, enhance their usage in a variety of ways. M-learning is an innovative idea that provides enormous opportunities by connecting humans and technology, such as better learning experiences and technology acceptance. The use of m-learning is growing at a higher pace worldwide, yet sufficient understanding of the factors that influence its acceptance in society is still lacking, particularly in developing countries. A number of models related to m-learning acceptance do exist, for instance, the extended unified theory of acceptance and use of technology (UTAUT2); however, the use of UTAUT2 to study m-learning acceptance is scant in the context of higher education institutes and it does not cover specific features of mobile devices. Therefore, this study not only uses UTAUT2 as a base theoretical framework but also extends it using five other constructs: ubiquity, information quality, system quality, appearance quality and satisfaction. A cross-sectional survey was conducted in two engineering universities in Pakistan. The questionnaire was administered among 900 students, out of which 730 usable responses were selected for further analysis. The data were analyzed using structural equation modeling. The findings revealed that the model fits data well; the model fit indices were within the recommended thresholds. The performance expectancy, hedonic motivation, habit, ubiquity and satisfaction have statistically significant impact on the behavioral intention and the information quality, system quality and appearance quality also have statistically significant impact on the mediator satisfaction toward m-learning acceptance. This study contributes to the body of literature related to technology acceptance models for m-learning by making a tailored extension in UTAUT2 that provides valuable insights into assess m-learning acceptance in the context of higher education institutes of developing countries, specifically in Pakistan.

M-learning adoption in the Arab gulf countries: A systematic review of factors and challenges

The simplicity and minimal cost of mobile phones make them more suitable for use among students and educators across the world. The integration of mobile technology in a university context has been found to provide a superior learning environment compared to the traditional classroom lecture. Yet, there are several challenges that may delay the adoption of m-learning and postpone the revolution it promised. Arab Gulf Countries (AGC), like other developing countries, are encountering challenges linked to the acceptance and adoption of m-learning. Therefore, it is vital to examine the factors driving the adoption of m-learning among instructors and students in these countries. This paper identified the influential factors affecting m-learning adoption among students in Arab Gulf universities. The challenges of adopting m-learning were also identified. A systematic literature review was conducted to explore the current evidence on the use of m-learning in AGC. The results showed different factors (mostly cultural and social) that contribute to m-learning acceptance/adoption in Arab Gulf universities. Findings from this study can aid educational policy makers with planning and implementing effective m-learning programs.

Approach to M-learning Acceptance Among University Students

A growing number of higher education institutions have adopted tools to promote mobile learning. However, studies into the driving factors of its adoption are insufficient. This article identifies the aspects that have an effect on the adoption of mobile learning (m-learning) among university students. The theory of planned behavior (TPB) and technology acceptance model (TAM) have been shown to be valid and powerful models in the research on the adoption of learning technologies. Based on TPB and TAM, we propose a model to explain how perceptions influence m-learning adoption among Colombian university students. To confirm the acceptability of the model, a self-administered questionnaire was applied to 878 undergraduate university students from the Instituto Tecnológico Metropolitano (ITM), a higher education institution in Colombia The results suggest that all of the constructs of TPB and TAM have a moderate impact on the intention to adopt m-learning. Specifically, perceived usefulness and attitude have a significant influence on students’ acceptance of m-learning. These results can stimulate future research and promote an effective diffusion of m-learning in developing countries.

Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers

Despite their many advantages, teachers' adoption of mobile technologies as didactic tools is still limited. Their adoption is conditioned by first-order and second-order barriers. The former are associated with the availability of resources, and the latter refer to internal barriers as a consequence of the reflection of instructors about their own teaching practice, which are harder to overcome. Teacher training plays an important role on the formation of these barriers, but prior research mainly focuses on pre-service teachers in their last years of training, where some of those barriers have already been formed, and it mostly investigates computer-based learning. This research aims to fill that gap by analyzing the influence of second-order barriers on first-year pre-service teachers' intention to use mobile devices in their future teaching practice. The study identifies the most relevant second-order barriers and tests the proposed model using a sample of 160 first-year primary education pre-service teachers. The results of the partial least squares structural equation modeling analysis offer relevant practical, theoretical and methodological implications for mobile learning adoption: first, they provide evidence of the key role of second-order barriers, accounting for 70.8% of the variance of the intention to use these technologies; second, the importance of compatibility and enjoyment, higher than that of traditional key variables as perceived ease of use and perceived usefulness, points out tothe need to reconsider pre-service teacher training programs; third, the study compares traditional reflective modeling of subjective norm and formative modeling, suggesting the adequacy of formative formulations in technology adoption studies.

Factor That Influence To Acceptance M-Learning among Jordanian Students

The research aims to examine critical factors that influence the acceptance and use of M-learning among Jordanian student. This study integrates technology acceptance model (TAM) with service quality factor. The primary data were collected from 487 valid questionnaires, which were distributed, to random Jordanian students in four cities. The analyses of the gathered data employed the (SPSS). The validity of the final overall model was evaluated using the statistics and acceptable fit of the measurement model to the data has been demonstrated. Based on the outcomes, the factors with the highest direct effect on Intention to use M-learning appeared to be Attitude toward using M-learning, while the factor with the highest indirect effect on Intention to use M-learning appeared to be Compatibility. The main findings of the study are: trust factor has a significant stronger impact on PEOU and PU. PEOU and PU have the stronger impact on customers' attitude, which in turn influences students’ intention to use M - learning services.

A Investigate of Influence Factor for Tertiary Students’ M-learning effectiveness: Adjust Industry 4.0 & 12-Year Curriculum of Basic Education

Mobile learning (M-learning) as a technology teaching has significant potential to improve student application and comprehension skills. The rapid development of the Internet and the resulting trends applying information available on the Internet have changed the nature of learning and learning behavior patterns of tertiary students. However, insufficient theoretical and empirical research on the effect of M-learning on learning attitude and M-learning effectiveness has been conducted for achieving any reliable understanding of the use of M-learning by tertiary students. This, study was therefore based on the theory of planned behavior, and combined the technology acceptance model and the structural equation model (SEM); it involved 892 tertiary student participants, and developed an empirical research model. The study found that in terms of M-learning acceptance, tertiary students have a positive evaluation and perception of using M-learning, with perceived enjoyment being the most significant factor. In addition, the impact of perceived innovation was significant in helping teachers understand students’ learning outcomes. The teaching material of learning motivations was also significant, indicating that current tertiary students are more likely to value their friends as sources of information. External influences on self-efficacy were also significant, suggesting that teachers must consider the ability of M-learning technology, with particular attention paid to students’ competency with the technology itself, as difficulty of use will reduce students’ willingness to use M-learning.

Student Preferences for M-Learning in Higher Education Institutions

It is inevitable to use different high-tech devices, particularly smart phones, in today’s rapid life. As a result, educators who work in this sector try to take advantage of developing technology in order to implement the teaching and learning process. Nevertheless, applying mobile learning to educational institutions requires special studies in order to adapt and implement the materials. Due to this reason, it is necessity to make a much more decent research on the acceptance of m-learning by students in terms of setting up m-learning systems in universities. The main goal of this research is to find out the students’ acceptance of mobile learning in Northern Iraq. The questionnaires were used to guide the educators to comprehend the research aim unambiguously and clearly. This study involves three primary research questions that propose to solve with the analysis of the findings, which are gathered by conducting questionnaires: How do university students accept mobile learning through devices inside and outside of the class? What is their level of mobile usage as a learning tool? And whether the students are ready to adapt mobile-assisted language learning. As a result of the research more than half of the students has revealed a positive attitude to use mobile devices in the process of learning. And most of them agree that mobile devices could be used as a learning tool. According to the last section of the questionnaire, students are ready to adopt mobile learning.

Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran

The increasing usage of smartphones, the increasing acceptance of electronic learning (e-learning), the improvement of the status of mobile networks and global internet as well as the need to flexibility in learning process have been led to the emergence of a phenomenon called mobile learning (m-learning). M-learning is not limited to time and place and it will make the desire of “equal education for all people around the world” comes true. Despite the advantages and opportunities that m-learning brings, it has not been implemented in developing countries (like Iran). The goal of this research is exploring the important factors affecting the acceptance of m-learning in Iran. These factors are divided into four macro groups: (1) Technological, (2) Pedagogical, (3) Social and (4) Individual issues. The purpose of this categorization is to discover the important factors affecting the acceptance of m-learning in both macro and micro scales. Because, it can help the developers to prioritize the issues in order to implement m-learning successfully in Iran. In this regard, a survey was conducted at one of the biggest and oldest universities of technology in Iran. The proposed model has been presented based on the combination of two models of Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use Technology (UTAUT) and some other factors due to cultural and social structure of Iran. This model has been tested using the new approach called Partial Least Squares-Artificial Neural Network (PLS -ANN), which has the ability to analyze both linear and nonlinear relationships. The results demonstrate that the selection of mobile devices as an educational strategy is depended on the combination of pedagogical, technological, social and individual factors. These results show that perceived usefulness is the most effective factor in acceptance of m-learning in Iran. Also, due to the cultural and social structure of Iran, personal innovativeness has no impact on acceptance of m-learning. Moreover, pedagogical factors are effective on perceived usefulness as well as technological and individual factors are effective on perceived ease of use. Social factors also have a positive effect on perceived usefulness and perceived ease of use.

Determinantes de la aceptación del mobile learning como elemento de formación del capital humano en las organizaciones (Factors Determining Mobile Learning Acceptance as a Tool of Human Capital Training in Organizations)

Technology has changed the way we communicate, do business and conduct all manner of activities, including organisational training at any time and place. E-learning provides a consolidated training methodology for organisations, opening the way to the latest mobile learning where employees can access content through mobile devices. Given the business opportunities this offers, companies must understand their employees’ acceptance of new technologies. Based on the TAM3 model, an extended version of the original TAM model (Technology Acceptance Model), the main objective of our research is to examine the determinants of the acceptance of mobile learning (m-learning) as an element of employee training. To this end, data was collected through an e-questionnaire of 268 workers who receive all or part of their training through mobile devices. The results obtained have made it possible to confirm most of our hypotheses and verify that the acceptance of m-learning can be reasonably well predicted from individuals' behavioural intention, which is determined by perceived usefulness and perceived ease of use. The results have interesting implications for business management.

Factors Influencing the Acceptance of M-Learning by Students of Higher Education in Morocco

The M-learning plays an increasingly important role in the development of teaching methods in higher education. However, the success of M-learning in higher education will depend on the acceptance of users of this technology. The purpose of this article is to study the factors that impact the intentions of college and university students to accept M-learning. Based on the Unified Theory of Acceptance and Use of technology (UTAUT) [18], this research proposes a model to identifying the factors that influence the acceptance of M- learning in the Higher education of Morocco. A structural equation model was used to analyze the data collected from 193 students. The findings of this study indicate that factors such as the "ease of use" and the "quality of service" influence the intent to use M-learning while others like the "expected performance", the " personal Innovativeness" and the "influence of professors” have no impact on the intent of using M-learning.

Technology Acceptance Model in M-learning context: A systematic review

Various review studies were conducted to provide valuable insights into the current research trend of the Technology Acceptance Model (TAM). Nevertheless, this issue still needs to be investigated from further directions. It has been noticed that research overlooks the investigation of TAM with regard to Mobile learning (M-learning) studies from the standpoint of different perspectives. The present study systematically reviews and synthesizes the TAM studies related to M-learning aiming to provide a comprehensive analysis of 87 research articles from 2006 to 2018. The main findings include that most of the TAM studies involving M-learning focused on extending the TAM with external variables, followed by the studies that extended the model by factors from other theories/models. In addition, the main research problem that was frequently tackled among all the analyzed studies was to examine the acceptance of M-learning among students. Moreover, questionnaire surveys were the primarily relied research methods for data collection. Additionally, most of the analyzed studies were undertaken in Taiwan, this is followed by Spain, China, and Malaysia, respectively among the other countries. Besides, most of the analyzed studies were frequently conducted in humanities and educational context, followed by IT and computer science context, respectively among the other contexts. Most of the analyzed studies were carried out in the higher educational settings. To that end, the findings of this review study provide an insight into the current trend of TAM research involving M-learning studies and form an essential reference for scholars in the M-learning context.