Adoption Of M-learning Research Articles

Determinants affecting the medical students adoption of mobile learning: extended UTAUT

BackgroundThis research investigates the fundamental factors which may stymie or simplify the adoption of m-learning by medical college educators in Sudan. We made use of the Extended Unified Theory of Acceptance and Use Technology (UTAUT) with COVID-19 fear (CF) and social isolation (SIS) as exogenous components.ObjectiveThis research aims to find out factors impacting medical students’ behavioural intention to use mobile learning.MethodsData was gathered from 499 undergraduate learners from different universities in Sudan. Partial Least Square (PLS) and structural equation modeling (SEM) were used to examine the data.The resultsShowed that expected performance, effort expectancy (EE), social influence (SI), facilitation condition (FC), COVID-19 fear, and social isolation (SIS) are predictors of behavioural intentions (BI). In addition, our findings show that COVID-19 fear and social isolation increase student adoption of mobile learning during the COVID-19 epidemics in developing countries.ConclusionsThe study unveiled that the suggested framework, derived from UTAUT, effectively discerns the factors impacting students’ intentions in mobile learning during pandemics. Sudanese medical students’ can strategize and execute effective classroom environments based on these findings.

Factors Affecting The Adoption Of Mobile Learning In Vocational High Schools And High Schools Using Extended UTAUT

M-Learning is a learning process that uses technology or mobile devices such as smartphones, tablets or wearable devices to support the learning process. This is still being done because there are many different theoretical models proposed. However, there is no model that can be generally accepted as an established theoretical model in the application of M-learning in vocational and high school education environments in Sidoarjo. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determining factors that influence M-learning adoption using the Unified Theory of Acceptance and Use of The Technology (UTAUT). To collect data, researchers distributed questionnaires to respondents using Google Form. The data used were 444 M-learning users. Theoretical model research was carried out using Structural Equation Modeling (SEM) analysis, then SPSS and Amos as analysis support. There are seven factors that determine the results of acceptance of M-Learning adoption in this research, namely Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. The six factors that show a positive and significant relationship are Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. Perceived Convenience has the first strongest positive and significant value, and Performance Expectancy has the second strongest value. Each factor has a moderate influence on Intention to Use. This factor is the most influential in implementing M-Learning in vocational and high schools in the Sidoarjo area.

Adoption and continued usage of mobile learning of virtual platforms in Iraqi higher education an unstable environment

This study investigates the implementation and continued utilization of mobile learning (M-learning) in Iraqi universities, considering the challenging circumstances of an unstable environment. The research expands upon the UTAUT2 and ECM models. The main objective is to tackle the difficulties and possibilities in Iraq's higher education institutions (HEIs) caused by geopolitical instability and comprehend their influence on student acceptance, satisfaction, and ongoing M-learning usage. The study expands on the increasing significance of mobile learning, particularly in higher education institutions (HEIs). It acknowledges the distinct difficulties encountered by institutions in Iraq due to the region's instability. The study identifies deficiencies in current models and suggests expansions by introducing the variable "Civil Conflicts" to consider the unstable environment. The study seeks to enhance comprehension of M-learning acceptance in conflict-affected regions and offer insights for enhancing M-learning initiatives in Iraqi higher education institutions. To accomplish its goals, this study utilizes a quantitative survey to gather data from 399 students in five universities in Iraq. PLS-SEM is employed to analyze quantitative data and assess the extended UTAUT2 and ECM models. The study's results are anticipated to enhance the understanding of M-learning adoption and ongoing usage in conflict-affected regions, specifically in the context of Iraqi higher education institutions. The study's results can guide improving the efficiency of M-learning programs in Iraqi higher education institutions and provide valuable knowledge on supporting education in regions marked by instability. Researchers' findings can assist educators and policymakers in making well-informed choices to promote the continuity and excellence of education, especially in regions affected by conflict. Researchers can expand upon this study by conducting further investigations into the implementation and utilization of M-learning in volatile environments and assessing the efficacy of the suggested model enhancements.

What drives the adoption of mobile learning services among college students: An application of SEM-neural network modeling

This research aimed at examining factors influencing college students to adopt mobile learning (m-learning) services. An integrated model combined the information systems success (ISS) and Unified Theory of Acceptance and Use of Technology (UTAUT2), was developed to identify m-learning determinants. A sample of 552 was recruited to test hypotheses using structural equation modeling (SEM). The significant factors explained 70 % of the variance toward Behavioral Intention (BI) based on SEM results. While price value (PV), effort expectancy (EE), performance expectancy (PE), and privacy (PR) were not significant predictors of BI, the results of the neural network model ranked the predictive power of the factors in the following order: information quality, habit (HB), system quality (SYQ), hedonic motivation (HM), facilitating condition (FC), and social influence (SI), positively influenced m-learning adoption. The findings of this study helps the policy makers at higher educational institutions to formulate strategies to enhance students’ learning experience in upcoming crises and place a focus on sustainable mobile learning environment.

Analysis of the Impact of Personalized Learning Paths in Mobile Learning Platforms on the Development of Self-Directed Learning Strategies Among Brazilian College Students

This paper investigates the impact of personalized mobile learning (m-learning) platforms on the development of self-directed learning (SDL) strategies among Brazilian college students. Employing a mixed-methods research design, the study combines quantitative surveys with qualitative interviews to explore how personalized learning paths influence SDL skills such as goal-setting, time management, and reflective practice. The quantitative analysis reveals a significant positive correlation between the degree of personalization in m-learning experiences and enhancements in SDL competencies. Qualitative insights further elucidate students’ perceptions of personalized m-learning, highlighting increased motivation, engagement, and a sense of ownership over their learning processes. The research also delves into the challenges and opportunities presented by the Brazilian educational context, including infrastructural limitations and the digital divide. The findings underscore the potential of personalized m-learning platforms to foster learner autonomy and suggest implications for educators, policymakers, and educational technology developers. The study calls for further research on the longitudinal effects of personalized learning and the socio-economic factors affecting m-learning adoption and effectiveness.

Investigating Students' Intention to Use M-Learning

Mobile learning (M-learning) has become a crucial tool for both students and educators. The unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM), and outside factors were all included in this study's integrative review approach to examine the variables affecting university students' intention to use M-learning and mobile effectiveness. A study at King Faisal University examined the viewpoints of 364 undergraduate and graduate students using a random sampling technique to have better understand the impact of such technological innovation on teaching. Participants in the study were invited to complete a survey created expressly for this study in order to find out if they would still be open to using M-learning to advance the cause of sustainable education. The paradigm of the study was evaluated using a structural equation modeling (SEM) approach and was based on the UTAUT and TAM technology adoption models. The findings demonstrated that each element of the research model had a positive influence on learners' behavioral intention to use M-learning (BIM); it also demonstrated the impact of that intention on long-term educational sustainability. The link between independent characteristics and users' enjoyment and adoption of M-learning is moderated by mobile usability and behavioral control to utilize M-learning. The results also showed that BIM and the utility of mobile devices increased the degree to which M-learning is liked. Mobile usability and BIM both favorably affect sustainable education in terms of user satisfaction and M-learning adaptation. These findings suggest that mobile usability and behavioral intention to utilize M-learning are the main factors influencing the adoption of M-learning in Saudi Arabia's higher education.

Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis

The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed.

Adoption of mobile application for enhancing learning in higher education: Students’ views from the State University of Zanzibar, Tanzania

Mobile (m)-learning is a technology-enhanced learning approach utilizing mobile technologies to facilitate learning. Despite significant attention from previous researchers, there is a lack of adequate studies about student’s usage and perceptions of m-learning in Zanzibar’s’ higher learning institutions (HLIs). In this study, the researchers explored the specific activities undertaken by students through the m-learning system, the students’ perceptions on the usefulness and ease of use of m-learning system, and the challenges they faced at the State University of Zanzibar (SUZA). The study used the survey method with descriptive statistical analysis and examined 240 randomly selected participating in the m-learning initiative. The findings indicate that 96.7% of participants easily used the system to navigate learning resources, 92.9% viewed assignments, and 94.6% took quizzes. Additionally, the majority of the respondents showed a positive attitude regarding the usefulness and ease of use of the system. However, 57% of participants experienced challenges, including unreliable internet connectivity and system incompatibility with some of their devices. The findings of this study will help HLIs like SUZA to find better strategies to improve m-learning, especially pedagogical features and awareness among students. Moreover, the study underscores the importance of more research to discover factors that influence m-learning adoption in Africa HLIs.

Continued Usage Intention of Mobile Learning (M-Learning) in Iraqi Universities Under an Unstable Environment: Integrating the ECM and UTAUT2 Models

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.

Mobile learning for teacher professional development: An empirical assessment of an extended technology acceptance model

This study explores how mobile learning (m-learning) can serve as a valuable resource for the professional development of Iraqi English as a Foreign Language (EFL) teachers in higher education during the COVID-19 pandemic. Utilizing an extended Technology Acceptance Model (TAM), the research investigates the model's fit, structural relationships between variables, and potential moderating effects of gender and academic rank. Findings revealed that Iraqi EFL teachers generally demonstrated positive attitudes toward m-learning adoption, with identified challenges in self-efficacy and ease of use. Gender analysis indicated that females exhibited higher ease of use, self-efficacy, enjoyment, and positive attitudes and intentions toward m-learning adoption. Higher-ranked teachers perceived m-learning as more useful. The extended TAM displayed a good fit to empirical data, revealing significant positive relationships between variables. Gender did not moderate these relationships, but academic rank played a substantial role. The findings guide strategies for targeted professional development, addressing technical support concerns, and designing engaging experiences to facilitate successful m-learning integration in higher education, considering the unique needs and challenges of Iraqi EFL teachers.

Using m-learning in teacher education: A systematic review of demographic details, research methodologies, pre-service teacher outcomes, and advantages and challenges

Mobile learning (m-learning) is a crucial educational technology for teacher education due to its significant benefits and the development of mobile technology. This study’s objective is to conduct a systematic review and present a recent synthesis of the m-learning literature from 2018 to 2023 in teacher education relating to subject publication year, geographic distribution, matter domains, mobile devices and technologies used, research methodologies used to examine the implementation of m-learning, results for pre-service teachers, as well as benefits and challenges of m-learning adoption. The study used the systematic review methodology and PRISMA guidelines. A list of 27 studies was included from several relevant studies in four, Google Scholar, Mendeley, ScienceDirect, and Scopus, databases using inclusion and exclusion criteria to evaluate the full text after screening the titles and abstracts. The results of this study show that m-learning has garnered interest in numerous nations worldwide, applied in different subject matter domains with the use of various mobile devices and technologies. More significantly, the findings show that using mobile learning to learn positively impacts how pre-service teachers develop their knowledge, skills, and attitudes. Additionally, adopting this learning style recently in teacher education has certain advantages and challenges, requiring lecturers, pre-service teachers, and institutions to have the necessary equipment for knowledge, skills and facilities to achieve efficiency. Consequently, the results of this study can be used as a guide for research on m-learning in the future and contribute to the body of knowledge about this pedagogical strategy for teacher training.

Students’ Adoption of m-Learning in Higher Education: An Empirical Study

Students’ Adoption of m-Learning in Higher Education: An Empirical Study

M-Learning adoption in higher education towards SDG4

Mobile learning (M-Learning) is under-researched in Zimbabwe although it has the potential to improve learning and teaching in the Zimbabwe higher education sector. It is a tool that can help to achieve the United Nation’s Sustainable Development Goal 4 (SDG4) which is to “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”. This study identifies the benefits and challenges of transitioning from a traditional approach to M-Learning from the perspective of Zimbabwean students, and how these challenges can be mitigated or overcome in tertiary institutions to achieve the SDG4. This qualitative study utilises focus group discussions involving twenty undergraduate students from three higher education institutions, and conducts thematic analysis of the data. This paper identifies the benefits of M-Learning in terms of SDG4 and the challenges of M-Learning in attaining SDG4, showing the complexity involved in introducing digital learning on a larger scale. The study offers suggestions to mitigate the challenges, and recommends ways to implement M-Learning effectively to ensure the provision of quality education. Finally, the paper concludes with an acknowledgement of the study’s limitations and suggestions for future research.

State of the Art of Mobile Learning in Jordanian Higher Education: An Empirical Study

A new approach to learning is mobile learning (m-learning), which makes use of special features of mobile devices in the education sector. M-learning is becoming increasingly common in higher education institutions all around the world. The use of mobile devices for education and learning has also gained popularity in Jordan. Unlike studies about Jordan, there are many studies that thoroughly analyze the situation of m-learning in other countries. Thus, it is important to understand the current situation of m-learning at Jordanian universities, especially in light of the COVID-19 pandemic. While there have been some studies conducted prior to COVID-19 and a few studies after COVID-19, there is a need for a comprehensive study that provides an in-depth exploration of the current situation, student adoption, benefits, disadvantages, and challenges, particularly following COVID-19. Therefore, this study utilizes a sequential exploratory mixed research method to investigate the current state of the art of m-learning in Jordanian higher education with a particular focus on student adoption, benefits, disadvantages, and challenges. Firstly, the study explores the existing literature on m-learning and conducts 15 interviews with educators and learners in three Jordanian universities to gain insights into their experiences with m-learning. The study then distributes a survey to students at four Jordanian universities, representing both public and private universities, to generalize the results from the qualitative study. Additionally, the study investigates the relationship between student enrollment in public/private universities and the adoption of m-learning. The study came to the conclusion that students have a positive opinion of m-learning and are also willing to use it. However, there are a number of disadvantages and challenges to its adoption. Additionally, there is a relationship between student enrolment in public/private universities and the adoption of m-learning. These findings have important implications for institutions that want to incorporate m-learning into their undergraduate and graduate degree programs, as they aid decision-makers in these universities in creating frameworks that may be able to meet the needs of m-learning.

Mobile Learning Among University Students: Adoption and Application of M-Learning

The successful implementation of mobile learning is mainly impacted by learners’ attitudes to accepting and applying mobile learning systems. In the current study, an m-learning application was developed and implemented for university students, and their perspective toward the adoption and application of m-learning was investigated. This cross-sectional study was carried out in 2020. The participants were 114 university students at Abadan University of Medical Sciences. Data were collected by means of a valid questionnaire containing 42 questions in 13 subscales in addition to an open-ended question about the positive and negative aspects of the m-learning application. There was a significant relationship between acceptance and major (P=0.001), mobile usage (P=0.035), and familiarity with m-learning (P<0.001). The total mean of m-learning application acceptance was 3.95±0.32, which shows a good level. Self-efficacy, perceived ease of use, and user interface were, respectively, the most influencing factors; mobile device limitation and governmental support were the least influencing factors. “Direct communication with the teacher/instructor” and “Internet disconnection during video teaching” were among the positive and negative aspects of applying the m-learning application, respectively. The m-learning application was well adopted among the students. Mobile device limitations are a reason for the student's concerns. “Usability features” can be a point for covering mobile device limitations.

Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia

In light of the conditions of the Covid-19 pandemic, people are looking for alternatives to accomplish their work. Education was not isolated from this change, where reliance, in whole or in part, on the use of modern ICTs has become imperative (e-learning). A related term is "mobile learning", which refers to the use of mobile devices in the learning process. Accordingly, the purpose of this research is to examine university level students’ behavioral intentions to use a mobile learning management system (m-LMS) (e.g. Blackboard application) in Saudi Arabia by empirically testing an extended version of the UTAUT model. For data collection, 258 university students participated in an online-based questionnaire. Six out of the eight proposed hypotheses were supported by analysis. PE, EE and LI showed a great impact on BI whereas FC was not significant factor. PMV and AR also demonstrated influence on PE, but UMS indicated no impact on PE. Further, UMS had a significant impact on EE. Results contribute to academia by enriching technology adoption literature in general and m-learning adoption in particular. Findings also contribute to policy and practice by providing insights to officials, decision makers, educators and learning management systems developers in terms of strategy, system design and resource management to meet the needs of students to increase their behavioral intentions to use m-LMS.

Adoption of Mobile Learning at Universities Using the Extended Technology Acceptance Model

This study aims to contribute to the proof of factors likely to determine the success of M-learning adoption based on previous research. This is done because there are many different theoretical models proposed. However, there is not yet a model that can be generally accepted as an established theoretical model for the adoption of M-learning in universities. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determinants that influence the adoption of M-learning using the Technology Acceptance Model (TAM). To collect the data, researchers distributed questionnaires to respondents using google forms. Forms are distributed via WhatsApp and Telegram. The data used was 515 M-learning users. Theoretical model research was carried out with Structural Equation Model (SEM) analysis, then SPSS and Amos as support for analysis. There are six factors that determine the results of acceptance of M-leaning adoption in this study, namely Social Influence, Perceived Enjoyment, Facilitating Condition, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. The five factors that show positive and significant relationships are Social Influence, Perceived Enjoyment, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. Perceived Usefulness has the first strongest positive and significant value, and then Social Influence has the second strongest value. Each factor has a medium influence value on Behavioral Intention. That factor is the most influential in the application of M-learning in universities.

Investigating lecturers’ reasons for adoption of mobile learning in higher education: A case study


 
 
 The usage of Mobile Learning (M-learning) has grown among Institutes of Higher Learning worldwide as mobile technologies benefit teaching and learning. Despite this interest, there is a lack of M-learning research in formal educational contexts and the reasons for lecturers to adopt M-learning in a Singapore private education institute have been limited. Furthermore, the Teaching and Learning Division (TLD) of the institution which looks after the professional development of lecturers and sponsors innovative technology-mediated learning initiatives like M-learning is keen to find out how lecturers can be supported to encourage a greater usage of M-learning. After interviewing 10 lecturers, the research found that the following five reasons influence lecturers’ adoption of M-learning from most important to the least: Usefulness in Teaching and Learning, Barriers to using M-Learning, Ease of use of mobile technology, Students’ Motivation and Service quality of the M-learning systems. From this, TLD can identify and develop strategies to support the successful integration of M-learning. The five support aspects considered when implementing M-learning are found to be the following in descending order of importance: Professional Development, Social, Organisational, Technical and Advanced features to support teaching and learning. This research has clarified the existing literature in M-learning especially in formal learning contexts where learning is intentional and recognises the role that lecturers play in the future adoption of M-learning.
 
 

Adoption of M-Learning in Business English Course and Its Relationship to Learning Style Preferences: An Empirical Investigation.

Learning around the world has been changed with the rapid development in technology which promotes the students to be more flexible and interactive with each other which has been encouraged by the mobile learning environment. Therefore, the current study intends to analyze the impact of inquiry learning, reflective thinking on problem-solving skills, and critical thinking skills with the mediation of peer communication. To carry out the study, data was collected from 378 college students in China by using survey forms. The analysis of the data and validation of the proposed hypotheses were conducted using Smart-PLS and structural equation modeling (SEM) technique. The results revealed that inquiry learning and reflective thinking affect problem-solving skills. However, inquiry learning and reflective thinking did not affect critical thinking skills. Moreover, the study found that peer communication mediated the relationship between reflective thinking, problem-solving skills, and between reflective thinking and critical thinking skills. However, peer communication did not mediate the relationship among inquiry learning as independent variable and problem-solving skills and critical thinking skills as dependent. The study has theoretically contributed by examining the impact of online learning styles on higher-order thinking skill (HOTS) in the M-learning environment. Also, the study greatly advances the literature by investigating the mediating role of peer communication. Practically, the colleges can improve the students HOTS by devising policies and educational programs focusing on learning styles.

Smart Mobile Learning Success Model for Higher Educational Institutions in the Context of the COVID-19 Pandemic

Smart mobile learning (M-learning) applications have shown several new benefits for higher educational institutions during the COVID-19 pandemic, during which such applications were used to support distance learning. Therefore, this study aims to examine the most important drivers influencing the adoption of M-learning by using the technology acceptance model (TAM). The structural equation modelling (SEM) method was used to test the hypotheses in the proposed model. Data were collected via online questionnaires from 520 undergraduate and postgraduate students at four universities in Saudi Arabia. Partial least squares (PLS)–SEM was used to analyse the data. The findings indicated that M-learning acceptance is influenced by three main factors, namely, awareness, IT infrastructure (ITI), and top management support. This research contributes to the body of knowledge on M-learning acceptance practices. Likewise, it may help to facilitate and promote the acceptance of M-learning among students in Saudi universities.