Mobile Learning Acceptance in Higher Education
Summary
In recent years, the proliferation of smartphones and tablets has transformed higher education by enabling learners to access content anytime and anywhere. Mobile learning acceptance refers to the willingness and behavioural intention of students and educators to use mobile devices for educational purposes. Research has consistently highlighted the importance of theoretical frameworks such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) in explaining adoption patterns. Key determinants include perceived usefulness, perceived ease of use, facilitating conditions and social influence. Self-efficacy and locus of control have also emerged as personal predictors, while security concerns and contextual factors shape perceived value. Empirical studies across diverse regions reveal that integration of mobile learning into curricula can enhance engagement, foster personalised learning pathways and support lifelong learning. Practical applications span interactive course delivery, micro-learning modules and real-time assessment, underscoring global significance for equity and inclusivity. Despite rapid growth, variations in infrastructure, digital literacy and pedagogical readiness pose challenges. Ongoing research seeks to refine acceptance models, address cultural and disciplinary differences and inform institutional strategies that ensure sustainable and scalable mobile learning ecosystems.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Mobile Learning Acceptance in Higher Education publication trend
The graph below shows the total number of articles in mobile learning acceptance in higher education across all publications each year (not limited to Nature Index journals).
Technical terms
Unified Theory of Acceptance and Use of Technology (UTAUT): A framework identifying performance expectancy, effort expectancy, social influence and facilitating conditions as predictors of technology adoption.
Technology Acceptance Model (TAM): A model positing perceived usefulness and perceived ease of use as core determinants of user acceptance.
Perceived usefulness: The degree to which an individual believes that using a technology will enhance their learning performance.
Perceived ease of use: The extent to which a person expects that using a system will be free of effort.
Behavioural intention: A measure of an individual’s readiness to perform a given behaviour, such as adopting mobile learning.
Self-efficacy: An individual’s belief in their capacity to execute behaviours necessary to achieve specific outcomes, such as using mobile tools effectively.
References
- Understanding the antecedents of intention for using mobile learning. Smart Learning Environments (2023).
- Mobile Learning in Higher Education: A Systematic Literature Review. Sustainability (2023).
- Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model. Electronics (2021).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.