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.

The current social dynamics perceive education as an impeller of social and economic change. However, there are problems of coverage, relevance and methodology in the educational process, mainly in Higher Education Institutions (HEIs). Recently, tools have emerged that adopt and appropriate ICTS in learning processes, such as mobile learning, which seeks to meet the requirements of mobility, accessibility and interactivity that traditional teaching mechanisms have not covered satisfactorily. Based on the necessity to identify the factors that drive the adoption of mobile learning by different HEIs, this research identifies the factors that encourage the adoption of mobile learning in the institutions. For this, results of an exercise of technological surveillance in indexed database (Scopus) were analyzed by means of scientometric indicators. Some of the most significant factors detected are: preparation of the professor, perceptions and attitudes of students, technological culture, and technological environment that leverages new learning models. Keywords: bibliometric analysis, high education, mobile learning, research trends.

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