Empirical Analysis on Factors Impacting Mobile Learning Acceptance in Higher Engineering Education

Owing to technological advancements and decreasing costs of mobile devices and services, there is a significant change in learning environment that demands for mobility. Such change has enabled a new way of learning, that is, mobile learning. The emergence and prevalence of mobile learning helps flexibility in delivering education, meeting learners' needs, and supporting learning activities without confining to physical locations or time. Mobile learning indicates a new opportunity for education system research and development. The acceptance of mobile learning by students is critical to the successful implementation of mobile learning systems. Therefore, it is important to understand the factors that affect students' perceptions of mobile learning. Encouraged by this new trend in learning, this research employs both quantitative and qualitative research methodologies to explore the factors that affect students' intention to use mobile devices for learning.

Based on the United Theory of Acceptance and Use of Technology (UTAUT), this research formulates the factors, including performance expectancy, effort expectancy, social influence, facilitating conditions, self-efficacy, ubiquity, self-management of learning, attainment value, service quality, and perceived enjoyment, and testable hypotheses that are critical to answer research questions and fulfill research objectives. In order to quantify these factors and test research hypotheses, a data collection instrument adapted from previous studies is developed and administered. The results indicate that performance expectancy, perceived enjoyment, ubiquity, service quality, attainment value, and self-management of learning are significant predictors of behavioral intention to use mobile learning; facilitating conditions, social influence, effort-expectancy and self-efficacy are found to be insignificant.

Additionally, this research examines the differences on intention to use mobile learning across student groups of age, gender, college level, years of using mobile devices, current and planned of mobile device ownership, and prior mobile learning experience via comparison analysis. This research provides university administrators and educators the understandings on the factors that influence student acceptance of mobile learning and the capability to build strategies and policies that incorporate these factors into planning and design phases of mobile learning system implementations.