Technology acceptance in education has been and continues to be a central concern for researchers, application and platform developers, and educators. Rapid advancements in miniaturization along with the availability of fast, reliable, and affordable networks have sparked an increasing demand by students for better ways to complement their mobile lifestyles in support of their learning. Based on a review of the literature of technology acceptance and trends in mobile device usage in learning, this researcher tested the predictive power of the Mobile Learning Acceptance Model (MLAM) in an online higher education setting. MLAM is an extension of the technology acceptance model (TAM) inasmuch as it seeks to obtain user perceptions of usefulness and ease of use and their effect on user attitude and behavioral intention to use mobile devices for learning. For this research, users included students and faculty. Current literature indicates that student desire for access to a variety of learning resources anywhere anytime is growing yet little is known regarding faculty perceptions regarding mobile learning (m-learning) or on how institutions can position themselves to meet the growing demand.
A web-based survey design was used to test MLAM using a previously developed and validated instrument, though updated to include and exclude what is now or no longer applicable and the wording modified to ensure relevancy to the target population studied. Exploratory factor analysis was performed to validate the factor structure. Multiple regression analysis was performed to determine which factors had the greatest influence on m-learning acceptance. Group analyses revealed significant differences among faculty and students between age groups, mobile device experience levels, and desired academic uses of mobile devices.
The results of this study enables administrators to make informed decisions regarding information technology (IT) investments, allocate scarce resources strategically, and implement appropriate technical support systems. Findings from this investigation may also be of interest to instructional designers, m-learning application developers, and mobile device manufacturers who will gain a better understanding of how to develop m-learning solutions that are both useful and easy to use.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1229 |
| Date | 01 January 2013 |
| Creators | Marrs, Kathleen |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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