ERG SES H 06, ICT and Education
The idea of mobile learning has been around since the early examples of portable devices including portable digital assistants (PDAs) and laptop computers. It, in the most simplistic terms, refers to accessing instructional resources through mobile devices virtually anytime and anywhere. The term applies to both informal and formal learning endeavors. The informal aspect is more up to the learners’ demands of information, whereas, the formal side is more structured as it is based on an external authority’s requirements. By its very nature, mobile learning is based on tools. During the last two decades, we have experienced a wealth of new technologies that brought about new opportunities as well as improved flexibility. Taking increased access to mobile devices into account, it would be fair to say there are less technical obstacles for mobile learning today. Nonetheless, research on mobile device use patterns indicated learning is not the most common purpose of using such devices (Lai & Smith, 2017; Lau, Chiu, Ho, Lo, & See-To, 2017; Şad, Göktaş, & Ebner, 2016; Steeves, 2014). Along with the access to technology, technology acceptance by the stakeholders is an important prerequisite of technology integration in education. Therefore, it is important to study the factors affecting learners’ acceptance of mobile devices for learning purposes.
The purpose of this study is to investigate the research trends in mobile learning acceptance/adoption through content analysis to reveal research trends and gaps in the literature, and consequently inform future studies. In line with this purpose, the following questions regarding mobile learning acceptance research were formulated:
- What theoretical frameworks are utilized the studies?
- What indicators of acceptance were employed in the studies?
- What are the participant and sample size preferences?
- What mobile technologies were employed in the studies?
- What means of measuring actual use were employed in the studies?
- What data analysis methods were utilized in the studies?
To answer the research questions, first, a literature review on mobile learning acceptance was conducted. Some general models of technology acceptance behavior were also reviewed to guide the process. A number of academic databases including EBSCO, ERIC, Science Direct, and Google Scholar were searched using the terms “mobile learning acceptance” and “mobile learning adoption”. An initial review resulted in over a hundred studies in full-text form. These studies, then, were examined in terms of inclusion criteria. An eligible study should focus on the factors affecting learners’ mobile learning acceptance, therefore, general e-learning studies mentioning mobile learning briefly in a couple of sentences were excluded from the study. Moreover, it should also be based on empirical data and should provide enough information on sampling, research design, and other procedures. In line with that, the studies that provided little information about the details, editorials, and the theoretical studies that proposed a mobile learning acceptance model were also excluded. The inclusion criteria did not pose any limits as to publication type or publication period. The studies were not evaluated in terms of academic rigor involved, either. Hence, any conference proceeding, journal article, colloquium or book chapter published before the date of the review was included in the study. The final sample consisted of 66 studies. The eligible studies were analyzed using content analysis employing an article review form developed by the researchers. The form included details such as the title, abstract, country of origin, research design, measurements, sample type (e.g., K-12, Higher Education, faculty and so on), sample size, the employed technology acceptance model or framework (if any), factors identified or examined in the study, mobile technology or technologies employed in the study and means to measure actual use.
The results indicated that the most common technology acceptance frameworks employed in the mobile learning contexts were variants of Technology Acceptance Model (TAM) (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000)) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003; Venkatesh, Thong, & Xu, 2012). As to the indicators, behavioral intention, perceived usefulness, perceived ease of use, social norms, and attitude were frequently utilized in the studies. In addition, personal innovativeness and self-management of learning were often used to extend the established models. Most studies focused on formal educational settings and collected data from higher education environments. The majority of the studies included undergraduate students (79,1%) as participants. Graduate students (10,4 %) and K-12 students (9%) were the second and third most common participant groups, respectively. Remaining studies also included teachers, academicians, and managers as participants. A significant portion of the reviewed studies was domain general, that is, they were not connected to a specific course, learning environment or mobile technology. This seems fair considering predominantly informal nature of mobile learning. However, this situation indicates an important limitation of the extant literature as a clear majority of the studies collected data within formal settings. Data analysis often involved structural equation modeling (SEM) and regression variant. Covariance-based SEM was the predominant method of analysis (40.3%), while regression variants (e.g., hierarchical regression) (26.9%) and partial least squares SEM (11.9%) were also trending. Other methods of data analysis included correlational methods, t-Test, and variance analysis. This study investigated the research trends in the mobile learning acceptance literature. A list of included studies, more detailed accounts, interpretations, and discussion of the findings, and recommendations for the future studies will be provided in the full paper.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008 Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology : A comparison of two theoretical models. Management Science, 35(8), 982–1003. Lai, K.-W., & Smith, L. (2017). Socio-demographic factors relating to perception and use of mobile technologies in tertiary teaching. British Journal of Educational Technology, 0(0). https://doi.org/10.1111/bjet.12544 Lau, K. P., Chiu, D. K. W., Ho, K. K. W., Lo, P., & See-To, E. W. K. (2017). Educational usage of mobile devices: Differences between postgraduate and undergraduate students. Journal of Academic Librarianship, 43(3), 201–208. https://doi.org/10.1016/j.acalib.2017.03.004 Şad, S. N., Göktaş, Ö., & Ebner, M. (2016). Prospective Teachers—Are They Already Mobile? In A. Peña-Ayala (Ed.), Mobile, Ubiquitous, and Pervasive Learning. Advances in Intelligent Systems and Computing (pp. 139–166). Springer. https://doi.org/10.1007/978-3-319-26518-6_6 Steeves, V. (2014). Young Canadians in a Wired world Phase III. MediaSmarts. Ottawa. https://doi.org/10.2752/174589314X13834112761164 Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal Studies. Management Science, 46(2), 186–205. https://doi.org/10.1287/mnsc.22.214.171.12426 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.1111/j.1365-2729.2006.00163.x
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