Résumés
Abstract
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.
Keywords:
- mobile learning,
- adoption factors,
- TPB,
- TAM,
- university students.
Parties annexes
Bibliography
- Adebayo, F. A. (2010). Towards the successful implementation of open and distance learning in Nigeria. Paper presented at the 12th Cambridge Conference on Open and Distance Learning, Cambridge, England.
- Aghaee, N., Jobe, W. B., Karunaratne, T., Smedberg, Å., Hansson, H., & Tedre, M. (2016). Interaction gaps in PhD education and ICT as a way forward: Results from a study in Sweden. The International Review of Research in Open and Distributed Learning, 17(3). doi: 10.19173/irrodl.v17i3.2220
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi: 10.1016/0749-5978(91)90020-T
- Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683. doi: 10.1111/j.1559-1816.2002.tb00236.x
- Ajzen, I., & Driver, B. L. (1992). Application of the theory of planned behavior to leisure choice. Journal of Leisure Research, 24(3), 207-224. doi: 10.1080/00222216.1992.11969889
- Akinwamide, T. K., & Adedara, O. G. (2012). Facilitating autonomy and creativity in second language learning through cyber-tasks, hyperlinks and net-surfing. English Language Teaching, 5(6), 36-42. doi: 10.5539/elt.v5n6p36
- Al-Hunaiyyan, A., Alhajri, R. A., & Al-Sharhan, S. (2016). Perceptions and challenges of mobile learning in Kuwait. Journal of King Saud University-Computer and Information Sciences. 30(2), 279-289. doi: 10.1016/j.jksuci.2016.12.001
- Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. doi: 10.1037/0033-2909.103.3.411
- Bach, T., Tariq, M. A., Mayer, R., & Rothermel, K. (2017, September). Knowledge is at the edge! How to search in distributed machine learning models. In OTM confederated international conferences “on the move to meaningful internet systems” (pp. 410-428). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-69462-7_27
- Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. doi: 10.1007/BF02723327
- Brown, T. H., & Mbati, L. S. (2015). Mobile learning: Moving past the myths and embracing the opportunities. The International Review of Research in Open and Distributed Learning, 16(2). doi: 10.19173/irrodl.v16i2.2071
- Cadavieco, J. F., Goulão, M. de F., & Costales, A. F. (2012). Using augmented reality and m-learning to optimize students performance in higher education. Procedia-Social and Behavioral Sciences, 46, 2970-2977. doi: 10.1016/j.sbspro.2012.05.599
- Calvo, C., Martínez Fernández, V. A., & Juanatey Boga, O. (2013). Análisis de dos modelos de ecuaciones estructurales alternativos para medir la intención de compra [Analysis of two models of alternative structural equations to measure purchase intention]. Investigación Operacional, 34(3), 230-243.
- Cao, C. F., & Li, G. S. (2013). Research and Design of Information Systems for Mobile Autonomous and Open Learning Based on RIA and Integrated Framework. Advanced Materials Research, 675, 27-30). doi: 10.4028/www.scientific.net/AMR.675.27
- Cataldi, Z., & Lage, F. (2012). TICs en educación: Nuevas herramientas y nuevos paradigmas. Entornos de Aprendizaje Personalizados en dispositivos móviles [ICTs in education: New tools and new paradigms. Personalized Learning Environments in Mobile Devices]. In VII Congreso de Tecnología en Educación y Educación en Tecnología. TE&ET (Num. 7, pp. 50-59). Buenos Aires, Argentina: Red de Universidades Nacionales con Carreras de Informática.
- Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064. doi: 10.1016/j.compedu.2012.04.015
- Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation), Cambridge, MA: Sloan School of Management, Massachusetts Institute of Technology.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. doi: 10.2307/249008
- Depetris, M. R., Tavela, D., & Castro, M. F. (2012). El futuro de las tecnologías móviles y su aplicación al aprendizaje: Mobile learning [The future of mobile technologies and their application to learning: Mobile learning]. In VII Congreso de Tecnología en Educación y Educación en Tecnología. TE&ET (pp. 6). Buenos Aires, Argentina: Red de Universidades Nacionales con Carreras de Informática.
- Díez-Echavarría, L., Valencia, A., & Cadavid, L. (2018). Mobile learning on higher educational institutions: How to encourage it? Simulation approach. Dyna, 85(204), 325-333. doi: 10.15446/dyna.v85n204.63221
- Downes, S. (2017). New models of open and distributed learning. In Open Education: From OERs to MOOCs (pp. 1-22). Berlin Heidelberg: Springer. doi: 10.1007/978-3-662-52925-6_1
- Duarte, A. M., & Arteaga, R. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640. doi: 10.1016/j.chb.2010.06.011
- El-Hussein, M. O. M., & Cronje, J. C. (2010). Defining mobile learning in the higher education landscape. Educational Technology & Society, 13(3), 12-21.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
- Gong, Z., & Wallace, J. D. (2012). A comparative analysis of iPad and other m-learning technologies: Exploring students' view of adoption, potentials, and challenges. The Journal of Literacy and Technology, 15(2), 2-29.
- Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2001). Análisis multivariante [Multivariate analysis] (5th ed.). Madrid: Prentice Hall Iberia.
- Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070. doi: 10.1016/j.tele.2017.09.016
- Herrera, S. I., & Fennema, M. C. (2011). Tecnologías móviles aplicadas a la educación superior [Mobile technologies applied to higher education]. In XVII congreso Argentino de ciencias de la computación. Buenos Aires, Argentina: Red de Universidades con Carreras en Informática (RedUNCI). Retrieved from http://sedici.unlp.edu.ar/handle/10915/18718
- Huang, R. T., Hsiao, C. H., Tang, T. W., & Lien, T. C. (2014). Exploring the moderating role of perceived flexibility advantages in mobile learning continuance intention (MLCI). The International Review of Research in Open and Distributed Learning, 15(3). doi: 10.19173/irrodl.v15i3.1722
- Jeng, Y.-L., Wu, T.-T., Huang, Y.-M., Tan, Q., & Yang, S. J. (2010). The add-on impact of mobile applications in learning strategies: A review study. Educational Technology & Society, 13(3), 3-11.
- Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. Thousand Oaks, California: Sage.
- King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. doi: 10.1016/j.im.2006.05.003
- Kukulska-Hulme, A. (2010). Mobile learning as a catalyst for change. Open Learning: The Journal of Open and Distance Learning, 25(3), 181-185.
- Lévy, J. P., Martín, M. T., & Román, M. V. (2006). Optimización según estructuras de covarianzas [Optimization according to covariance structures]. In J. P. Lévy & J. Varela (Eds.), Modelización con estructuras de covarianzas en ciencias sociales (pp. 11-30). Coruña, España: Netbiblo.
- Manzano, J. A., Navarré, C. L., Mafé, C. R., & Blas, S. S. (2011). Análisis de los factores determinantes de la lealtad hacia los servicios bancarios online [Analysis of the determining factors of loyalty to online banking services]. Cuadernos de Economía y Dirección de la Empresa, 14(1), 26-39. doi: 10.1016/j.cede.2011.01.003
- Moreno-Agudelo, J. A., & Valencia-Arias, J. A. (2017). Factores implicados en la adopción de software libre en las Pyme de Medellín [Factors involved in the adoption of free software in the SMEs of Medellín]. Revista CEA, 3(6), 55-75. doi: 10.22430/24223182.673
- Ossiannilsson, E. (2015). Quality enhancement for mobile learning in higher education. In Promoting active learning through the integration of mobile and ubiquitous technologies, (pp. 167-182). Hershey, PA: IGI Global. doi: 10.4018/978-1-4666-6343-5.ch010
- Oviedo, H. C., & Campo-Arias, A. (2005). Aproximación al uso del coeficiente alfa de Cronbach [Approach to the use of Cronbach's Alpha coefficient]. Revista colombiana de psiquiatría, 34(4), 572-580.
- Ozdamli, F., & Cavus, N. (2011). Basic elements and characteristics of mobile learning. Procedia-Social and Behavioral Sciences, 28, 937-942. doi: 10.1016/j.sbspro.2011.11.173
- Pardo, H., & Balestrini, M. (2010). Prototipos de mobile open education: Una breve selección de casos [Prototypes of mobile open education: A brief selection of cases]. IEEE-RITA, 5(4), 125-131.
- Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162.
- Sampedro, I. R., Fernández‐Laviada, A., & Herrero, A. (2014). Entrepreneurial intention: Perceived advantages and disadvantages. Academia Revista Latinoamericana de Administración, 27(2), 284-315.
- Sarrab, M., Al Shibli, I., & Badursha, N. (2016). An empirical study of factors driving the adoption of mobile learning in Omani higher education. The International Review of Research in Open and Distributed Learning, 17(4), 331-349. doi: 10.19173/irrodl.v17i4.2614
- Schifter, D. E., & Ajzen, I. (1985). Intention, perceived control, and weight loss: An application of the theory of planned behavior. Journal of Personality and Social Psychology, 49(3), 843-851. doi: 10.1037/0022-3514.49.3.843
- Schwartz, S. H. (1977). Normative influences on altruism 1. In Advances in experimental social psychology (Vol. 10, pp. 221-279). United States: Academic Press. doi: 10.1016/S0065-2601(08)60358-5
- Serbanescu, L. (2010). Internet: A new way of training. Designing an e-learning platforms. Revista Tinerilor Economisti (The Young Economists Journal), 1(14), 151-158.
- Shin, W. S., & Kang, M. (2015). The use of a mobile learning management system at an online university and its effect on learning satisfaction and achievement. The International Review of Research in Open and Distributed Learning, 16(3). 110-130. doi: 10.19173/irrodl.v16i3.1984
- Snow, C. P. (1993). The Two Cultures. London, UK: Cambridge University Press.
- Spiegel, A., & Rodríguez, G. (2016). Students at university have mobile technologies. Do they do m-learning? Procedia-Social and Behavioral Sciences, 217, 846-850. doi: 10.1016/j.sbspro.2016.02.006
- Surry, D. W., Ensminger, D. C., & Haab, M. (2005). A model for integrating instructional technology into higher education. British Journal of Educational Technology, 36(2), 327-329. doi: 10.1111/j.1467-8535.2005.00461.x
- Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. doi: 10.1287/isre.6.2.144
- UNESCO. (2016). Education for people and planet: Creating sustainable futures for all (2nd ed.; Global Education Monitoring Report). Retrieved from UNESDOC: UNESCO Digital Library website: http://unesdoc.unesco.org/images/0024/002457/245752e.pdf
- Valencia-Arias, A., Chalela-Naffah, S., & Bermúdez-Hernández, J. (2018). A proposed model of e-learning tools acceptance among university students in developing countries. Education and Information Technologies, 24(2), 1-15. doi: 10.1007/s10639-018-9815-2
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. doi: 10.2307/30036540
- Vinu, P. V., Sherimon, P. C., & Krishnan, R. (2011). Towards pervasive mobile learning-the vision of 21st century. Procedia-Social and Behavioral Sciences, 15, 3067-3073. doi: 10.1016/j.sbspro.2011.04.247
- Wallerstein, I. (2011). The modern world-system I: Capitalist agriculture and the origins of the European world-economy in the sixteenth century (Vol. 1). California, USA: University of California Press.
- Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26(4), 323-338. doi: 10.1007/s12525-015-0214-x