Abstract
The Technology Acceptance Model (TAM) is regularly used to investigate students’ acceptance of virtual learning environments and the impact on students’ intention to use. Past TAM studies barely investigated students’ actual use of virtual learning environments, neither how actual use relates to their performance in an ecological valid educational context. In this study, a Moodle-based virtual learning environment regarding teaching a foreign language is studied in a teacher education context. The instructional design is based on the four-component instructional design model (4C/ID model). We measured the students’ acceptance of the virtual learning environment using the perceived usefulness (PU) and perceived ease of use scales of TAM. Log data were analysed to detect students’ actual use, and a pretest and posttest was used to assess students’ performance. Structural equation modelling (N = 191) reveals that PU has a significant influence on actual use of the virtual learning environment, and that actual use of the virtual learning environment positively influences students’ performance.
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References
Bell, B. S., & Federman, J. E. (2013). E-learning in postsecondary education. The Future of Children, 23, 165–185. https://doi.org/10.1353/foc.2013.0007.
Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8, 211–218.
Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43, 706. https://doi.org/10.1016/j.im.2006.03.007.
Clarebout, G., & Elen, J. (2006). Tool use in computer-based learning environments: Towards a research framework. Computers in Human Behavior, 22, 389–411. https://doi.org/10.1016/j.chb.2004.09.007.
Clarebout, G., Horz, H., Schnotz, W., & Elen, J. (2010). The relation between self-regulation and the embedding of support in learning environments. Educational Technology Research and Development, 58, 573–587. https://doi.org/10.1007/s11423-009-9147-4.
Czerkwaski, C., & Leyman, E. (2016). An instructional design framework for fostering student engagement in online learning environments. Technology Trends, 60(6), 532–539. https://doi.org/10.1007/s11528-016-0110-z.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340. https://doi.org/10.2307/249008.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal Man-Machine Studies, 38, 475–487. https://doi.org/10.1006/imms.1993.1022
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1002. https://doi.org/10.1287/mnsc.35.8.982.
Elen, J. (2004). Turning electronic learning environments into useful and influential “instructional design anchor points”. Educational Technology Research and Development, 52, 67–73. https://doi.org/10.1007/BF02504719.
Evens, M., Elen, J., & Depaepe, F. (in press). Effects of opportunities to learn in teacher education on the development of teachers’ professional knowledge of French as a foreign language. Journal of Advances in Education Research.
Fischer, G. (2014). Beyond hype and underestimation: identifying research challenges for the future of MOOCs. Distance Education, 35(2), 149–158. https://doi.org/10.1080/01587919.2014.920752
Fishbein, M., & Ajzen, I. (1975). Belief, attitudes, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Frick, T. W., Chadha, R., Watson, C., & Zlatkovska, E. (2010). Improving course evaluations to improve instruction and complex learning in higher education. Educational Technology Research and Development, 58, 115–136. https://doi.org/10.1007/s11423-009-9131-z.
Herrington, J., Oliver, R., & Reeves, T. C. (2003). Patterns of engagement in authentic online learning environments. Australasian Journal of Educational Technology, 19, 59–71. https://doi.org/10.14742/ajet.1701.
Huang, H., Rauch, U., & Liaw, S. (2010). Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach. Computers & Education, 55(3), 1171–1182. https://doi.org/10.1016/j.compedu.2010.05.014.
Islam, N. (2013). Investigating e-learning system usage outcomes in the university context. Computers & Education, 69, 387–399. https://doi.org/10.1016/j.compedu.2013.07.037.
Jaggers, S., & Xu, D. (2016). How do online course design features influence student performance? Computers & Education, 95, 270–284. https://doi.org/10.1016/j.compedu.2016.01.014.
Johnson, S. D., & Aragon, S. R. (2003). An instructional strategy framework for online learning environments. New directions for Adult and Continuing Education, 10, 31–43. https://doi.org/10.1002/ace.117.
Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94.
Juarez Collazo, J. N. A., Wu, X., Elen, J., & Clarebout, G. (2014). Tool use in computer-based learning environments: Adopting and extending the Technology Acceptance Model. Hindawi Publishing Corporation, 2014, 1–10. https://doi.org/10.1155/2014/736931.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003.
Kline, M. S. (2013). Application of Structural Equation Modeling in Educational Research and Practice. Rotterdam, NL: Sense Publishers.
Lau, S., & Woods, P. C. (2009). Understanding learner acceptance of learning objects: The re-learning environments of learning object characteristics and individual differences. British Journal of Educational Technology, 40, 1059–1075. https://doi.org/10.1111/j.1467-8535.2008.00893.x.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? Critical review of the technology acceptance model. Information & Management, 40, 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4.
Lust, G., Juarez Collazo, N., Elen, J., & Clarebout, G. (2012). Content management systems: Enriched learning opportunities for all? Computers in Human Behavior, 28, 795–808. https://doi.org/10.1016/j.chb.2011.12.009.
Martens, R., Bastiaens, T., & Kirschner, P. A. (2007). New learning design in distance education: The impact on student perception and motivation. Distance Education, 28, 81–93. https://doi.org/10.1080/01587910701305327.
McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52, 496–508. https://doi.org/10.1016/j.compedu.2008.10.002.
Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50, 43–59. https://doi.org/10.1007/BF02505024.
Nunnally, J. (1978). Psychometric theory. New York: McGraw-Hill.
Revere, L., & Kovach, J. V. (2011). Online technologies for engaged learning, a meaningful synthesis for educators. The Quarterly Review of Distance education, 12(113–124), 149–150.
Rienties, B., & Toetenel, L. (2016). The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior, 60, 333–341. https://doi.org/10.1016/j.chb.2016.02.074.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.
Sarfo, F. K., & Elen, J. (2007). Developing technical expertise in secondary technical schools: the effect of the 4C/ID learning environment. Learning Environment Research, 10(3). https://doi.org/10.1007/s10984-007-9031-2
Savalei, V., & Bentler, P. (2009). A two-stage approach to missing data: Theory and application to auxiliary variables. Structural Equation Modeling: A Multidisciplinary Journal, 16, 477–497. https://doi.org/10.1080/10705510903008238.
Schepers, J., & Wetzels, J. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderating effects. Information & Management, 44, 90–103. https://doi.org/10.1016/j.im.2006.10.007.
Schmuckler, M. A. (2001). What is ecological validity? A dimensional analysis. Infancy, 2, 419–436. https://doi.org/10.1207/S15327078IN0204_02.
Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers & Education, 40, 343–360. https://doi.org/10.1016/S0360-1315(02)00142-2.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49, 396–431. https://doi.org/10.1016/j.compedu.2005.09.004.
Slavin, R. (2003). Educational psychology: Theory and practice. Boston: Pearson Education.
Song, L., Singleton, E. S., Hill, J. R., & Koh, H. M. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. Internet and Higher Education, 7, 59–70. https://doi.org/10.1016/j.iheduc.2003.11.003.
Šumak, B., Hericko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27, 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005.
Tarhini, A., Hone, K., & Xiaohui, L. (2013). Factors affecting students’ acceptance of e-learning environment in developing countries: A structural equation modeling approach. International Journal of Information and Educational Technology, 3, 54–59. https://doi.org/10.7763/IJIET.2013.V3.233.
Teo, T. (2009). Is there an attitude problem? Reconsidering the re-learning environment of attitude in TAM. British Journal of Educational Technology, 40, 1139–1141. https://doi.org/10.1111/j.1467-8535.2008.00913.x.
Teo, T., & Zhou, M. (2016). The influence of teachers’ conceptions of teaching and learning on their technology acceptance. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2016.1143844
Terras, M. M., & Ramsay, J. (2014). Massive open online courses (MOOCs): Insights and challenges from a psychological perspective. British Journal of Educational Technology, 46, 472–487. https://doi.org/10.1111/bjet.12274.
Thompson, R., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15, 125–143. https://doi.org/10.2307/249443.
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52, 463–479. https://doi.org/10.1016/j.infsof.2009.11.005.
Van Gog, T., Sluijsmans, D. M. A., Joosten-ten Brinke, D., & Prins, F. J. (2008). Formative assessment in an online learning environment to support flexible on-the-job learning in complex professional domains. Educational Technology Research and Development, 58, 311–324. https://doi.org/10.1007/s11423-008-9099-0.
Van Merriënboer, J. J. G. (1997). Training complex cognitive skills: A four-component instructional design model for technical training. Englewood Cliffs, NJ: Educational Technology Publications.
Van Merriënboer, J. J. G. (2002). Bleuprints for complex learning: The 4C/ID-model. Educational Technology Research and Development, 50, 39–61. https://doi.org/10.1007/BF02504993.
Van Merriënboer, J. J. G., & Kirschner, P. A. (2007). Ten steps to complex learning: A systematic approach to four-component instructional design. Mahwah, NJ: Lawrence Erlbaum Associates.
Van Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learners’ mind: Instructional design for complex learning. Educational Psychologist, 38, 5–13. https://doi.org/10.1207/S15326985EP3801_2.
Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838–852.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Journal Management Science, 46, 186–204. https://doi.org/10.1016/j.compedu.2006.09.001.
Venkatesh, V., Morris, M. G., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
Younis Alsabawy, A., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness in online learning systems. Computer in Human Behavior, 64, 843–858. https://doi.org/10.1016/j.chb.2016.07.065.
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Larmuseau, C., Evens, M., Elen, J. et al. The Relationship Between Acceptance, Actual Use of a Virtual Learning Environment and Performance: An Ecological Approach. J. Comput. Educ. 5, 95–111 (2018). https://doi.org/10.1007/s40692-018-0098-9
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DOI: https://doi.org/10.1007/s40692-018-0098-9