Investigation of the Factors Affecting Open and Distance Education Learners’ Intentions to Use a Virtual Laboratory
DOI:
https://doi.org/10.19173/irrodl.v22i2.5076Keywords:
virtual laboratories, open learning, distance education, technology acceptanceAbstract
Laboratories, which are an integral part of education in disciplines that require hands-on training and application, can now be presented using new technologies, and application activities can be realized at a distance. In this study, virtual laboratories (VLs) are discussed, and factors affecting the students’ intention to use VLs are investigated. The study was conducted within laboratory applications of circuit analysis within an associate degree program of a distance teaching university in Turkey. In this study, which used exploratory sequential design approach, the learners’ intentions to use a VL were examined within the framework of the technology acceptance model (TAM). Content analysis was used for the analysis of qualitative data, and the partial least squares structural equation model was used for the analysis of quantitative data. As a result of the study, the developed TAM-based research model is a useful conceptual framework towards understanding and explaining the intentions of learners’ virtual laboratory usage. The results of this study will guide institutions to integrate VLs effectively into the education process and to increase and disseminate the use of VLs by learners.
References
Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
Camarero, C., Rodríguez, J., & José, R. S. (2012). An exploratory study of online forums as a collaborative learning tool. Online Information Review, 36(4), 568–586. https://doi.org/10.1108/14684521211254077
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
Dalgarno, B. (2002). The potential of 3D virtual learning environments: A constructivist analysis. Electronic Journal of Instructional Science and Technology, 5(2), 1–19. https://researchoutput.csu.edu.au/en/publications/the-potential-of-3d-virtual-learning-environments-a-constructivis
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. http://www.jstor.org/stable/2632151
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210–232. https://www.merlot.org/merlot/viewMaterial.htm?id=1053019
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and atatistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Hofstein, A., & Lunetta, V. N. (1982). The role of the laboratory in science teaching: Neglected aspects of research. Review of Educational Research, 52(2), 201–217. https://doi.org/10.2307/1170311
Hung, J. F., & Tsai, C. Y. (2020). The effects of a virtual laboratory and meta-cognitive scaffolding on students’ data modeling competences. Journal of Baltic Science Education, 19(6), 923–939. https://doi.org/10.33225/jbse/20.19.923
Iqbal, S., & Bhatti, Z. A. (2015). An investigation of university student readiness towards M-learning using technology acceptance model. International Review of Research in Open and Distributed Learning, 16(4). https://doi.org/10.19173/irrodl.v16i4.2351
Kang, M., & Shin, W. S. (2015). An empirical investigation of student acceptance of synchronous e-learning in an online university. Journal of Educational Computing Research, 52(4), 475–495. https://doi.org/10.1177/0735633115571921
Kennepohl, D. K. (2013). Learning from blended chemistry laboratories. In K. S. Iyer (Ed.), 2013 IEEE Fifth International Conference on Technology for Education (pp. 135–138). https://doi.org/10.1109/T4E.2013.40
Kennepohl, D. K. (2017). Providing effective teaching laboratories at an open university. International Journal on Innovations in Online Education, 1(4). https://doi.org/10.1615/IntJInnovOnlineEdu.2017021513
Khor, E. T. (2014). An analysis of ODL student perception and adoption behavior using the technology acceptance model. International Review of Research in Open and Distance Learning, 15(6), 275–288.
Meester, M. A. M., & Kirschner, P. A. (1995). Practical work at the Open University of the Netherlands. Journal of Science Education and Technology, 4(2), 127–140. https://doi.org/10.1007/BF02214053
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS (Version 3) [Computer software]. SmartPLS. http://www.smartpls.com
Stefanovic, M. (2013). The objectives, architectures and effects of distance learning laboratories for industrial engineering education. Computers & Education, 69, 250–262. https://doi.org/10.1016/j.compedu.2013.07.011
Sun, H. M., & Cheng, W. L. (2009). The input-interface of webcam applied in 3D virtual reality systems. Computers & Education, 53(4), 1231–1240. https://doi.org/10.1016/j.compedu.2009.06.006
Tan, P. J. B. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a second language): Taiwanese citizens’ acceptance and use of the modular object-oriented dynamic learning environment. Social Science Information, 54(2), 211–228. https://doi.org/10.1177/0539018414566670
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://www.jstor.org/stable/2634758
Wolski, R., & Jagodzinski, P. (2019). Virtual laboratory—Using a hand movement recognition system to improve the quality of chemical education. British Journal of Educational Technology, 50(1), 218–231. https://doi.org/10.1111/bjet.12563
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