Investigating The Drivers and Barriers to MOOC Adoption by Course and Training Institutions
DOI:
https://doi.org/10.59188/eduvest.v4i7.1229Keywords:
Course Institution, E-learning, MOOC, Online Course, VET, VocationalAbstract
The integration of MOOCs into Course and Training Institutions represents a profound shift in the landscape of education delivery and reception. Despite the substantial potential benefits of MOOCs, the adoption process is intricate. This research delves into the essential factors influencing adoption decisions and explores the unique challenges confronted by both adopters and non-adopters. Employing the TAM and TOE theoretical framework, the study utilizes a mixed-methods approach, combining quantitative analysis with qualitative insights from open-ended questions. The findings underscore the critical role of perceived ease of use (PEOU), emphasizing the importance of user-friendly platforms. Additionally, the study recognizes the pivotal influence of Service Quality, Financial Support, and Government Policy in shaping institutional intentions to embrace MOOCs. A comparative analysis between adopters and non-adopters reveals distinctive challenges for each group. Adopter express concerns regarding inadequate government support and promotional efforts affecting platform access. In contrast, non-adopters highlight the necessity for offline training and underscore government-related support and prioritization challenges impacting MOOC adoption.
References
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Klobas, J. E., Mackintosh, B., & Murphy, J. (2014). The anatomy of MOOCs. Massive Open Online Courses: The MOOC Revolution, 1–22.
Ma, L., & Lee, C. S. (2020). Drivers and barriers to MOOC adoption: Perspectives from adopters and non-adopters. Online Information Review, 44(3), 671–684.
Mardiati, M., Saputri, L., Afni, K., & Sitepu, D. R. (2022). The Utilization of Google Classroom on Students’ Self Regulated Learning During The Covid-19 Pandemic. International Journal of Social Service and Research, 2(2), 109–113.
Rai, L., & Chunrao, D. (2016). Influencing factors of success and failure in MOOC and general analysis of learner behavior. International Journal of Information and Education Technology, 6(4), 262.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer.
Scerbakov, N., Schukin, A., & Rezedinova, E. (2023). Architecture of Modern E-Learning Management System. 2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), 1–5.
Sumiarti, E., Rusijono, R., & Mariono, A. (2021). Online Learning Evaluation In Malang City and Batu To Improve SMK Students Competency. Journal of Social Science, 2(4), 356–364.
Suwarno, S., Durhan, D., & Muhaimin, M. (2021). Implementation of Covid-19 on character education. Journal of Social Science, 2(3), 312–319.
Tang, H., & Xing, W. (2022). Massive open online courses for professional certificate programs? Perspectives on professional learners’ longitudinal participation patterns. Australasian Journal of Educational Technology, 38(1), 136–147.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation, Lexington, MA. Lexington books.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 2.
Vululleh, P. (2018). Determinants of students’e-learning acceptance in developing countries: An approach based on Structural Equation Modeling (SEM). International Journal of Education and Development Using ICT, 14(1).
Yang, L. (2023). Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model. Plos One, 18(5), e0284463.
Bordoloi, R., Das, P., & Das, K. (2020). Lifelong learning opportunities through MOOCs in India. Asian Association of Open Universities Journal, 15(1), 83–95.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.
Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Haryanto, I., Setiawan, A., & Djajadikerta, H. (2023). E-Learning: Metode Pembelajaran Masa Depan yang Efektif dan Efisien. Syntax Literate; Jurnal Ilmiah Indonesia, 8(7), 5267–5280.
Henderikx, M., Kreijns, K., Castano Munoz, J., & Kalz, M. (2019). Factors influencing the pursuit of personal learning goals in MOOCs. Distance Education, 40(2), 187–204.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited.
Klobas, J. E., Mackintosh, B., & Murphy, J. (2014). The anatomy of MOOCs. Massive Open Online Courses: The MOOC Revolution, 1–22.
Ma, L., & Lee, C. S. (2020). Drivers and barriers to MOOC adoption: Perspectives from adopters and non-adopters. Online Information Review, 44(3), 671–684.
Mardiati, M., Saputri, L., Afni, K., & Sitepu, D. R. (2022). The Utilization of Google Classroom on Students’ Self Regulated Learning During The Covid-19 Pandemic. International Journal of Social Service and Research, 2(2), 109–113.
Rai, L., & Chunrao, D. (2016). Influencing factors of success and failure in MOOC and general analysis of learner behavior. International Journal of Information and Education Technology, 6(4), 262.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer.
Scerbakov, N., Schukin, A., & Rezedinova, E. (2023). Architecture of Modern E-Learning Management System. 2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), 1–5.
Sumiarti, E., Rusijono, R., & Mariono, A. (2021). Online Learning Evaluation In Malang City and Batu To Improve SMK Students Competency. Journal of Social Science, 2(4), 356–364.
Suwarno, S., Durhan, D., & Muhaimin, M. (2021). Implementation of Covid-19 on character education. Journal of Social Science, 2(3), 312–319.
Tang, H., & Xing, W. (2022). Massive open online courses for professional certificate programs? Perspectives on professional learners’ longitudinal participation patterns. Australasian Journal of Educational Technology, 38(1), 136–147.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation, Lexington, MA. Lexington books.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 2.
Vululleh, P. (2018). Determinants of students’e-learning acceptance in developing countries: An approach based on Structural Equation Modeling (SEM). International Journal of Education and Development Using ICT, 14(1).
Yang, L. (2023). Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model. Plos One, 18(5), e0284463.
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