Analisis Penerimaan Teknologi Zoom dalam Pembelajaran Online Mahasiswa Selama Covid-19: Pendekatan Technology Acceptance Model (TAM)
Abstract
Penggunaan teknologi Zoom sebagai media pembelajaran online sudah banyak digunakan di berbagai lembaga pendidikan khususnya ketika pandemic Covid-19. Sudah hampir dua tahun lebih pembelajaran di perguruan tinggi vokasi menggunakan mode online dengan platfrom Zoom. Namun, hingga kini masih terbatas studi yang mengevaluasi bagaimana pengaruh faktor eksternal yang meliputi pengaruh dosen, teman sejawat, dan dukungan institusi terhadap niat belajar online mahasiswa politeknik menggunakan Zoom. Oleh karena itu, penelitian ini bertujuan untuk menganalisis model pembentukan niat mahasiswa menggunakan Zoom untuk belajar online dengan melibatkan faktor anteseden yang meliputi pengaruh dosen, teman sejawat, dukungan institusi, perceived ease of use, dan perceived usefulness. Penelitian ini melibatkan mahasiswa politeknik untuk mengungkapkan persepsi mereka tentang pengaruh dosen, teman sejawat, dukungan institusi, perceived ease of use, perceived usefulness, dan niat menggunakan Zoom untuk belajar online. Penelitian ini menggunakan analisis SEM (structural equation modeling) berbasis Partial Least Square (PLS). Hasil studi menunjukkan bahwa social norms, perceived ease of use, dan perceived usefulness mempengaruhi niat mahasiswa menggunakan video conferencing technology (VCT) dari Zoom untuk pembelajaran online mereka. Secara khusus, social norm memiliki pengaruh positif terhadap perceptions of perceived ease of use dan perceived usefulness. Selain itu, perceived ease of use menggunakan zoom pada pembelajaran online juga mempengaruhi secara positif terhadap perceived usefulness. Temuan lainnya juga menunjukkan bahwa perceived ease of use maupun perceived usefulness memiliki pengaruh terhadap niat mahasiswa menggunakan Zoom untuk belajar online. Dan terakhir, perceived ease of use memediasi pengaruh social norm terhadap niat mahasiswa menggunakan Zoom untuk belajar online.
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Alassafi, M. O. (2021). E-learning intention material using TAM: A case study. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2021.09.457
Cheng, B., Wang, M., Moormann, J., Olaniran, B. A., & Chen, N.-S. (2012). The effects of organizational learning environment factors on e-learning acceptance. Computers & Education, 58(3), 885–899. https://doi.org/https://doi.org/10.1016/j.compedu.2011.10.014
Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296–325.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems. https://doi.org/10.2307/249008
Davis, H. C., & Fill, K. (2007). Embedding blended learning in a university’s teaching culture: Experiences and reflections. British Journal of Educational Technology, 38(5), 817–828. https://doi.org/https://doi.org/10.1111/j.1467-8535.2007.00756.x
Efron, B., & Tibshirani, R. J. (1998). An introduction to the bootstrap. New York: Chapman & Hall / CRC.
Estriegana, R., Medina-Merodio, J.-A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1–14. https://doi.org/https://doi.org/10.1016/j.compedu.2019.02.010
Faraday, S., Overton, C., & Cooper, S. (2011). Effective teaching and learning in vocational education. https://doi.org/http://www.skillsdevelopment.org/pdf/Effective_teaching_and_learning_in_VE_report.pdf
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
Fawaz, M., & Samaha, A. (2021). E-learning: Depression, anxiety, and stress symptomatology among Lebanese university students during COVID-19 quarantine. Nursing Forum, 56(1), 52–57. https://doi.org/https://doi.org/10.1111/nuf.12521
Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating Cognitive Presence in Online Learning: Interaction Is Not Enough. American Journal of Distance Education, 19(3), 133–148. https://doi.org/10.1207/s15389286ajde1903_2
Ghozali, I. (2006). Structural Equation Modeling metode alternatif dengan Partial Least Square. Semarang: Badan Penerbit Universitas Diponegoro.
Ghozali, I. (2017). Structural equation models: Concepts and applications with the AMOS 24 bayesian SEM update program (Indonesian version) (Edisi 7). Semarang: Badan Penerbit Universitas Diponegoro.
Ghozali, I., & Fuad. (2008). Structural Equation Modeling: Teori, konsep dan aplikasi dengan Pprogram lisrel 8.0. Semarang: Badan Penerbit Universitas Diponegoro.
Guri-Rosenblit, S. (2018). E-teaching in higher education: an essential prerequisite for e learning. Journal of New Approaches in Education Research, 7(2), 93–97. Retrieved from https://naerjournal.ua.es/article/view/v7n2-3
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th.). Upper Saddle River: Pearson Prentice Hall.
Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123. Retrieved from http://www.jstor.org/stable/45018532
Hartono, J., & Abdillah, W. (2009). Konsep dan Aplikasi PLS (Partial Least Square) untuk Penelitian Empiris. Yogyakarta: BPFE.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London, UK: Routledge.
Hiltz, S. R., Coppola, N., Rotter, N., Turoff, M., & Benbunan-Fich, R. (2000). Measuring the Importance of Collaborative Learning for the Effectiveness of ALN: A Multi-Measure, Multi-Method Approach. Journal of Asynchronous Learning Networks, 4, 124+. Retrieved from https://link.gale.com/apps/doc/A284451526/AONE?u=googlescholar&sid=googleScholar&xid=003caa0a
Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68(1), 575–591. https://doi.org/10.1007/s11423-019-09695-y
Isaac, S., & Michael, W. B. (1981). Handbook in research and evaluation. California: Edits Publisher.
Jang, M., Aavakare, M., Nikou, S., & Kim, S. (2021). The impact of literacy on intention to use digital technology for learning: A comparative study of Korea and Finland. Telecommunications Policy, 45(7), 102154. https://doi.org/https://doi.org/10.1016/j.telpol.2021.102154
Kimathi, F. A., & Zhang, Y. (2019). Exploring the General Extended Technology Acceptance Model for e-Learning Approach on Student’s Usage Intention on e-Learning System in University of Dar es Salaam. Creative Education, 10(1), 208–223. https://doi.org/10.4236/ce.2019.101017.
Kunter, M., & Voss, T. (2013). The model of instructional quality in COACTIV: A multicriteria analysis. In M. Kunter, J. Baumert, W. Blum, U. Klusmann, S. Krauss, & M. Neubrand (Eds.), Cognitive activation in the mathematics classroom and professional competence of teachers. Results from the COACTIV project (pp. 97–124). New York: Springer.
Lee, A., & Bailey, D. (2016). Korean EFL Students’ Perceptions of Instructor Video and Written Feedback in a Blended Learning Course. STEM Journal, 17, 133–158. https://doi.org/10.16875/stem.2016.17.4.133
Lin, H.-M., Chen, W.-J., & Nien, S.-F. (2014). The study of achievement and motivation by e-learning–A case study. International Journal of Information and Education Technology, 4(5), 421–425.
Mahfud, T., Indartono, S., Saputro, I. N., & Utari, I. (2019). The effect of teaching quality on student career choice: The mediating role of student goal orientation. Integration of Education, 23(4). https://doi.org/10.15507/1991-9468.097.023.201904.541-555
Malik, M., & Javed, S. (2021). Perceived stress among university students in Oman during COVID-19-induced e-learning. Middle East Current Psychiatry, 28(1), 49. https://doi.org/10.1186/s43045-021-00131-7
Matsumura, L. C., Garnier, H., Pascal, J., & Valdes, R. (2002). Measuring Instructional Quality in Accountability Systems: Classroom Assignments and Student Achievement. Educational Assessment, 8(3), 207–229. https://doi.org/10.1207/S15326977EA0803_01
McLaren, B. M., Richey, J. E., Nguyen, H., & Hou, X. (2022). How instructional context can impact learning with educational technology: Lessons from a study with a digital learning game. Computers & Education, 178, 104366. https://doi.org/https://doi.org/10.1016/j.compedu.2021.104366
Muijs, D., & Reynolds, D. (2007). Effective teaching: Evidence and practice. London: Sage.
Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512–520. https://doi.org/https://doi.org/10.1016/j.procs.2021.12.168
Naylor, R., & Sayed, Y. (2014). Teacher quality: Evidence review. Canberra, Australia.
Nilsen, T., & Gustafsson, J.-E. (2016). Teacher Quality, Instructional Quality and Student Outcomes: Relationships Across Countries, Cohorts and Time. Springer Open.
Ochoa, A. A. C., Thomas, P. S., Tikly, P. L., & Doyle, H. (2018). Scan of international approaches to teacher assessment. Bristol, England.
Praetorius, A.-K., Pauli, C., Reusser, K., Rakoczy, K., & Klieme, E. (2014). One lesson is all you need? Stability of instructional quality across lessons. Learning and Instruction, 31, 2–12. https://doi.org/https://doi.org/10.1016/j.learninstruc.2013.12.002
Salmon, G. (2003). E-moderating: The key to teaching and learning online. Psychology Press.
Sánchez-Cabrero, R., Casado-Pérez, J., Arigita-García, A., Zubiaurre-Ibáñez, E., Gil-Pareja, D., & Sánchez-Rico, A. (2021). E-Assessment in E-Learning Degrees: Comparison vs. Face-to-Face Assessment through Perceived Stress and Academic Performance in a Longitudinal Study. Applied Sciences , Vol. 11. https://doi.org/10.3390/app11167664
Song, Y., & Kong, S.-C. (2017). Investigating Students’ Acceptance of a Statistics Learning Platform Using Technology Acceptance Model. Journal of Educational Computing Research, 55(6), 865–897. https://doi.org/10.1177/0735633116688320
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), e05410. https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e05410
Üzel, D., & Özdemir, E. (2012). The Effects of Problem-Based E-Learning on Prospective Teachers’ Achievements and Attitudes towards Learning Mathematics. Procedia - Social and Behavioral Sciences, 55, 1154–1158. https://doi.org/https://doi.org/10.1016/j.sbspro.2012.09.609
Valaei, N. (2017). Organizational structure, sense making activities and SMEs’ competitiveness. VINE Journal of Information and Knowledge Management Systems, 47(1), 16–41. https://doi.org/10.1108/VJIKMS-04-2016-0015
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science. https://doi.org/10.1287/mnsc.46.2.186.11926
Wardoyo, C., Satrio, Y. D., Narmaditya, B. S., & Wibowo, A. (2021). Do technological knowledge and game-based learning promote students achievement: lesson from Indonesia. Heliyon, 7(11), e08467. https://doi.org/https://doi.org/10.1016/j.heliyon.2021.e08467
Weng, F., Jouyang, R., Ho, H. J., & Su, H. M. (2018). A study of elementary school teachers’ intention to use multimedia materials based on the technology acceptance model-A case study of elementary Schools in Chiayi County. Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017, 98–100. https://doi.org/10.1109/ICICE.2017.8478890
Zheng, J., & Li, S. (2020). What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102, 101612. https://doi.org/https://doi.org/10.1016/j.ijer.2020.101612
DOI: https://doi.org/10.32487/jshp.v7i1.1620
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