Analysis of MOOC User Adoption Patterns Based on the Diffusion of Innovations Theory
DOI: https://doi.org/10.62381/H251802
Author(s)
Yixin Zhu*, Zhiruo Zhang
Affiliation(s)
Shanghai Publishing and Printing College, Shanghai, China
*Corresponding Author
Abstract
Research on the diffusion of smart education technologies is critical for enhancing their implementation in higher education contexts. As an innovation within smart education, Massive Open Online Course (MOOC) platforms have been extensively adopted and promoted across higher education institutions, enabling the global exchange and dissemination of educational resources. This study centers on MOOCs, employing the theoretical framework of technological innovation diffusion to investigate their diffusion trajectories and to systematically analyze the characteristics of MOOC adopters. The results indicate that from 2014 to 2024, the diffusion of MOOCs conformed to an S-curve model. In China, MOOC users are presently transitioning from the “early majority” phase to the “late majority” phase. During this diffusion process, the “early majority” and “late majority” groups constituted the largest proportions of adopters, with university faculty and students identified as the principal agents driving diffusion. In sum, smart education technologies have attained extensive adoption within the educational sector.
Keywords
Technology Diffusion; S-Curve; MOOC; Diffusion Characteristics
References
[1] Rogers E M. Diffusion of Innovations, 5th edition. Simon and Schuster, 2003.
[2] Zhu zhiting, Peng hongdao, Lei yunhe. Intelligence education: Practical an approach to smarter education. Open education research, 2018, 24(04): 1324+42.
[3] Yang xianmin Yu Shengquan.The Architecture and Key Support Technologies of Smart Education. China Educational Technology, 2015(1): 77-84,130.
[4]Zhang Jinbao. Research on the Diffusion of Educational Technology Innovation. Beijing: Beijing University of Posts and Telecommunications Press. 2013.
[5]Xie XiaoHui. Research in adoption and factors of K12onlineeducation by Urban families. Jiangxi Normal University, 2020.
[6] Zhao Lei. Construction and Enlightenment of Influencing Factors Model of the Diffusion and Sharing of Massive Open Online Courses (MOOCs) Based on lSM: Strategies of Advancing Continuous Application of Online Courses Resources in Colleges and Universities. Meitan Higher Education, 2020, 38(04): 49-55.
[7] Zhao Ying, Yang Ge, Luo Xuan. An Investigation of University Students Students' Acceptance of Acceptance MoOCs and Usage Behavior. Chinese Journal of Distance Education, 2015(8): 37-44+80.
[8] Mehmood Y, Barbieri N, Bonchi F. Modeling adoptions and the stages of the diffusion of innovations. Knowledge and Information Systems, 2016, 1-27.
[9] Kannan Aadharsh, Lariviere Jacob, Mcafee R. Preston. Characterizing the Usage Intensity of Public Cloud. Acm Transactions on Economics and Computation, 2021, 9(3).
[10] Yan ZhiMing, Wang ShiChun. Research on Innovation Diffusion in Educational Informatization. China Educational Technology, 2008(12): 1-5.
[11] Zhai Y, Ding Y, Wang F. Measuring the diffusion of an innovation: A citation analysis. Journal of the Association for Information Science & Technology, 2017, 69(3): 368–379.
[12] Song Ge. The Diffusion Process of Academic Innovation. Journal of Library Science in China, 2015(1): 62-75.