Xinchao Li1 & Isaac Kwaku Asante2
1Teaching Affairs Office-Jiangsu University, Jingkou District, Zhenjiang, Jiangsu, China
2School of Teacher Education-Jiangsu University, Jingkou District, Zhenjiang, Jiangsu, China
Abstract: The present study examined the relationships among variables of student-centered learning and online international students’ satisfaction in two distinct universities in China (Jiangsu University and Jiangsu University of Science and Technology) after the advent of COVID-19 pandemic. The total participants for this study were two hundred and fifty (250) international students from the two Chinese universities. The student-centered learning hypothesis were verified through structural equation modeling. The results showed that all the four constructs for student-centered learning- course flexibility, mode of collaboration, environment, and curriculum design, predicted online international students’ satisfaction with proximity and program satisfaction are at a statistically significant level. The results provided an empirical student-centered understanding of online international student expectations. Further, it identifies that the universities’ knowledge of online international students’ expectation may help in designing an online educational campaign to tap into possible desires and decision-making around students in choosing online courses and to prepare students for a progressive online learning experience.
Keywords: COVID-19, International Students, Satisfactions, Student-Centered Approach
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
Ashton-Hay, S. (2006). Constructivism and powerful learning environments: create your own!
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.
Balloo, K. (2018). In-depth profiles of the expectations of undergraduate students commencing university: a Q methodological analysis. Studies in Higher Education, 43(12), 2251–2262.
Bettinger, E., Fairlie, R. W., Kapuza, A., Kardanova, E., Loyalka, P., & Zakharov, A. (2020). Does edtech substitute for traditional learning? experimental estimates of the educational production function. National Bureau of Economic Research.
Crane, L., Kinash, S., Bannatyne, A., Judd, M.-M., Eckersley, W., Hamlin, G., … Stark, A. (2016). Engaging postgraduate students and supporting higher education to enhance the 21st century student experience. Final Report 2016. Department of Education and Training.
Furstenberg, G., Levet, S., English, K., & Maillet, K. (2001). Giving a virtual voice to the silent language of culture: The Cultura project. Language Learning & Technology, 5(1), 55–102.
Gewin, V. (2020). Five tips for moving teaching online as COVID-19 takes hold. Nature, 580(7802), 295–296.
Ghazal, S., Al-Samarraie, H., & Aldowah, H. (2018). “I am still learning”: Modeling LMS critical success factors for promoting students’ experience and satisfaction in a blended learning environment. IEEE Access, 6, 77179–77201.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. (2006). Multivariate data analysis . Uppersaddle River. NJ: Pearson Prentice Hall.
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.
Hannafin, M. J., & Kim, M. C. (2003). In search of a future: A critical analysis of research on web-based teaching and learning. Instructional Science, 31(4–5), 347–351.
Hassel, S., & Ridout, N. (2018). An investigation of first-year students’ and lecturers’ expectations of university education. Frontiers in Psychology, 8, 2218.
Hayes, S., Smith, S. U., & Shea, P. (2015). Expanding learning presence to account for the direction of regulative intent: self-, co-and shared regulation in online learning. Online Learning, 19(3), 15–31.
Hu L.-T., & Bentler P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling , 6(July 2012), 1–55.
Lesgold, A. (2004). Contextual requirements for constructivist learning. International Journal of Educational Research, 6(41), 495–502.
Li, L. (2014). Understanding language teachers’ practice with educational technology: A case from China. System, 46, 105–119.
Luo, H., & Yang, C. (2018). Twenty years of telecollaborative practice: implications for teaching Chinese as a foreign language. Computer Assisted Language Learning, 31(5–6), 546–571.
Paulsen, J., & McCormick, A. C. (2020). Reassessing disparities in online learner student engagement in higher education. Educational Researcher, 49(1), 20–29.
Shah, M., & Jarzabkowski, L. (2013). The Australian higher education quality assurance framework: From improvement-led to compliance-driven. Perspectives: Policy and Practice in Higher Education, 17(3), 96–106.
Social Research Center (2019). 2018 Student Experience Survey: national report
Stone, C. (2017). Opportunity through online learning: Improving student access, participation and success in higher education. Perth: The National Centre for Student Equity in Higher Education (NCSEHE), Curtin University.
Stone, C. M. M., & O’Shea, S. E. (2019). My children… think it’s cool that Mum is a uni student: Women with caring responsibilities studying online. Australasian Journal of Educational Technology, 35(6), 97–110.
Su, J., & Waugh, M. L. (2018). Online student persistence or attrition: Observations related to expectations, preferences, and outcomes. Journal of Interactive Online Learning, 16(1), 63–79.
Swan, K. (2003). Learning effectiveness online: What the research tells us. Elements of Quality Online Education, Practice and Direction, 4(1), 13–47.
Sykes, J. (2017). Technologies for teaching and learning intercultural competence and interlanguage pragmatics. The Handbook of Technology and Second Language Teaching and Learning, 119–133.
Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67(3), 749–766.
Walker, S. L., & Fraser, B. J. (2005). Development and validation of an instrument for assessing distance education learning environments in higher education: The Distance Education Learning Environments Survey (DELES). Learning Environments Research, 8(3), 289–308.
Weldy, T. G. (2018). Traditional, blended, or online: Business student preferences and experience with different course formats. E-Journal of Business Education and Scholarship of Teaching, 12(2), 55–62.
Wieser, D., & Seeler, J.-M. (2018). Online, Not Distance Education: The Merits of Collaborative Learning in Online Education. In The Disruptive Power of Online Education. Emerald Publishing Limited.
Wlodkowski, R. J., & Ginsberg, M. B. (2017). Enhancing adult motivation to learn: A comprehensive guide for teaching all adults. John Wiley & Sons.
International Journal of Social Sciences & Educational Studies
ISSN 2520-0968 (Online), ISSN 2409-1294 (Print), March 2021, Vol.8, No.1
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