DETERMINANTS INFLUENCING THE LEARNING SATISFACTION OF ONLINE ART EDUCATION FOR POSTGRADUATES IN PUBLIC UNIVERSITIES IN SICHUAN, CHINA
Keywords:
Online Education, System Quality, Information Quality, Service Quality, Learning SatisfactionAbstract
This study aims to measure determinants influencing learning satisfaction art graduates with their online education are during the COVID-19 era at public universities in Sichuan Province. The conceptual framework includes system quality, information quality, service quality, self-efficacy, perceived utility, perceived ease of use, and learning satisfaction. The investigation and research methods involve judgmental, stratified random, and convenience sampling. The target population is 496 participants who are postgraduate students. In order to analyze the data, the researcher utilized confirmatory factor analysis and structural equation modeling to evaluate the hypotheses and the relationships between the variables. The findings demonstrate that all hypotheses are supported. This study can therefore be applied to enhance students’ learning efficiency and satisfaction in Sichuan public universities’ online programs for art majors.
References
Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy: Online learning usage in Yemen. The International Journal of Information and Learning Technology, 35(4), 285-304. https://doi.org/10.1108/ijilt-11-2017-0116
Arbuckle, J. L. (1995). AMOS for Windows Analysis of Moment Structures. (1st ed.). Version 3.5. Small Waters Corp.
Arbuckle, J. L., & Wothke, W. (2008). AMOS 18.0 update to the AMOS user’s guide. (1st ed.). Amos Development Corporation.
Baber, H. (2021). Social Interaction and Effectiveness of the Online Learning – A Moderating Role of Maintaining Social Distance During the Pandemic COVID-19. Asian Education and Development Studies, 11(1), 159-171.
Chan, A. K. W., Ngan, L. L.-S., Wong, A. K. W., & Chan, W. S. (2017). 'Border' matters in discussions of cross-border students. Social Transformations in Chinese Societies, 13(1), 56-70. https://doi.org/10.1108/stics-04-2017-0005
Chang, C. C. (2013). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. https://doi.org/10.1108/01435121311298261
Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390.
Cheng, Y. M. (2014). Exploring the Intention to Use Mobile Learning: The Moderating Role of Personal Innovativeness. Journal of Systems and Information Technology, 16(1), 40-61.
Chughtai, A. (2018). Authentic Leadership, Career Self-Efficacy and Career Success: A Cross-sectional Study. Career Development International, 23(6/7), 595-607.
Eom, S. B. (2012). Effects of LMS, Self-efficacy, and Self-Regulated Learning on LMS Effectiveness in Business Education. Journal of International Education in Business, 5(2). 129-144.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th ed.). Prentice Hall.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis, (6th ed.). Upper Saddle River, NJ: Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariant Data Analysis. (1st ed.). Pearson International Edition.
Hair, J. F., Marko, S., Lucas, H., & Volker, K. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool for Business Research. European Business Review, 26(2), 106-121.
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modeling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
Huang, H. M., & Liaw, S. (2018). An Analysis of Learners Intentions Toward Virtual Reality Learning Based on Constructivist and Technology Acceptance Approaches. International Review of Research in Open and Distributed Learning, 19(1), 1-25.
Ifinedo, P. (2017). Students’ Perceived Impact of Learning and Satisfaction with Blogs. The International Journal of Information and Learning Technology, 34(4), 322-337.
Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: A guide to the program and applications. (1st ed.) SPSS.
Kilic, E., Güler, C., & Çelik, H. E. (2015). Learning with Interactive Whiteboards. Determining the Factors on Promoting Interactive Whiteboards to Students by Technology Acceptance Model. Interactive Technology and Smart Education, 12(4), 285-297.
Kitcharoen, S. (2018). User Satisfaction with Learning Management System (LMS): A Case of Assumption University. AU-GSB E-JOURNAL, 11(2), 20-39.
Li, W. (2021). Online Art Education Under the Epidemic Situation “Taking Xi” an Academy of Fine Arts as an Example. Northwest fine arts, 1(27), 121-123.
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31(4), 705-737.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149. https://doi.org/10.1037/1082-989x.1.2.130
Masrek, M. N., & Gaskin, J. E. (2016). Assessing User’s Satisfaction with Web Digital Library: The Case of Universiti Teknologi MARA. The International Journal of Information and Learning Technology, 33(1), 36-56.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
Ramlall, I. (2017). Economics and Finance in Mauritius: A modern perspective (1st ed.). Springer.
Ren, S., & Chadee, D. (2017). Ethical Leadership, Self-efficacy and Job Satisfaction in China: The Moderating Role of Guanxi. Personnel Review, 46(2), 371-388.
Roca, J. C., Chiu, C. M., & López, F. J. M. (2006). Understanding E-Learning Continuance Intention: An Extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003
Salkind, N. J. (2010). Encyclopedia of Research Design (Vols. 1-0). SAGE Publications, https://doi.org/10.4135/9781412961288
Sánchez, R. A., Hueros, A. D., & Ordaz, M. G. (2013). E-learning and the University of Huelva: A Study of WebCT and the Technological Acceptance Model. Campus-Wide Information Systems, 30(2), 135-160.
Sharma, S. K., Chandel, J. K., & Govindaluri, S. M. (2014). Students’ Acceptance and Satisfaction of Learning Through Course Websites. Education, Business and Society Contemporary Middle Eastern Issues, 7(2/3), 152-166.
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. www.danielsoper.com/statcalc/default.aspx
Sun, X. (2019). Designing Mobile Learning to Create Active Learning and Just-in-time Learning Experience. Proceedings of EdMedia + Innovate Learning, 20(3), 1882-1884.
Wang, D. D., Wang, H. B., Zhang, W., Wang, H. R., & Shen, X. P. (2020). Research on Online Teaching and Learning in the Period of "School Closure and Non-stop Learning". Based on a Nationwide Survey of 33240 Online Questionnaires. Modern Educational Technology, 30(3), 1-30.
Wang, Q., Zhu, Z., Chen, L., & Yan, H. (2009). E-learning in China. Campus-Wide Information Systems, 26(2), 77-81. https://doi.org/10.1108/10650740910946783
Wang, Y.-S., Li, H.-T., Li, C.-R., & Wang, C. (2014). A model for assessing blog-based learning systems success. Online Information Review, 38(7), 969-990. https://doi.org/10.1108/oir-04-2014-009
Wu, J., & Wang, Y. (2006). Measuring KMS Success: A specification of the DeLone and McLean’s model, Information and Management, 43(6), 728-739.
Xie, F. (2020). Current Situation Analysis and Development Countermeasures of Online Open Curriculum Construction in Art Colleges. Scientific consultation (education and scientific research), 4(1), 79.
Yu, C. S. (2012). Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the UTAUT Model. Journal of Electronic Commerce Research, 13(2), 104–123.
Yuce, A., Abubakar, A. M., & Ilkan, M. (2019). Intelligent Tutoring Systems and Learning Performance Applying Task-technology Fit and IS Success Model. Online Information Review, 43(4), 600-616.
Zhao, X. Y., Mattila, A. S., & Tao, L. S. E. (2008). The Role of Post-Training Self-efficacy in Customers Use of Self-service Technologies. International Journal of Service Industry Management, 19(4), 492-505.
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