Key Factors Shaping Students' E-Learning Satisfaction in Higher Education: A Study in Chengdu, China
Keywords:
Service Quality, Information Quality, Learner Quality, Perceived Usefulness, Student SatisfactionAbstract
Purpose: This research aimed to enhance student satisfaction by fostering social presence, quality of service, information, learner, and perceived usefulness. Research Design, Data, and Methodology: The sample was 80 full-time students from the three main colleges of Xihua University. The change level was determined by conducting the same questionnaire before and after the strategic plan (SP). The data was analyzed, and the hypotheses were tested using mixed methods research. Experts conducted the item-objective congruence (IOC) test and pilot test, and multiple linear regression (MLR) indicated that mean values change significantly for those six variables between pre- and post-SP. The strategic plans included team establishment, goal setting, SWOT analysis, policy support, and course program. Results: Findings from qualitative research methods proved improvements in all variables between the pre-and post-SP. Additionally, innovative approaches to improving student satisfaction are discussed, along with recommendations and limitations for future research. Conclusions: Learners' attitudes and self-discipline play crucial roles in influencing satisfaction with online learning. A negative learning attitude, reluctance to engage in online platforms, a tendency to passively absorb information in traditional settings, and a lack of initiative are key factors that diminish satisfaction in online learning environments.
References
Al-Fraihat, D., Joy, M., & Sinclair, J. (2017). Identifying success factors for e-learning in higher education. Academic Conferences International Limited.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in human behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004
Alkhattabi, M., Neagu, D., & Cullen, A. (2011). Assessing information quality of e-learning systems: a web mining approach. Computers in Human Behavior, 27(2), 862-873. https://doi.org/10.1016/j.chb.2010.11.011
Arbaugh, J. B. (2000). How classroom environment and student engagement affect learning in Internet-based MBA courses. Business Communication Quarterly, 63(4), 9-26.
https://doi.org/10.1177/108056990006300402
Candra, S., & Jeselin, F. S. (2024). Students' perspectives on using e-learning applications and technology during the COVID-19 pandemic in Indonesian higher education. Journal of Science and Technology Policy Management, 15(2), 226-243. https://doi.org/10.1108/JSTPM-12-2021-0185
Cheng, Y. M. (2023). What roles do quality and cognitive absorption play in evaluating cloud-based e-learning system success? Evidence from medical professionals. Interactive Technology and Smart Education, 20(2), 228-256. https://doi.org/10.1108/ITSE-12-2021-0222
Cronin, J. J., & Taylor, S. A. (1992). Measuring service quality: a reexamination and extension. Journal of marketing, 56(3), 55-68. https://doi.org/10.1177/002224299205600304
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
Dorobăţ, I., Corbea, A. M. I., & Muntean, M. (2019). Integrating student trust in a conceptual model for assessing learning management system success in higher education: An empirical analysis. IEEE Access, 7, 69202-69214. http://doi: 10.1109/ACCESS.2019.2919100
Elfaki, N. K., Abdulraheem, I., & Abdulrahim, R. (2019). Impact of e-learning vs traditional learning on student’s performance and attitude. International Medical Journal, 24(03), 225-33.
Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094. https://doi.org/10.1016/j.ijinfomgt.2020.102094
Hair, J. F. (2011). Multivariate data analysis: An overview. International encyclopedia of statistical science, 2(3), 904-907. https://doi.org/10.1007/978-3-642-04898-2_395
Horzum, M. B. (2017). Interaction, structure, social presence, and satisfaction in online learning. Eurasia Journal of Mathematics, Science and Technology Education, 11(3), 505-512.
https://doi.org/10.12973/eurasia.2014.1324a
Lim, J., Kim, M., Chen, S. S., & Ryder, C. E. (2008). An empirical investigation of student achievement and satisfaction in different learning environments. Journal of Instructional Psychology, 35(2), 113.
Lin, H. C., Chiu, Y. H., Chen, Y. J., Wuang, Y. P., Chen, C. P., Wang, C. C., & Ho, W. H. (2017). Continued use of an interactive computer game-based visual perception learning system in children with developmental delay. International journal of medical informatics, 107, 76-87.
https://doi.org/10.1016/j.ijmedinf.2017.09.003
Liu, C. T., Du, T. C., & Tsai, H. H. (2009). A study of the service quality of general portals. Information & Management, 46(1), 52-56. https://doi.org/10.1016/j.im.2008.11.003
Muralidharan, R., Kanagasabapathy, T., & Ganesh, R. V. (2021, February). Analyzing e-learning platform reviews using sentimental evaluation with SVM classifier. Journal of Physics: Conference Series, 1767(1), 12. https://doi.org/10.1088/1742-6596/1767/1/012012
Navimipour, N. J., & Zareie, B. (2015). A model for assessing the impact of e-learning systems on employees’ satisfaction. Computers in Human Behavior, 53, 475-485. https://doi.org/10.1016/j.chb.2015.07.026
Nedeljković, I., & Rejman Petrović, D. (2023). Student satisfaction and intention to use e-learning during the Covid-19 pandemic. The International Journal of Information and Learning Technology, 40(3), 225-241. https://doi.org/10.1108/IJILT-05-2022-0119
Nunnally, J. C. (1975). Psychometric theory—25 years ago and now. Educational Researcher, 4(10), 7-21. https://doi.org/10.3102/0013189X004010007
Nyathi, M. (2023). Virtual campuses in developing countries: an evaluation of the “right connectors” for effective e-learning in higher education institutions in Zimbabwe. Journal of Research in Innovative Teaching & Learning, 16(2), 253-267. https://doi.org/10.1108/JRIT-06-2022-0040
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263. https://doi.org/10.1057/ejis.2008.15
Queiros, D. R., & de Villiers, M. R. (2016). Online Learning in a South African Higher Education Institution: Determining the Right Connections for the Student. The International Review of Research in Open and Distributed Learning, 17(5). https://doi.org/10.19173/irrodl.v17i5.2552
Rahmi, B. A. K. I., Birgoren, B., & Aktepe, A. (2018). A meta-analysis of factors affecting perceived usefulness and perceived ease of use in the adoption of e-learning systems. Turkish Online Journal of Distance Education, 19(4), 4-42. https://doi.org/10.17718/tojde.471649
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007
Suryanto, E., Oktarina, N., & Tusyanah, T. (2024). Role of Social Presence in Mediating Learning Satisfaction in Online Learning. Journal of Learning for Development, 11(1), 82-98. https://doi.org/10.56059/jl4d.v11i1.1043
Syed, A. M., Ahmad, S., Alaraifi, A., & Rafi, W. (2021). Identification of operational risks impeding the implementation of eLearning in higher education system. Education and Information Technologies, 26, 655-671. https://doi.org/10.1007/s10639-020-10281-6
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2016). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306-328. https://doi.org/10.1080/10494820.2015.1122635
Wijaya, M. I., Suzanna, S., Utomo, D., & Adnizio, K. (2021, August). Analyzing the impact of social presence on student satisfaction through small group discussion in a synchronous online learning. 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), 136-142.
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