Students’ Satisfaction towards Online Learning: A Longitudinal Study
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Abstract
Online education encompasses terms like web-based training, e-learning, distributed learning, Internet learning, web instruction, cyber education, virtual education, and net-based learning, as outlined by Urdan & Weggen (2000). It includes various technological applications and instructional methods such as computer-based learning, web education, virtual classrooms, and digital collaboration. Recognising that the educational landscape has transitioned from traditional in-person settings to online platforms, compelling tertiary education institutions to integrate technology effectively to ensure uninterrupted learning experiences is crucial. These institutions have thus become keen on optimizing online course delivery, engaging students, and facilitating assessments. Understanding students' perceptions and responses to online education over time (longitudinally) offers valuable insights for these institutions, allowing them to adjust their approaches to meet students' needs and expectations. Considering students' views is essential, though adapting to online education poses challenges. Consequently, this research investigates students' satisfaction with online education within higher education institutions. Employing a longitudinal approach for data collection, the study analyzed the information through correlations and regression analysis. The sample comprised 104 undergraduate business school students from a private university. Findings indicated a positive correlation between all independent variables and student satisfaction, with course design, student interactions, and lecturer engagement significantly influencing satisfaction levels. The study highlights the potential of online education to contribute to the continuous development of innovative and effective learning within the complex knowledge management framework of higher education. However, the unpredictability of challenges and changes complicates the management and resourcing of higher education. Future studies should focus on the nature and quality of student interactions, their cognitive processes, preferences for specific educational tools and methods, motivation, and learning strategies to further understand factors influencing their satisfaction.
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References
Arbough, J.B., & Rau, B.L. (2007). A study of disciplinary, structural, and behavioural effects on course outcomes in online MBA courses. Decision Science Journal of Innovative Education, 5(1), 65–94.
Basuony, M. A-K., EmadEldeen, R., Farghaly, M., El-Bassiouny, N. and Mohamed, E. K. A. (2021). The factors affecting student satisfaction with online education during the COVID-19 pandemic: an empirical study on an emerging Muslim country. Journal of Islamic Marketing 12(3), 631–648.
Becker, G. S. (1975). Human capital: A theoretical and empirical analysis, with special reference to education, 2nd ed, Columbia University Press for NBER, New York, NY.
Bitzer, P., & Janson, A. (2014, July). Towards a Holistic Understanding of Technology-Mediated Learning Services–A State-of-the-Art Analysis. In Bitzer, P. & Janson, A. (2014): Towards a Holistic Understanding of Technology-Mediated Learning Services-A State-of-the-Art Analysis. In: European Conference on Information Systems (ECIS), Tel Aviv, Israel.
Boling, E.C., Hough, M., Krinsky, H., Saleem, H., & Stevens, M. (2012). Cutting the distance in distance education: Perspectives on what promotes positive, online learning experiences. Internet and Higher Education, 15,118-126
Bolliger, D. U. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-learning,3(1), 61-67.
Chou, S. W., & Liu, C. H. (2005). Learning effectiveness in a Web‐based virtual learning environment: a learner control perspective. Journal of computer-assisted learning,21(1), 65–76.
DeBourgh, G.A. (1999). Technology is the tool; teaching is the task: Student satisfaction in distance learning.
Eom, S. B., & Ashill, N. (2016). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An update. Decision Sciences Journal of Innovative Education, 14(2), 185–215.
Fieber, C. (2019). The effect of collaboration on online student motivation in a College Biology Course, University of South Carolina, Sumter.
Finaly-Neumann, E. (1994). Coursework characteristics and students’ satisfaction with instructions. Journal of Instructional Psychology, 21(2), 14-19
Kim, S., and Kim, D. –J (2021). Structural relationship of key factors for student satisfaction and achievement in asynchronous online learning. Sustainability 13, 6734.
Kleij, F.M.v.d., Eggen, T.J.H.M., Timmers, C.F., & Veldkamp, B.P. (2012). Effects of feedback in a computer-based assessment for learning. Computers & Education, 58,263-272.
Kuo,Y.-C., Walker, A.E., Schroder, K.E.E., & Belland, B.R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet and Higher Education, 20, 35-50.
Lucha & Berhanu. (2015). Analysis of Verbal Interaction between Teacher and Students in Industrial Engineering International Program at the Islamic University of Indonesia.
Moore, M. G. (1991). Distance education theory. In D. Keegan (Ed.), Theoretical principles of distance education. New York, NY: Routledge, pp. 22–38.
Nyathi, M. and Sibanda, E. (2022). E-learning: substitutability of learner-learner, and learner- facilitator interactions to enhance learner satisfaction in higher education. Journal of Research in Innovative Teaching & Learning, 2397-7604.
Olson, D. L. (2003). Principles, impracticality, and passion. Phi Delta Kappa, 85(4), 307–309.
Rubin, B., & Fernandes, R. (2013). Measuring the Community in Online Classes. Journal of Asynchronous Learning Networks, 17(3), 115-136.
She, L., Ma, L., Jan A, Sharif Nia H. and Rahmatpour, P. (2021). Online learning satisfaction during COVID-19 pandemic among Chinese university students: the serial mediation model. Front. Psychol 12, 1-12.
Shea. T., & Parayitam. S. (2019). Antecedents of graduate student satisfaction through e-portfolio: content analysis. Education + Training, 61(9), 1045-1063
Shin, N., Jonassen, D., & McGee, S. (2003). Predictors of well-structured and ill-structured problem-solving in an astronomy simulation. Journal of Research in Science Teaching, 40,6-33.
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.
Swan, K., Day, S. L., Bogle, L. R., & Matthews, D. B. (2014). A collaborative, design-based approach to improving an online program. The Internet and Higher Education, 21, 74–81.
Teo, T., & Wong, S. L. (2013). Modelling key drivers of e-learning satisfaction among student teachers. Journal of educational computing research,48(1), 71-95.
Volery, T., & Lord, D. (2000). Critical success factors in online education. International Journal of Educational Management, 14(5), 216–223.
Weerasinghe, I. M. S., & Lalitha, F. R. S. (2018). University facilities and student satisfaction in Sri Lanka. International Journal of Educational Management, 32(5), 866–880.
Woo, Y., & Reeves, T.C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. Internet and Higher Education, 10,15-25.