Studying Alone Together: Narrative Accounts of Motivation, Meaningful Interaction, and the Search for Academic Satisfaction Among Distance Learners at Indonesia’s Universitas Terbuka
Indonesia Indonesia Indonesia Indonesia Indonesia 
Article Sidebar
-
Academic Satisfaction, Distance Learning, Structural Equation Modeling (SEM), Student Interaction, Student Motivation
Abstract
Background The growing usage of online tutorial systems (tuton) has revolutionized the paradigms of higher education in the digital age. Student motivation and interaction in online learning are significant antecedents of academic satisfaction and achievement.
Purpose. This research aims to examine the influence of motivation and interaction on academic achievement using satisfaction as mediating variable in tutorial learning environment.
Methods: The study is quantitative and Structural Equation Modelling techniques were used with the help of IBM AMOS version 23.0. A total of 877 respondents were identified by purposive sampling. The instrument used was a questionnaire with four factors measured on a likert scale ranging from 1-5. Five motivation indicators, four interaction indicators, six satisfaction indicators and three academic achievement indicators
Results The descriptive analysis showed that all the variables were in good category with mean score of 4.19 for motivation, 4.02 for interaction, 4.05 for satisfaction and 4.04 for academic success. The t-values were more than 1.96 and p-values were less than 0.05, which means all seven hypotheses were supported. The structural model had R2 of 0.845 and 0.938 and good fit indices such as GFI of 0.943, RMSEA of 0.055 and CFI of 0.978 .
Conclusion Motivation and interaction are found to positively influence students’ satisfaction and academic progress. The mediating role of satisfaction in the link of motivation and interaction to academic performance is significant and practically meaningful, with indirect effects of 0.071 for motivation and 0.244 for interaction. It demonstrates the critical role of student satisfaction in online learning settings for enhancement of academic accomplishment.
Full text article
References
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562
Alzahrani, D. M. G. (2017). The Effect of Using Online Discussion Forums on Students’ Learning. The Turkish Online Journal of Educational Technology, 16(1).
Amoozegar, A., Abdelmagid, M., & Anjum, T. (2024). Course satisfaction and perceived learning among distance learners in Malaysian Research Universities: The impact of motivation, self-efficacy, self-regulated learning, and instructor immediacy behaviour. Open Learning: The Journal of Open, Distance and e-Learning, 39(4), 387–413. https://doi.org/10.1080/02680513.2022.2102417
Azhra, S. Y., Alsamiri, Y., & Putro, N. H. P. S. (2023). The Emotional Intelligence and Burnout of In-service English Teachers in Yogyakarta. Jurnal Penelitian Dan Evaluasi Pendidikan, 27(2), 257–268. https://doi.org/10.21831/pep.v27i2.68992
Bardakci, S., Cayci, K., Colkesen, A., & Fatih, A. (2021). How to become a constructivist teacher: The impact of philosophical thoughts on constructivist learning beliefs. Research in Pedagogy, 11(1), 214–233. https://doi.org/10.5937/IstrPed2101v214B
Byrne, B. M. (2016). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Third Edition (0 ed.). Routledge. https://doi.org/10.4324/9781315757421
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dawadi, S., Goshtasbpour, F., & Kukulska-Hulme, A. (2024). Equitable Access to Higher Education Learning and Assessment: Perspectives from Low-Resource Contexts. Journal of Interactive Media in Education, 2024(1), 2. https://doi.org/10.5334/jime.832
Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. Springer US. https://doi.org/10.1007/978-1-4899-2271-7
Hair, J. F. (2019). Multivariate data analysis (Eighth edition). Cengage.
Harsasi, M., & Sutawijaya, A. (2018). Determinants of Student Satisfaction in Online Tutorial: A Study of A Distance Education Institution. Turkish Online Journal of Distance Education, 19(1), 89–99. https://doi.org/10.17718/tojde.382732
Hartnett, M., St. George, A., & Dron, J. (2011). Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent. The International Review of Research in Open and Distributed Learning, 12(6), 20. https://doi.org/10.19173/irrodl.v12i6.1030
Hidayat, R. & Patricia Wulandari. (2022). Data Analysis Procedures with Structural Equation Modelling (SEM): Narrative Literature Review. Open Access Indonesia Journal of Social Sciences, 5(6), 859–865. https://doi.org/10.37275/oaijss.v5i6.142
Huang, F., Teo, T., & Zhou, M. (2019). Factors Affecting Chinese English as a Foreign Language Teachers’ Technology Acceptance: A Qualitative Study. Journal of Educational Computing Research, 57(1), 83–105. https://doi.org/10.1177/0735633117746168
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. The Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001
Kwak, S. G., & Kim, J. H. (2017). Central limit theorem: The cornerstone of modern statistics. Korean Journal of Anesthesiology, 70(2), 144. https://doi.org/10.4097/kjae.2017.70.2.144
Liu, M., Jacob, R., Belusko, M., Riahi, S., & Bruno, F. (2021). Techno-economic analysis on the design of sensible and latent heat thermal energy storage systems for concentrated solar power plants. In Renewable Energy (Vol. 178). Elsevier BV. https://doi.org/10.1016/j.renene.2021.06.069
Martin, F., & Bolliger, D. U. (2018). Engagement Matters: Student Perceptions on the Importance of Engagement Strategies in the Online Learning Environment. Online Learning, 22(1), Article 1. https://doi.org/10.24059/olj.v22i1.1092
Masa’deh, R., Almajali, D., Alrowwad, A., Alkhawaldeh, R., Khwaldeh, S., & Obeidat, B. (2023). Evaluation of factors affecting university students’ satisfaction with e-learning systems used dur-ing Covid-19 crisis: A field study in Jordanian higher education institutions. International Journal of Data and Network Science, 7(1), 199–214. https://doi.org/10.5267/j.ijdns.2022.11.003
Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The Effectiveness of Online and Blended Learning: A Meta-Analysis of the Empirical Literature. Teachers College Record: The Voice of Scholarship in Education, 115(3), 1–47. https://doi.org/10.1177/016146811311500307
Meri-Yilan, S. (2022). (Re)considering Motivational Scaffolding: A Mixed-Method Study on Turkish Students’ Perspectives on Online Learning Before and During the Pandemic. Open Praxis, 14(3), 179–189. https://doi.org/10.55982/openpraxis.14.3.466
Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. https://doi.org/10.1080/08923648909526659
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions -. https://journals.sagepub.com/doi/abs/10.1177/002224378001700405
Ouajdouni, A. (2024). E-Learning in Morocco: Definitions and Leading Approaches for Assessing the Success of Information Systems. In S. Goundar (Ed.), Massive Open Online Courses—Learning Frontiers and Novel Innovations. IntechOpen. https://doi.org/10.5772/intechopen.1006484
Pauls, F., & Daseking, M. (2021). Revisiting the Factor Structure of the German WISC-V for Clinical Interpretability: An Exploratory and Confirmatory Approach on the 10 Primary Subtests. Frontiers in Psychology, 12, 710929. https://doi.org/10.3389/fpsyg.2021.710929
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Rahmani, A. M., Groot, W., & Rahmani, H. (2024). Dropout in online higher education: A systematic literature review. International Journal of Educational Technology in Higher Education, 21(1), 19. https://doi.org/10.1186/s41239-024-00450-9
Rice, M. F. (2022). OLJ December 2022 26(4). Online Learning, 26(4). https://doi.org/10.24059/olj.v26i4.3752
Salameh, W. A. K. (2025). Exploring the impact of AI-driven real-time feedback systems on learner engagement and adaptive content delivery in education. International Journal of Science and Research Archive, 14(2), 098–104. https://doi.org/10.30574/ijsra.2025.14.2.0299
Salman, O. K., Khasawneh, Y. J. A., Alqudah, H., Alwaely, S. A., & Khasawneh, M. A. S. (2024). Tailoring gamification to individual learners: A study on personalization variables for skill enhancement. International Journal of Data and Network Science, 8(2), 789–796. https://doi.org/10.5267/j.ijdns.2023.12.025
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
Wicaksono, P. A., Sari, D. P., Azzahra, F., & Sa’adati, N. A. (2024). Analysis of Consumer Behaviour in Purchasing Second-Hand Fashion Products: An Extended Theory of Planned Behaviour Model. International Journal of Sustainable Development and Planning, 19(8), 2955–2964. https://doi.org/10.18280/ijsdp.190813
Xu, D., Avakian Orona, G., & Li, Q. (2025). Interaction-oriented Instructional Practices Show Promise in Enhancing Overall Performance and Narrowing Racial Achievement Gaps in College Online Courses. Online Learning, 29(3). https://doi.org/10.24059/olj.v29i3.4626
Yadav, M., & Tandel, B. (2021). Structural Equation Model-Based Selection and Strength Co-Relation of Variables for Work Performance Efficiency Under Traffic Noise Exposure. Archives of Acoustics, 155–166. https://doi.org/10.24425/aoa.2021.136569
Yang, H.-H., & Lin, J.-Y. (2023). Students’ persistence intention in MOOCs in the psychomotor domain: An extended 3P model of the teaching and learning perspective. Frontiers in Psychology, 14, 1094138. https://doi.org/10.3389/fpsyg.2023.1094138
Authors
Copyright (c) 2026 Meirani Harsasi, Minrohayati Minrohayati, Rahmad Purnama, Sila Ninin Wisnantiasri, Hubertina Karolina Ngarbingan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
