An Assessment of Student Motivation in Online Learning Environments

This study assesses student motivation in online learning environments, with the purpose of exploring how affiliation influences learners’ power and achievement in university learning. The study is guided by McClelland’s Theory of Needs (1965) and Fowler’s (2018) online motivation framework, which frame motivation through affiliation, power, and achievement. The research problem concerns the difficulty of sustaining motivation online, where students may experience isolation, reduced focus, and heavy demands for self-discipline despite the normalisation of virtual education. A quantitative survey design was employed using a convenient sample of 199 students. Data were collected through a five-point Likert-scale questionnaire adapted from Fowler’s motivation to learn online instrument and analysed using descriptive statistics, reliability analysis, t-tests, and Pearson correlation in SPSS. The findings showed no significant differences in online motivation across gender, clusters, education background, and academic years, suggesting that demographic factors were not the main source of motivational variation. The findings also revealed that learners perceived affiliation, power, and achievement positively, while affiliation had strong positive relationships with both power and achievement. Overall, online motivation appears to depend strongly on meaningful connection and instructor support. The study implies that educators should design affiliation-rich online learning environments, while future research should use mixed-method approaches to examine learner motivation.

Online Learning

1. Ahmad, N., Alias, F. A., Hamat, M., & Mohamed, S.A. (2024). Reliability analysis: Application of Cronbach’s Alpha in research instruments. SIG: e-L earning@CS, 114-119. [Google Scholar] [Crossref]

2. Ajlouni, A., Almahaireh, A., Awwad, F. A., & Rawadieh, S. (2022). Gender differences in the motivational profile of undergraduate students in light of Self-Determination Theory: The case of online learning setting. Journal of Social Studies Education Research, 13(1), 75–103. [Google Scholar] [Crossref]

3. Anderman, E. M., & Anderman, L. H. (2020). Classroom motivation (3rd ed.). Routledge. [Google Scholar] [Crossref]

4. Fowler, S. (2018). The motivation to learn online questionnaire [Doctoral Dissertation, University of Georgia]. Department of Educational Psychology Theses and Dissertations. [Google Scholar] [Crossref]

5. https://getd.libs.uga.edu/pdfs/fowler_kevin_s_201805_phd.pdf [Google Scholar] [Crossref]

6. He, L. (2024) The application of SPSS correlation analysis in the study of precision teaching of English in universities. Applied Mathematics and Nonlinear Science, 9(1), 1-13. [Google Scholar] [Crossref]

7. http://dx.doi.org/10.2478/amns-2024-1371 [Google Scholar] [Crossref]

8. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review, 27, 1–12. [Google Scholar] [Crossref]

9. Kovtoniuk, M. M., Kosovets, O. P., Soia, O. M., & Tyutyu, L. L. (2022). Virtual educational environments: Major trends in the use of modern digital technologies in higher education institutions. Open Educational Studies, 4(1), 135–147. https://doi.org/10.1515/edu-2022-0008 [Google Scholar] [Crossref]

10. McClelland, D. C. (1965). Toward a theory of motive acquisition. American Psychologist, 20(5), 321–333. https://doi.org/10.1037/h0022225 [Google Scholar] [Crossref]

11. Meşe, E., & Sevilen, Ç. (2021). Factors influencing EFL students' motivation in online learning: A qualitative case study. Journal of Educational Technology and Online Learning, 4(1), 11–22. [Google Scholar] [Crossref]

12. http://doi.org/10.31681/ [Google Scholar] [Crossref]

13. Rahmat, N. H. (2025). A comparison of online engagement for undergraduates. International Journal of Research and Innovation in Social Sciences (IJRISS), 9(6), 3260-3270. [Google Scholar] [Crossref]

14. https://dx.doi.org/10.47772/IJRISS.2025.906000240 [Google Scholar] [Crossref]

15. Rahmat, N. H., Mok, S. S., & Teh, H. S. (2023). Motivation to learn online: An analysis from McClelland’s theory of needs. International Journal of Academic Research in Business and Social Sciences, 13(3), 200–218. https://doi.org/10.6007/IJARBSS/v13-i3/16471 [Google Scholar] [Crossref]

16. Ravichandran, N., & Kohli, C. (2021). The future of online learning: An outlook based on recent worldwide pandemic experience. International Dialogues on Education, 8(1), 89–118. [Google Scholar] [Crossref]

17. https://doi.org/10.53308/ide.v8i1.250 [Google Scholar] [Crossref]

18. Saufianim, Izni, N. A., Mahmood, N. A., Tajuddin, N. I. I., Zainuddin, N., & Rahmat, N. H. (2023). Affiliation, achievement and power in learning motivation: How do they relate? International Journal of Academic Research in Business & Social Sciences, 13(8), 1231-1248. [Google Scholar] [Crossref]

19. http://dx.doi.org/10.6007/IJARBSS/v13-i8/17742 [Google Scholar] [Crossref]

20. Shah, M. A. (2024). Meaning and Statistical Analysis of T-Tests in SPSS. Journal of Saidu Medical College, 14(1), 64-65. https://doi.org/10.522006 [Google Scholar] [Crossref]

21. Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018). American Journal of Distance Education, 33(4), 289–306. https://doi.org/10.1080/08923647.2019.1663082 [Google Scholar] [Crossref]

22. Slavin, R. E. (2019). Educational psychology: Theory and practice (12th ed.). Pearson. [Google Scholar] [Crossref]

23. Sudarnoto, L. F., Handoko, M. T., Riyanto, A., & Arini, D. P. (2025). The impact of online learning, learning motivation, and interpersonal relationships on students’ wellbeing. Social Sciences & Humanities Open, 11, 1-9. https://doi.org/10.1016/j.ssaho.2025.101485 [Google Scholar] [Crossref]

24. Vetter, T. R. (2017). Descriptive statistics: Reporting the answers to the 5 basic questions of who, what, why, when, where, and a sixth, so what? Anesth Analg, 125(5), 1797-1802. [Google Scholar] [Crossref]

25. https://doi.org/10.1213/ane.0000000000002471 [Google Scholar] [Crossref]

26. Yusof, R., Harith, N. H. M., Lokman, A., Abd Batau, M. F., Zain, Z. M., & Rahmat, N. H. (2023). A study of perception on students' motivation, burnout and reasons for dropout. International Journal of Academic Research in Business & Social Sciences, 13(3), 200–215. [Google Scholar] [Crossref]

27. https://doi.org/10.6007/IJARBSS/v13-i3/16471 [Google Scholar] [Crossref]

28. Zhang, Q., & Hou, Y. (2024). Exploring the differential effects of face-to-face and e-learning approaches on learning outcomes, retention, and student engagement in English language education. Journal of Social Sciences and Economics, 3(2), 118–126. https://doi.org/10.61363/4nqn0785 [Google Scholar] [Crossref]

29. Ziegenfuss, J. Y., Easterday, C. A., Dinh, J. M., JaKa, M. M., Kottke, T. E., & Canterbury, M. (2021). Impact of demographic survey questions on response rate and measurement: A randomized experiment. Survey Practice 14(1), 1-11. https://doi.org/10.29115/SP-2021-0010 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study