Year 2020, Volume 21 , Issue 4, Pages 110 - 123 2020-10-01


Fatma BAYRAK [1] , Moanes H. TIBI [2] , Arif ALTUN [3]

Higher education institutions consider student satisfaction to be one of the main factors in determining the quality of their online learning. The purpose of this study was to develop a reliable, valid, and practical instrument to measure online students’ satisfaction as well as to explore the psychometric and theoretical concerns surrounding the construct validity of existing satisfaction scales. The study was carried out in 2017–2018 fall and spring with participants consisting of freshmen who took the online course in a state university (Nfall=1585; Nspring=1206). In this study exploratory factor analysis (EFA) (Study 1-NEFA=921) and confirmatory factor analysis (CFA) (Study 1-NCFA=664; Study 1-NCFA=1206) were performed to assess the construct validity of the scale’s measures. As proof of validity, the effect of gender on satisfaction was examined, for which independent sample t-test was performed. For the criterion validity, the relationship between computer and internet self-efficacy and satisfaction scores of the learners was examined. The finalized version of satisfaction scale, consisting of eight items, demonstrated that the scale is suitable for general use. Suggestions for future researchers and practioners are proposed.
Online learning, student satisfaction, scale development
  • Alshare, K. A., Freeze, R. D., Lane, P. L., & Wen, H. J. (2011). The impacts of system and human factors on online learning systems use and learner satisfaction. Decision Sciences Journal of Innovative Education, 9(3), 437-461. doi:
  • Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research, 9(1), 45. doi:
  • An, H., Kim, S., & Kim, B. (2008). Teacher perspectives on online collaborative learning: Factors perceived as facilitating and impeding successful online group work. Contemporary Issues in Technology and Teacher Education, 8(1), 65-83.
  • Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with Internet-based MBA courses. Journal of Management Education, 24(1), 32.
  • Bolliger, D.U. (2004). Key Factors for Determining Student Satisfaction in Online Courses. International Journal on E-Learning, 3(1), 61-67. Norfolk, VA: Association for the Advancement of Computing in Education (AACE).
  • Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(1), 81-98. doi:
  • Britto, M., & Rush, S. (2013). Developing and implementing comprehensive student support services for online students. Journal of Asynchronous Learning Networks, 17, 29–42.
  • Chen, S. J. (2014). Instructional design strategies for intensive online courses: An objectivist-constructivist blended approach. Journal of Interactive Online Learning, 13(1), 72-86.
  • Chiu, C., M. Hsu, S. Sun, T. Lin, P. Sun, (2005), Usability, quality, value and e-learning continuance decisions. Computers & Education, 45, 399–416. doi:
  • Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 174-203.
  • Croxton, R. A. (2014). The role of interactivity in student satisfaction and persistence in online learning. Journal of Online Learning and Teaching, 10(2), 314.
  • McGorry, S. Y. (2003). Measuring quality in online programs. The Internet and Higher Education, 6(2), 159- 177. doi:
  • 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. doi:
  • Eryilmaz, M. (2012). The effect of hyper media on academic achievement satisfaction and cognitive load of students by using adaptive presentation and adaptive navigation. (Unpublished doctoral dissertation). Ankara University:Ankara.
  • Fedynich, L., Bradley, K. S., & Bradley, J. (2015). Graduate Students’ Perceptions of Online Learning. Research in Higher Education Journal, 27, 1-13.
  • Fleiss, J. L. (1981). Balanced incomplete block designs for inter-rater reliability studies. Applied Psychological Measurement, 5(1), 105-112.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18, 39–50.
  • Gecer, A. K., & Topal, A. D. (2015). Development of satisfaction scale for e-course: reliability and validity study. Journal of Theory & Practice in Education (JTPE), 11(4), 1271-1287.
  • Gil, H. (2008). The challenge of the transition from online delivery to online teaching and learning. In K. McFerrin, R. Weber, R. Carlsen, D. A. Williset. (Eds.), Proceedings of Society for Information Technology and Teacher Education International Conference. Chesapeake, VA: AACE, 2589-2594.
  • Gonzalez-Gomez, F., Guardiola, J., Rodriguez, O. M., & Alonso, M. A. M. (2012). Gender differences in e-learning satisfaction. Computers & Education, 58(1), 283-290. doi: compedu.2011.08.017
  • Green, K. C. (2010). The Campus Computing Survey. Encino, CA: The Campus Computing Project. Retrieved from
  • Gunawardena, C. N., Linder-VanBerschot, J. A., LaPointe, D. K., & Rao, L. (2010). Predictors of learner satisfaction and transfer of learning in a corporate online education program. The American Journal of Distance Education, 24(4), 207-226. doi:
  • Gunawardena, C. N., & Zittle, F. J. (1997). Social presence as a predictor of satisfaction within a computermediated conferencing environment. The American Journal of Distance Education, 11(3), 8-26.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th Edition), Upper Saddle River, NJ [etc.]. Pearson Prentice Hall, New York, NY: Macmillan
  • 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. d
  • Harvey, H. L., Parahoo, S., & Santally, M. (2017). Should gender differences be considered when assessing student satisfaction in the online learning environment for millennials?. Higher Education Quarterly, 71(2), 141-158.
  • Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (p. 76–99). Sage Publications, Inc.
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling., 6, 1–55.
  • Hung M-L, Chou C, Chen C-H, & Own, Z-Y. (2010) Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3):1080–90.
  • Ilgaz, H. (2008). The contribution of technology acceptance and community feeling to learner satisfaction in distance education [Uzaktan egitimde teknoloji kabulunun ve topluluk hissinin ogrenen memnuniyetine katkisi] (Unpublished master’s thesis). Hacettepe University, Ankara.
  • Jaggars, S. S., & Xu, D. (2016). How do online course design features influence student performance? Computers & Education, 95, 270-284.
  • Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23, 1-13.
  • Kelecioglu, H., & Sahin, S. G. (2014). Validity from Past to Present [Gecmisten gunumuze gecerlik]. Journal of Measurement and Evaluation in Education and Psychology, 5(2), 1-11.
  • Kirmizi, O. (2015). The influence of learner readiness on student satisfaction and academic achievement in an online program at higher education. Turkish Online Journal of Educational Technology-TOJET, 14(1), 133-142.
  • Kirtman, L. (2009). Online versus in-class courses: An examination of differences in learning outcomes. Issues in Teacher Education, 18(2), 103-116.
  • Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.
  • Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16-39.
  • Kuo, Y. C., Walker, A. E., Schroder, K. 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.
  • Kurucay, M., & Inan, F. A. (2017). Examining the effects of learner-learner interactions on satisfaction and learning in an online undergraduate course. Computers & Education, 115, 20-37.
  • Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. doi:
  • Lim, C. K. (2001). Computer self-efficacy, academic self-concept, and other predictors of satisfaction and future participation of adult distance learners. The American Journal of Distance Education 15(2): 41–51. doi:
  • Lister, M. (2014). Trends in the design of e-learning and online learning. Journal of Online Learning and Teaching, 10(4), 671-680.
  • Martin-Rodriguez, O., Fernandez-Molina, J. C., Montero-Alonso, M. A., & Gonzalez-Gomez, F. (2015). The main components of satisfaction with e-learning. Technology, Pedagogy and Education, 24(2), 267-277. doi:
  • McGorry, S. Y. (2003). Measuring quality in online programs. The Internet and Higher Education, 6(2), 159-177.
  • Milheim, K. L. (2012). Toward a better experience: Examining student needs in the online classroom through Maslow’s hierarchy of needs model. Merlot Journal of Online Learning and Teaching, 8(2).
  • Nunnally, J. C., and I. H. Bernstein (1994). Psychometric Theory, 3rd ed. New York: McGraw‐Hill. Patil Vivek H, Surendra N. Singh, Sanjay Mishra, and D. Todd Donavan (2017). Parallel Analysis Engine to Aid in Determining Number of Factors to Retain using R
  • Parahoo, S. K., Santally, M. I., Rajabalee, Y., & Harvey, H. L. (2016). Designing a predictive model of student satisfaction in online learning. Journal of Marketing for Higher Education, 26(1), 1-19.
  • Ralston-Berg, P., Buckenmeyer, J., Barczyk, C., & Hixon, E. (2015). Students’ perceptions of online course quality: How do they measure up to the research? Internet Learning Journal, 4(1), 38–55.
  • Rios, T., Elliott, M., & Mandernach, B. J. (2018). Efficient Instructional Strategies for Maximizing Online Student Satisfaction. Journal of Educators Online, 15(3).
  • Roper, A. R. (2007). How students develop online learning skills. Educause Quarterly, 30(1), 62-65. Sahin, I., & Shelley, M. (2008). Considering students’ perceptions: the distance education student satisfaction model. Journal of Educational Technology & Society, 11(3).
  • Sebastianelli, R., Swift, C., & Tamimi, N. (2015). Factors affecting perceived learning, satisfaction, and quality in the online MBA: A structural equation modeling approach. Journal of Education for Business, 90(6), 296–305.
  • Tibi, M. H. (2015). Improving collaborative skills by computer science students through structured discussion forums. Journal of Technologies in Education, 10 (3-4), 27-41. doi:10.24059/olj.v22i1.99
  • Turhangil Erenler, H. H. (2019). A structural equation model to evaluate students’ learning and satisfaction. Computer Applications in Engineering Education.
  • Uusiautti, S., Maatta, K., & Leskisenoja, E. (2017). Succeeding Alone and Together-University Students’ Perceptions of Caring Online Teaching. Journal of Studies in Education, 7(2), 48-66. doi: https://
  • Wallace, R. M. (2003). Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication, and Information, 3(2), 241-280. doi: https://
  • Ward, M., Peters, G., & Shelley, K. (2010). Student and faculty perceptions of the quality of online learning experiences. The International Review of Research in Open and Distributed Learning, 11(3), 57-77.
  • Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3).
  • Young, A., & Norgard, C. (2006). Assessing the quality of online courses from the students’ perspective. The Internet and Higher Education, 9(2), 107-115. doi:
  • Yurdugul, H., & Alsancak Sirakaya, D. (2013). The scale of online learning readiness: A study of validity and reliability. Education and Science, 38, 391–406.
  • Yurdugul, H., & Bayrak, F. (2012). Content validity criteria in scale development studies: Comparison of content validity index and Kappa statistics [Olcek Gelistirme Calismalarinda Kapsam Gecerlik Olculeri: Kapsam Gecerlik Indeksi ve Kappa Istatistiginin Karsilastirilmasi]. Hacettepe University Journal of Education, 2(Special Issue), 264-271
  • Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and flexibility as the contributing factors to students’ satisfaction in an online certificate program. Educational Technology & Society, 11(4), 51-65.
Primary Language en
Subjects Social
Journal Section Articles

Orcid: 0000-0001-8500-1456
Author: Fatma BAYRAK (Primary Author)
Institution: Hacettepe University Ankara, TURKEY
Country: Turkey

Orcid: 0000-0002-0661-5212
Author: Moanes H. TIBI
Institution: Faculty of Education Beit Berl College Kfar Saba, ISRAEL
Country: Israel

Orcid: 0000-0003-4060-6157
Author: Arif ALTUN
Institution: Hacettepe University Ankara, TURKEY
Country: Turkey


Application Date : November 25, 2019
Acceptance Date : October 22, 2020
Publication Date : October 1, 2020

APA Bayrak, F , Tıbı, M , Altun, A . (2020). DEVELOPMENT OF ONLINE COURSE SATISFACTION SCALE . Turkish Online Journal of Distance Education , 21 (4) , 110-123 . DOI: 10.17718/tojde.803378