International Association of Educators   |  ISSN: 2834-7919   |  e-ISSN: 1554-5210
International Journal of Progressive Education
International Association of Educators

Original article | International Journal of Progressive Education 2023, Vol. 19(5) 153-171

Translation of the MOOC Student Satisfaction Survey to Turkish: A Scale Adaptation and Validation Study

Emre Uygun & Kürşat Cesur

pp. 153 - 171   |  DOI: https://doi.org/10.29329/ijpe.2023.603.10   |  Manu. Number: MANU-2305-27-0004.R1

Published online: October 16, 2023  |   Number of Views: 16  |  Number of Download: 59

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Abstract

Massive Open Online Courses (MOOCs) have been widely used all around the world to a great extent. Many of the MOOCs in different countries are in their native language, and there is a need to reliably assess the satisfaction levels of learners with various first languages since satisfaction stands as a critical aspect in identifying the reasons of dropouts and incontinence to MOOCs. To this end, this study aimed to translate Kumar and Kumar’s (2020) “MOOC Student Satisfaction Survey” into Turkish. The researchers first translated the instrument items from English to Turkish before consulting a panel of three English experts and one Turkish expert on the suitability of the translation. A professional translator then backtranslated the scale to English, ensuring that no items were lost in translation. To establish content validity, changes were done in view of the professional feedback. The translated scale was subsequently administered to 150 former massive open online course participants for testing validity and reliability. Since this was a translation study, the same constructs of the original scale were retained, and a confirmatory factor analysis was conducted, the results of which indicated acceptable levels of validity with one item being discarded. As for the reliability values, Cronbach’s alpha coefficient for the entire scale was .91, and the split-half reliability score was .87, indicating that the scale maintains good internal consistency. Therefore, it was determined that the scale’s Turkish translation was valid and reliable.

Keywords: Distance Education, MOOCs, Online Learning, Scale Adaptation, Student Satisfaction

How to Cite this Article?

APA 6th edition
Uygun, E. & Cesur, K. (2023). Translation of the MOOC Student Satisfaction Survey to Turkish: A Scale Adaptation and Validation Study . International Journal of Progressive Education, 19(5), 153-171. doi: 10.29329/ijpe.2023.603.10

Harvard
Uygun, E. and Cesur, K. (2023). Translation of the MOOC Student Satisfaction Survey to Turkish: A Scale Adaptation and Validation Study . International Journal of Progressive Education, 19(5), pp. 153-171.

Chicago 16th edition
Uygun, Emre and Kursat Cesur (2023). "Translation of the MOOC Student Satisfaction Survey to Turkish: A Scale Adaptation and Validation Study ". International Journal of Progressive Education 19 (5):153-171. doi:10.29329/ijpe.2023.603.10.
References
  1. Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2019). Factors affecting student dropout in MOOCs: A cause and effect decision-making model. Journal of Computing in Higher Education, 32, 429-454. https://doi.org/10.1007/s12528-019-09241-y  [Google Scholar] [Crossref] 
  2. Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006 [Google Scholar] [Crossref] 
  3. Astin, A. W. (1984). Student satisfaction and participation in the college classroom. Research in Higher Education, 21(2), 153–164. https://doi.org/10.1007/BF00988238  [Google Scholar] [Crossref] 
  4. Astin, A. W. (1993). What matters in college? Four critical years revisited. Jossey-Bass. [Google Scholar]
  5. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921  [Google Scholar] [Crossref] 
  6. Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing 13(2), 139-161. https://doi.org/10.1016/0167-8116(95)00038-0 [Google Scholar] [Crossref] 
  7. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Taylor and Francis Group Publication. [Google Scholar]
  8. Chiu, C-M., Hsu, M-H., Sun, S-Y., Lin T-C., & Sun, P-C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399-416. https://doi.org/10.1016/j.compedu.2004.06.001 [Google Scholar] [Crossref] 
  9. Christensen, G., Steinmetz, A., Alcorn, B., Bennett, A., Woods, D., & Emanuel, E. J. (2014). The MOOC phenomenon: Who takes massive open online courses and why? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2350964  [Google Scholar] [Crossref] 
  10. Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press. [Google Scholar]
  11. Creswell, J. W. (2002). Educational research: planning, conducting, and evaluating quantitative. Prentice Hall. [Google Scholar]
  12. Demirci, C., & Akcaalan, M. (2022). The adaptation of language learning curiosity scale into Turkish language. International Journal of Educational Research Review, 7(1), 48-55. https://doi.org/10.24331/ijere.1019300 [Google Scholar] [Crossref] 
  13. De Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32, 281-231. https://doi.org/10.1111/jcal.12130 [Google Scholar] [Crossref] 
  14. DeVellis, R. F. (2017). Scale development theory and applications (4th ed.). SAGE Publications. [Google Scholar]
  15. Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling, 2(2), 119-143. https://doi.org/10.1080/10705519509540000  [Google Scholar] [Crossref] 
  16. Doll, W. J., Xia, W., Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 357–369. https://doi.org/10.1111/j.1540-5414.2003.02428.x [Google Scholar] [Crossref] 
  17. Elia, G., Solazzo, G., Lorenzo, G., & Passiante, G. (2019). Assessing learners’ satisfaction in collaborative online courses through a big data approach. Computers in Human Behavior, 92, 589-599. https://doi.org/10.1016/j.chb.2018.04.033  [Google Scholar] [Crossref] 
  18. Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681. [Google Scholar]
  19. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. https://doi.org/10.2307/3151312 [Google Scholar] [Crossref] 
  20. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059  [Google Scholar] [Crossref] 
  21. Gameel, B. G. (2017). Learner satisfaction with massive open online courses. American Journal of Distance Education, 31(2), 98-111. https://doi.org/10.1080/08923647.2017.1300462  [Google Scholar] [Crossref] 
  22. Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Lawrence Erlbaum Associates. [Google Scholar]
  23. Göktaş, M. (2019). Evaluating massive open online course participants in terms of environmental factors [Unpublished master’s thesis]. Fırat University, Turkiye. [Google Scholar]
  24. Groth-Marnat, G., & Wright, A. J. (2016). Handbook of psychological assessment (6th ed.). Wiley. [Google Scholar]
  25. Gunawardena, C. N., & McIsaac, M. N. (2008). Distance education. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (pp. 355-395). Lawrence Erlbaum Associates. [Google Scholar]
  26. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Pearson Education India. [Google Scholar]
  27. Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, Article 103724, https://doi.org/10.1016/j.compedu.2019.103724  [Google Scholar] [Crossref] 
  28. Hunter, M. C. (1976). Improved instruction. Theory Into Practice (TIP) Publications. [Google Scholar]
  29. İskifoğlu, G., & Ağazade, A. S. (2013). Translation and validation of a Turkish version of the California critical thinking disposition inventory. Social Behavior and Personality: An International Journal, 41(2), 187–196. https://doi.org/10.2224/sbp.2013.41.2.187 [Google Scholar] [Crossref] 
  30. İşgör Şimşek, E., & Turan, B. O. (2017). Evaluation of massive open online courses (MOOC) usability in mobile platforms. Mersin University Journal of the Faculty of Education, 13(2), 595-608. https://doi.org/10.17860/mersinefd.336745  [Google Scholar] [Crossref] 
  31. Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272. https://doi.org/10.1016/j.compedu.2018.01.003  [Google Scholar] [Crossref] 
  32. Kara, M., Kukul, V., & Çakır, R. (2021). Self-regulation in three types of online interaction: How does it predict online pre-service teachers’ perceived learning and satisfaction? The Asia-Pacific Education Researcher, 30(1), 1–10. https://doi.org/10.1007/s40299-020-00509-x [Google Scholar] [Crossref] 
  33. Kirkpatrick, J., & Kirkpatrick, W. K. (2021). An introduction to the New World Kirkpatrick Model. Kirkpatrick Partners. https://www.kirkpatrickpartners.com/wp-content/uploads/2021/11/Introduction-to-the-Kirkpatrick-New-World-Model.pdf [Google Scholar]
  34. Klein, S., Astrachan, J., & Smyrnios, K. (2005). The F-PEC scale of family influence: Construction, validation and further implication for theory. Entrepreneurship Theory and Practice, 29(3), 321-39. https://doi.org/10.1111/j.1540-6520.2005.00086.x [Google Scholar] [Crossref] 
  35. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press. [Google Scholar]
  36. Konting, M. M., Kamaruddin, N., & Man, N. A. (2009). Quality assurance in higher education institutions: Exit survey among Universiti Putra Malaysia graduating students. International Education Studies, 2(1), 25–31. [Google Scholar]
  37. Kumar, P., & Kumar. N. (2020). A study of learner’s satisfaction from MOOCs through a mediation model. Procedia Computer Science, 173, 354-363. https://doi.org/10.1016/j.procs.2020.06.041  [Google Scholar] [Crossref] 
  38. Kyriazos, T. A., & Stalikas, A. (2018). Applied psychometrics: The steps of scale development and standardization process. Psychology, 9, 2531-2560. https://doi.org/10.4236/psych.2018.911145  [Google Scholar] [Crossref] 
  39. Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105-117. [Google Scholar]
  40. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35, 382–385. [Google Scholar]
  41. Ma, L., & Lee, C. S. (2018). Understanding the barriers to the use of MOOCs in a developing country: An innovation resistance perspective. Journal of Educational Computing Research, 57(3), 571–590. https://doi.org/10.1177/0735633118757732  [Google Scholar] [Crossref] 
  42. MacCallum, R. C., Browne, M. W., & Sugawara, H., M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-49. https://doi.org/10.1037/1082-989X.1.2.130 [Google Scholar] [Crossref] 
  43. Mehlenbacher, B., & Mehlenbacher, A. R. (2020). Distance education. In A. Tatnall (Ed.), Encyclopaedia of education and information technologies (pp. 612-622). Springer. https://doi.org/10.1007/978-3-030-10576-1  [Google Scholar] [Crossref] 
  44. Mendi, B., & Mendi, O. (2015). Evaluation of validity and reliability of the Turkish version of the e-lifestyle instrument. Journal of Yasar University, 10(40), 6624-6632. https://doi.org/10.19168/jyu.37431  [Google Scholar] [Crossref] 
  45. Miller, S. L. (2015). Teaching an online pedagogy MOOC. MERLOT Journal of Online Learning and Teaching, 11(1), 104-119. [Google Scholar]
  46. Moore, J. C. (2005). The Sloan Consortium quality framework and the five pillars. The Sloan Consortium. http://www.mit.jyu.fi/OPE/kurssit/TIES462/Materiaalit/Sloan.pdf [Google Scholar]
  47. Orçan, F. (2018). Exploratory and confirmatory factor analysis: Which one to use first? Journal of Measurement and Evaluation in Education and Psychology, 9(4), 414-421. https://doi.org/10.23031/epod.394323  [Google Scholar] [Crossref] 
  48. Pratton, J., & Hales, L. W. (1986). The effects of active participation on student learning. Journal of Educational Research, 79(4), 210-215. https://doi.org/10.1080/00220671.1986.10885679 [Google Scholar] [Crossref] 
  49. Sallam, M. H., Martin-Monje, E., & Li, Y. (2022). Research trends in language MOOC studies: A systematic review of the published literature (2012-2018). Computer Assisted Language Learning, 35(3), 764-791. https://doi.org/10.1080/09588221.2020.1744668  [Google Scholar] [Crossref] 
  50. Shrader, S., Wu, M., Owens, D., & Ana, K. S. (2016). Massive open online courses (MOOCs): Participant activity, demographics, and satisfaction. Online Learning Journal, 20(2), 199-216. https://doi.org/10.24059/olj.v20i2.596  [Google Scholar] [Crossref] 
  51. So, H-J., & Brush, T. A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers & Education, 51(1), 318-336. https://doi.org/10.1016/j.compedu.2007.05.009 [Google Scholar] [Crossref] 
  52. Şahin, E. B., & Durdu, P. O. (2021). Usability evaluation of massive open online courses (MOOC) websites with the cognitive walkthrough. The Journal of Information Technologies, 14(4), 377-389. https://doi.org/10.17671/gazibtd.871801  [Google Scholar] [Crossref] 
  53. Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). Harper Collins. [Google Scholar]
  54. Tay, L., & Jebb, A. (2017). Scale development. In S. Rogelberg (ed.), The SAGE encyclopedia of industrial and organizational psychology (2nd ed.). SAGE Publications. [Google Scholar]
  55. Teo, T. S. H., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99-132. https://doi.org/10.2753/MIS0742-1222250303  [Google Scholar] [Crossref] 
  56. Thomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251-269. https://doi.org/10.1023/b:rihe.0000019589.79439.6e  [Google Scholar] [Crossref] 
  57. Tuğsal, T. (2020). Translation, adaptation, validity, and reliability of the sense-making scale: A cross-cultural evidence from India, Malaysia, Romania, and Turkey. Electronic Journal of Social Sciences, 19(76), 1810-1848. https://doi.org/10.17755/esosder.710803 [Google Scholar] [Crossref] 
  58. Ustaoğlu, M. A., & Kukul, V. (2022). Gaining an insight into learner satisfaction in MOOCs: An investigation through blog mining. Open Praxis, 14(3), 230–241. https://doi.org/10.55982/openpraxis.14.3.490 [Google Scholar] [Crossref] 
  59. Wild, D., Grove, A., Martin, M., Eremenco, S., McElroy, S., Verjee-Lorenz, A., Erikson, P., & ISPOR Task Force for Translation and Cultural Adaptation (2005). Principles of good practice for the translation and cultural adaptation process for Patient-Reported Outcomes (PRO) Measures: Report of the ISPOR Task Force for Translation and Cultural Adaptation. Value in Health, 8(2), 94–104. [Google Scholar]
  60. https://doi.org/10.1111/j.1524-4733.2005.04054.x  [Google Scholar]
  61. Wu, J.-H., Tennyson, R. D., & Hsia, T.L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164. https://doi.org/10.1016/j.compedu.2009.12.012  [Google Scholar] [Crossref] 
  62. Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding student motivation, behaviors and perceptions in MOOCs. In D. Cosley, A. Forte, L. Ciolfi, & D. McDonald (Eds.), Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 1882–1895). Association for Computing Machinery. https://doi.org/10.1145/2675133.2675217  [Google Scholar] [Crossref] 
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