- 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]
- 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]
- 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]
- Astin, A. W. (1993). What matters in college? Four critical years revisited. Jossey-Bass. [Google Scholar]
- 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]
- 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]
- Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Taylor and Francis Group Publication. [Google Scholar]
- 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]
- 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]
- Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press. [Google Scholar]
- Creswell, J. W. (2002). Educational research: planning, conducting, and evaluating quantitative. Prentice Hall. [Google Scholar]
- 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]
- 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]
- DeVellis, R. F. (2017). Scale development theory and applications (4th ed.). SAGE Publications. [Google Scholar]
- 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]
- 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]
- 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]
- Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681. [Google Scholar]
- 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]
- 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]
- 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]
- Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Lawrence Erlbaum Associates. [Google Scholar]
- Göktaş, M. (2019). Evaluating massive open online course participants in terms of environmental factors [Unpublished master’s thesis]. Fırat University, Turkiye. [Google Scholar]
- Groth-Marnat, G., & Wright, A. J. (2016). Handbook of psychological assessment (6th ed.). Wiley. [Google Scholar]
- 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]
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Pearson Education India. [Google Scholar]
- 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]
- Hunter, M. C. (1976). Improved instruction. Theory Into Practice (TIP) Publications. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35, 382–385. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- Miller, S. L. (2015). Teaching an online pedagogy MOOC. MERLOT Journal of Online Learning and Teaching, 11(1), 104-119. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). Harper Collins. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- https://doi.org/10.1111/j.1524-4733.2005.04054.x [Google Scholar]
- 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]
- 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]
|