Examination of Intention to Use Synchronous E-Classroom Environments of University Students in Distance Education Programs

Öz

The aim of this study is to investigate the variables affecting the tendency of the students to use the synchronous virtual classroom and to analyze the causal relations between these variables. For this purpose the Synchronous Virtual Classroom Acceptance Scale which was developed by Kang and Shin (2015) was adapted to Turkish and tested in this study. Synchronous Virtual Classroom Acceptance Model was used as theoretical background. The study group consists of the prospective teachers (n=310) studying at various departments of a state university. The hypothetical model was tested with the structural equation modeling. Research findings confirmed the hypothetical model based on Synchronous Virtual Classroom Acceptance Model. The findings showed that the students’ intention to use the synchronous virtual classroom could be explained directly or indirectly by self-efficacy, systematic lecture content, subjective norm, system accessibility, perceived usefulness, and perceived ease of use and the resultant model produced a valid, reliable and good fit. 77% of the variance observed in behavioural intention explained by perceived usefulness and perceived ease of use. Self-efficacy and subjective norm explained about 82% of the variance observed in the perceived usefulness. 73% of the variance seen in perceived ease of use explained by all exogenous variables.

Anahtar Kelimeler

• Synchronous E-Classroom
• Synchronous E-Learning
• Technology Acceptance Model
• Structural Equation Model
• Distance Learning

Uzaktan Eğitim Programlarında Üniversite Öğrencilerinin Eş Zamanlı Sanal Sınıf Ortamlarını Kullanım Niyetlerinin İncelenmesi

Öz

Bu çalışmanın amacı, öğrencilerin eş zamanlı sanal sınıf kullanma eğilimlerine etki eden değişkenleri ve bu değişkenler arasındaki nedensel ilişkileri incelemektir. Bu amaçla Kang ve Shin (2015) tarafından geliştirilen Eş Zamanlı Sanal Sınıf Kabul Ölçeği (E-SSKÖ) Türkçeye uyarlanarak test edilmiştir. Araştırmada Eş Zamanlı Sanal Sınıf Kabul Modeli (E-SSKM) kuramsal temel olarak ele alınmıştır. Araştırmanın çalışma grubunu, bir devlet üniversitesinin çeşitli bölümlerinde öğrenim gören 1. sınıf öğrencileri (n=310) oluşturmaktadır. Kurulan hipotetik model yapısal eşitlik modeli ile sınanmıştır. Araştırma bulguları E-SSKM temel alınarak kurgulanan hipotetik modeli doğrulamıştır. Araştırma sonucunda, öğrencilerin eş zamanlı sanal sınıf kullanım niyetinin, öz-yeterlilik, yapılandırılmış ders içeriği, öznel norm, sistem erişilebilirliği, yarar algısı, kullanım kolaylığı algısı değişkenlerince doğrudan veya dolaylı olarak açıklanabildiği ve ortaya çıkan modelin geçerli, güvenilir ve iyi bir uyum sağladığı görülmüştür. Araştırma modelinin hedef değişkeni durumundaki davranışsal niyette görülen varyans değişiminin %77’si kullanım kolaylığı algısı ve yarar algısı tarafından açıklanabilmiştir. Öz-yeterlilik ve öznel norm değişkenleri, yarar algısı değişkeninde görülen varyans değişimin %82’sini açıklamıştır. Dışsal değişkenler ise, kullanım kolaylığı algısı değişkeninde görülen varyans değişiminin %73’ünü açıklamaktadır.

Anahtar Kelimeler

• Eş Zamanlı E-Sınıf
• Eş Zamanlı E-Öğrenme
• Teknoloji Kabul Modeli
• Yapısal Eşitlik Modeli
• Uzaktan Öğrenme

Kaynakça

1. Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238-256. doi: 10.1016/j.chb.2015.11.036
2. Ajzen, I. (1985). From intentions to behavior: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-Control: From cognition to behavior (pp. 11–39). Heidelberg: Springer.
3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi: 10.1016/0749-5978(91)90020-T
4. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
5. Anastasiades, P. S., Filippousis, G., Karvunis, L., Siakas, S., Tomazinakis, A., Giza, P., & Mastoraki, H. (2010). Interactive videoconferencing for collaborative learning at a distance in the school of 21st century: A case study in elementary schools in Greece. Computers & Education, 54(2), 321-339. doi: 10.1016/j.compedu.2009.08.016
6. Anohina, A. (2005). Analysis of the terminology used in the field of virtual learning. Journal of Educational Technology & Society, 8(3), 91-102.
7. Aypay, A. (2010). Öz yeterlik ölçeğinin Türkçe’ye uyarlama çalışması. İnönü Üniversitesi Eğitim Fakültesi Dergisi, 11(2), 113-131.
8. Aypay, A., Celik, H. C., Aypay, A., & Sever, M. (2012). Technology acceptance in education: A study of pre-service teachers in Turkey. Turkish Online Journal of Educational Technology-TOJET, 11(4), 264-272.

9. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. doi: 10.1007/BF02723327
10. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. doi: 10.1037/0033-295X.84.2.191
11. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice Hall.
12. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. doi: 10.1037/0033-2909.88.3.588
13. Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., ..., & Huang, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379-439. doi: 10.3102/00346543074003379
14. Bhatiasevi, V. (2011). Acceptance of e-learning for users in higher education: An extension of the technology acceptance model. The Social Sciences, 6(6), 513-520. doi: 10.3923/sscience.2011.513.520
15. Brown, T.A. (2006), Confirmatory factor analysis for applied research. ABD: Guilford Press.
16. Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. doi: 10.1016/j.compedu.2017.04.010
17. Chapman, D., & Wiessner, C. (2008). Exploring engaged learning as a tool for evaluating web conferencing. In C. Bonk et al. (Eds.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2008, Chesapeake, VA: AACE.
18. Chen, N. S., Ko, H. C., Kinshuk, & Lin, T. (2005). A model for synchronous learning using the Internet. Innovations in Education and Teaching International, 42(2), 181-194. doi: 10.1080/14703290500062599
19. Cheng, Y. M. (2011). Antecedents and consequences of e‐learning acceptance. Information Systems Journal, 21(3), 269-299. doi: 10.1111/j.1365-2575.2010.00356.x
20. Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390. doi: 10.12691/ajss-2-5-2
21. Collis, B. (1996). Tele-learning in a digital world: The future of distance learning. London: International Thomson Computer Press.
22. Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158. doi: 10.2307/249749
23. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. doi: 10.1007/BF02310555
24. Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211. doi: 10.1016/j.chb.2016.02.066
25. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. doi: 10.2307/249008
26. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
27. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. doi: 10.1080/07421222.2003.11045748
28. Finkelstein, J. (2006). Learning in real time: Synchronous teaching and learning online. San Francisco: Jossy-Bass Publishing Company.
29. Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention and behaviour: an introduction to theory and research. Reading, MA: Addison-Wesley.
30. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. doi: 10.2307/3151312
31. Granda, J. C., Nuño, P., Suárez, F. J., & Pérez, M. A. (2013). E-pSyLon: a synchronous e-learning platform for staff training in large corporations. Multimedia Tools and Applications, 66(3), 431-463. Doi: 10.1007/s11042-012-1061-9
32. Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-30.
33. Hastie, M., Hung, I. C., Chen, N. S., & Kinshuk (2010). A blended synchronous learning model for educational international collaboration. Innovations in Education and Teaching International, 47(1), 9-24. doi: 10.1080/14703290903525812
34. Hrastinski, S. (2008). The potential of synchronous communication to enhance participation in online discussions: A case study of two e-learning courses. Information & Management, 45(7), 499-506. doi: 10.1016/j.im.2008.07.005
35. Hsia, J. W., & Tseng, A. H. (2008). An enhanced Technology Acceptance Model for e-learning systems in high-tech companies in Taiwan: Analyzed by Structural Equation Modeling. International Conference on Cyberworlds (pp. 39-44). Hangzhou: China.
36. Johnson, G. M. (2006). Synchronous and asynchronous text-based CMC in educational contexts: A review of recent research. TechTrends, 50(4), 46. doi: 10.1007/s11528-006-0046-9
37. Kang, M., & Shin, W. S. (2015). An empirical investigation of student acceptance of synchronous e-learning in an online university. Journal of Educational Computing Research, 52(4), 475-495. doi: 10.1177/0735633115571921
38. Khan, B. H. (2006). Flexible Learning in an Information Society: Hershey PA17033: Information Science Publishing, USA.
39. Kim, B. G., Park, S. C., & Lee, K. J. (2007). A structural equation modeling of the Internet acceptance in Korea. Electronic Commerce Research and Applications, 6(4), 425-432. doi: 10.1016/j.elerap.2006.08.005
40. Kline, R. B. (2011). Convergence of structural equation modeling and multilevel modeling. In M. Williams (Ed.), Handbook of methodological innovation. Thousand Oaks, CA: Sage.
41. Kuo, Y. C., Kuo, Y. T., & Walker, A. (2010). The effect of student interactions and Internet self-efficacy on satisfaction in two synchronous Interwise course sessions. Proceedings of Global Learn Asia Pacific 2010 (pp. 4242-4246). Chesapake, VA: AACE.
42. Lau, S. H., & Woods, P. C. (2008). An investigation of user perceptions and attitudes towards learning objects. British Journal of Educational Technology, 39(4), 685-699. doi: 10.1111/j.1467-8535.2007.00770.x
43. Lau,. S.-H. & Woods, P. C. (2009). Understanding learner acceptance of learning objects: The roles of learning object characteristics and individual differences. British Journal of Educational Technology, 40, 1059-1075. doi: 10.1111/j.1467-8535.2008.00893.x
44. Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329. doi: 10.1016/j.compedu.2009.06.014
45. Lee, J.-S., Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2003). Technology acceptance and social networking in distance learning. Educational Technology & Society, 6(2), 50-61.
46. Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516. doi: 10.1016/j.compedu.2009.09.002
47. Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095-1104. doi: 10.1016/j.im.2003.10.007
48. Li, Y. H., & Huang, J. W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Academy of Science, Engineering and Technology, 53(1), 919-925.
49. Lim, C. L. (2010). Student perceptions of the use of elluminate live! for synchronous e-learning. International Journal of Arts and Sciences, 3(11), 123-136.
50. Lin, J. C. C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197-208. doi: 10.1016/S0268-4012(00)00005-0
51. Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599-607. doi: 10.1016/j.compedu.2008.11.002
52. Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600-610. doi: 10.1016/j.compedu.2009.09.009
53. Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21(6), 387-395. doi: 10.1111/j.1365-2729.2005.00145.x
54. Martin, F. (2010). Best practices for teaching in a synchronous virtual classroom. 2010 International Conference on Technology for Education (pp. 44-46). Mumbai, India.
55. Martin, F., Parker, M. A., & Deale, D. F. (2012). Examining interactivity in synchronous virtual classrooms. The International Review of Research in Open and Distributed Learning, 13(3), 228-261. doi: 10.19173/irrodl.v13i3.1174
56. McBrien, J. L., Jones, P., & Cheng, R. (2009) Virtual spaces: Employing a synchronous online classroom to facilitate student engagement in online learning. International Review of Research in Open and Distance Learning, 10(3), 1-17. doi: 10.19173/irrodl.v10i3.605
57. Morris, M. G., & Dillion, A. (1997). How user precautions information software use, software. IEEE Software, 14(4), 58-65. doi: 10.1109/52.595956
58. Nunnally, J. (1978). Psychometric methods. McGraw Hill, New York.
59. Oztok, M., Zingaro, D., Brett, C., & Hewitt, J. (2013). Exploring asynchronous and synchronous tool use in online courses. Computers & Education, 60(1), 87-94. doi: 10.1016/j.compedu.2012.08.007
60. Özkök, G. A. (2013a). Reliability and validity of the Turkish version of the web-based learning environment instrument (WEBLEI), Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 28(2), 335-347.
61. Özkök, G. A. (2013b). Web-tabanlı öğrenme ortamlarında yaratıcı problem çözme öğretim yönteminin tasarımı, Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, Özel Sayı-1, 335-347.
62. Özkök, G. A. (2009). Çevrimiçi öğrenme ortamlarında disiplinler arası yaklaşım. XI. Akademik Bilişim 2009’da sunulan bildiri, Harran Üniversitesi, Şanlıurfa.
63. Pan, C. C., & Sullivan, M. (2005). Promoting synchronous interaction in an eLearning environment. The Journal, 33(2), 27-30.
64. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162.
65. Park, S. Y., Nam, M., & Cha, S. (2012). University students; behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605. doi: 10.1111/j.1467-8535.2011.01229.x
66. Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51.
67. Ramírez-Correa, P. E., Arenas-Gaitán, J., & Rondán-Cataluña, F. J. (2015). Gender and acceptance of e-learning: a multi-group analysis based on a structural equation model among college students in Chile and Spain. PloS One, 10(10), 1-17. doi: 10.1371/journal.pone.0140460
68. Santos, J. R. A. (1999). Cronbach’s alpha: A tool for assessing the reliability of scales. Journal of Extension, 37(2), 1-5.
69. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
70. Schullo, S., Hilbelink, A., Venable, M., & Barron, A. E. (2007). Selecting a virtual classroom system: Elluminate Live vs. Macromedia Breeze (Adobe Acrobat Connect Professional). MERLOT Journal of Online Learning and Teaching, 3(4), 331-345.
71. Shahabadi, M. M., & Uplane, M. (2015). Synchronous and asynchronous e-learning styles and academic performance of e-learners. Procedia-Social and Behavioral Sciences, 176, 129-138. doi: 10.1016/j.sbspro.2015.01.453
72. Sharp, V. (2004). Computer education for teachers: Integrating technology into classroom teaching (5th ed.). New York, NY: McGraw-Hill.
73. Shen, D., Laffey, J., Lin, Y., & Huang, X. (2006). Social influence for perceived usefulness and ease-of-use of course delivery systems. Journal of Interactive Online Learning, 5(3), 270-282.
74. Skylar, A. A. (2009). A comparison of asynchronous online text-based lectures and synchronous interactive web conferencing lectures. Issues in Teacher education, 18(2), 69-84.
75. Smyth, R. (2011). Enhancing learner–learner interaction using video communications in higher education: Implications from theorising about a new model. British Journal of Educational Technology, 42(1), 113-127. doi: 10.1111/j.1467-8535.2009.00990.x
76. Şimşek, Ö. F. (2007). Yapısal eşitlik modellemesine giriş: Temel ilkeler ve Lisrel uygulamaları. Ankara: Ekinoks.
77. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). United States: Pearson Education.
78. Thong, J. Y., Hong, W., & Tam, K. Y. (2002). Understanding user acceptance of digital libraries: What are the roles of interface characteristics, organizational context, and individual differences?. International Journal of Human-Computer Studies, 57(3), 215-242. doi: 10.1016/S1071-5819(02)91024-4
79. Wang, Y., Chen, N. S., & Levy, M. (2010). The design and implementation of a holistic training model for language teacher education in a cyber face-to-face learning environment. Computers & Education, 55(2), 777-788. doi: 10.1016/j.compedu.2010.03.010
80. Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852. doi: 10.1016/j.compedu.2006.09.001
81. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. doi: 10.1287/mnsc.46.2.186.11926
82. Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e‐learning technology. Asia‐Pacific Journal of Teacher Education, 36(3), 229-243. doi: 10.1080/13598660802232779