The Determinants of Senior Students' Behavioral Intention on the Blended E-Learning for the Ceramics Teaching Course at the Active Aging University
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In this paper, the authors try to investigate the
determinants of behavioral intention of the blended E-learning course
for senior students at the Active Ageing University in Taiwan. Due to
lower proficiency in the use of computers and less experience on
learning styles of the blended E-learning course for senior students
will be expected quite different from those for most young students.
After more than five weeks course for two years the questionnaire
survey is executed to collect data for statistical analysis in order to
understand the determinants of the behavioral intention for senior
students. The object of this study is at one of the Active Ageing
University in Taiwan total of 84 senior students in the blended
E-learning for the ceramics teaching course. The research results show
that only the perceived usefulness of the blended E-learning course has
significant positive relationship with the behavioral intention.
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References
- OEPD, Executive Yuan.(2010). Estimation Report of Populations in Taiwan 2010~2060,Retrieved ,1, 2, 2013, from http://www.cepd.gov.tw/m1.aspx?sNo=0000455
- Y.-S.Kim, & Merriam, S. B.(2010). Situated learning and identity development in a Korean older adults' computer classroom. Adult Education Quarterly, 60(5), 438-455.
- C.C.Chang, Yan, C.F., & Tseng, J.S. (2012). Perceived convenience in anextended technology acceptance model: Mobile technology and English learningfor college students. Australasian Journal of Educational Technology, 28(5),809-826.
- B.Cheng, Wang, M., & Moormann, J. (2012). The Effects of Organizational Learning Environment Factors on E-Learning Acceptance. Computers and Education, 58(3), 885-899.
- K.-M. Lin,(2011). e-Learning continuance intention: Moderating effects of user e-learning experience. Computers and Education, 56, 515-526.
- T.Teo, (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers and Education, 52(1), 302-312.
- 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.
- T.Ahn, Ryu, S., & Han, I. (2007).The impact of web quality and playfulness on user acceptance of online retailing. Information and Management, 44(3), 263-275.
- S.-Y.Park, (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Educational Technology and Society, 12(3), 150-162. [10] Tourangeau,R. Rips, L.J. and Rasinski, K. (2000), "The psychology of survey response," CambridgeUniversity Press, Cambridge. [11] P.M. Podsakoff, MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003), "Common method biasesin behavioral research: a critical review of the literature and recommended remedies,"Journal of Applied Psychology,Vol. 88 No. 5, pp. 879-903. [12] W.-W. Chin, (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), VII-XVI. [13] D.Gefen, Karahanna, E., & Straub, D.W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. [14] D.Gefen, Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practives. Communication of the Association for Information Systems, 7(4), 1-79. [15] W.-W.Chin, Marcolin, B. L., & Newsted, P. R. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Information Systems Research, 14(2), 189-217.