UPSI Digital Repository (UDRep)
|
|
|
Full Text : |
The study aims to evaluate an independent learning measurement model based on connectivism theory and Web 2.0. The quantitative method is used in this study. The data is obtained through the instrument of connectivism theory and Facebook usage. The subject of this study was 81 students of Two Year Programme in one of the matriculation colleges in Malaysia. These respondents were selected based on purposive sampling. The statistical analysis involved descriptive statistics and Partial Least Squares-Structural Equation Modeling (PLS-SEM) as the method used in this study. The findings indicated that there were significant structural relationships between connectivism theory and Web 2.0 towards students' achievement. Furthermore, the structural model showed that students' achievement is influenced by the principles of connectivism theory and Facebook as a learning tool. In conclusion, this study had successfully developed and evaluated an independent learning model based on connectivism theory and Web 2.0 through PLS-SEM. This study implied that apart from connectivism theory, Web 2.0 learning tool which is Facebook is also contributed a different perspective to the process of students' learning at matriculation colleges. |
References |
1. Altınay Gazi, A., Altınay Aksal, F. & Menemenci Bahçelerli, N. (2013). Practice of Connectivism
As Learning Theory: Enhancing Learning Process Through Social Networking Site (Facebook)/Gaziantep University Journal of Social Sciences (http://jss.gantep.edu.tr) JSS
12(2) (2013) Technology Special Issue: 243-252. ISSN: 1303-0094
2. Bernsteiner, R., Ostermann, H., & Staudinger, R. (2008). Facilitating e-learning with social software: Attitudes and usage from the student’s point of view. International Journal of Web-Based Learning and Teaching Technologies, 3(3), 16-33.
3. Campbell, T., Wang, S., Hsu, H.-Y., Duffy, A., & Wolf, P. (2010). Learning with web tools, simulations, and other technologies in science classrooms. Journal of Science Education and Technology, 19(5), 505-511. doi:10.1007/ s10956-010-9217-8
4. Candy, P. C. (1991). Self-Direction for Lifelong Learning. A Comprehensive Guide to Theory and Practice. Jossey-Bass, 350 Sansome Street, San Francisco, CA 94104-1310.
5. Crook, C., Cummings, J., Fisher, T., Graber, R., Harrison, C., & Lewin, C. (2008). Web 2.0 technologies for learning: The current landscape-opportunities, challenges and tensions.
6. Dabbagh, N. (2007). The online learner: Characteristics and pedagogical implications.Contemporary Issues in Technology and Teacher Education, 7(3), 217- 226.
7. Dabbagh, N., & Reo, R. (2011). Back to the future: Tracing the roots and learning affordances of
social software. Hershey, PA: IGI Global.
8. Downes, S. (2010). Learning networks and connective knowledge. In H. Yang & S. Yuen (Eds.), Collective intelligence and e-learning 2.0: Implications of web-based communities and networking (pp. 1-26). Hershey, PA: IGI Global.
9. Drexler, W., Baralt, A., & Dawson, K. (2008). The teach Web 2.0 consortium: A tool to promote
educational social networking and Web 2.0 use among educators. Educational Media International, 45(4), 271-283. doi:10.1080/09523980802571499
10. Du, F. (2012). Using study plans to develop self-directed learning skills: Implications from a pilot
project. College Student Journal, 46(1), 223-232.
11. Dunlap, J. C., & Lowenthal, P. R. (2011). Learning, unlearning, and relearning: Using Web 2.0
technologies to support the development of lifelong learning skills.
12. Fein, M. L. (2014). Redefining higher education: How self-direction can save colleges. New Brunswick, NJ: Transaction.
13. Fornell, C., & Bookstein, F.L. (1982). two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440-452.
14. Fornell, C., & Cha, J. (1994). Partial least squares. Advanced methods of marketing research, 407(3), 52-78.
15. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable and
measuremenr error Journal of Marketing Research, 34(2), 161-188.
16. Gefen, D., Straub, D.W., & Boudreau, M.C. (2000). Structural equation modelling and regression: Guidelines for research practice. Communication of the Association fo Information Systems, 4(7), 2-77.
17. Glud, L. N., Buus, L., Ryberg, T., Georgsen, M., & Davidsen, J. (2010). Contributing to a learning
methodology for Web 2.0 learning—Identifying central tensions in educational use of Web 2.0 technologies. In
L. Dirckinck-Holmfeld, V. Hodgson, C. Jones,M. de Laat, D. McConnell, & T. Ryberg (Eds.), Proceedings of the 7th International Conference on Networked Learning (pp.934–942). Retrieved from: http://www.lancs.ac.uk/fss/organisations/netlc/past/nlc2010/abstracts/PDFs/N%C3%B8rgaa
rd%20Glud.pdf
18. Greenhow, C., Robelia, B., & Hughes, J. E. (2009). Learning, teaching, and scholarship in a digital
age: Web 2.0 and classroom research: What path should we take “now”? Educational Researcher, 38(4), 246 259.doi:10.3102/0013189X09336671
19. Hair, J. F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of
Marketing Theory and Practice, 19(2), 139-151.
20. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial Least Squares
Structural Equation Modeling (PLS-SEM). Thousand Oaks, california: SAGE Publications.
21. Henseler, J., Ringle, C.M., & Sinkovics, R. (2009). The use of Partial Least Squares path modeling
in International Marketing. International Marketing, 20, 277-319.
22. Marcoulides, G.A., Chin, W.W., & Saunders, C. (2009). A critical look at Partial Least Square
Modeling. MIS Quarterly, 33(1), 171-175.
23. McLoughlin, C., & Lee, J. W. (2008). The three P‘s of pedagogy for the networked society: Personalization, participation, and productivity. International Journal of Teaching and Learning in Higher Education, 20(1), 10–27. Retrieved from http://www.isetl.org/ijtlhe/pdf/IJTLHE395.pdf
24. Meyer, B., Haywood, N., Sachdev. D., & Faraday S. (2008). Independent Learning Literature Review. (Research Report No. DCSF-RR05). Retrieved from Learning and Skills Network
25. Rouse, A.C., & Corbitt, B. (2008). There's SEM and "SEM": A critique of the use of PLS regression
in Information System researchAIS. Symposium conducted at the meeting of the 19th Australasian Conference on Information Systems, Christchurch, New Zealand.
26. Ryberg, T., Dirckinck-Holmfeld, L., & Jones, C. (2010a). Catering to the needs of the "digital
natives" or educating the "net generation"? In M. J. W. Lee & C. McLoughlin (Eds.), Web 2.0- Based E-Learning: Applying Social Informatics for Tertiary Teaching (pp. 301–318). Hershey, PA: IGI Global
27. Sinclair, B. (2001). What do we mean by learner independence and wrestling with a jelly: The evaluation of learner autonomy. Workshops given at the Higher Colleges of Technology, United Arab Emirates.
28. Torkzadeh, G., Koufteros, X., & Pflughoeft, K. (2003). Confirmatory analysis of computer selfefficacy. Structural Equation Modeling, 10(2), 263-275.
29. Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hiererchical construct models: Guidelines and empirical illustration. MIS Quarterly,33(1), 177-195 |
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |