IDENTIFYING FACTORS IMPACTING ONLINE LEARNING

Dennis Kira, Raafat George Saadé, Xin He

Abstract

The study presented in this paper sought to explore several dimensions to online learning. Identifying the dimensions to online learning entails important basic issues which are of great relevance to educators today. The primary question is “what are the factors that contribute to the success/failure of online learning?” In order to answer this question we need to identify the important variables that (1) measure the learning outcome and (2) help us understand the learning experience of students using specific learning tools. In this study, the dimensions we explored are student’s attitude, affect, motivation and perception of an Online Learning Tool usage. A survey utilizing validated items from previous relevant research work was conducted to help us determine these variables. An exploratory factor analysis (EFA) was used for a basis of our analysis. Results of the EFA identified the items that are relevant to the study and that can be used to measure the dimension to online learning. Affect and perception were found to have strong measurement capabilities with the adopted items while motivation was measured the weakest.

References

  1. Agarwal, R., and Karahanna, E., 2000. Time Flies When You're Having Fun: Cognitive Absorption and Beliefs, About Information Technology Usage. MIS Quarterly, Vol. 24 No. 4, pp. 665-694.
  2. Arkkelin, D., 2003. Putting Prometheus' Feet to the Fire: Student Evaluations of Prometheus in Relation to Their Attitudes Towards and Experience With Computers, Computer Self-Efficacy and Preferred LearningStyle. http://faculty.valpo.edu/darkkeli/papers/syllabus03.ht m (Last accessed on April 17, 2004).
  3. Bergeron, F., Raymond, L. Rivard, S. and Gara, S., 1995. Determinants of EIS Use: Testing a Behavioral Model. Decision Support System, Vol. 14, pp. 131- 146.
  4. Daley, B. J., Watkins, K., Williams, W., Courtenay, B. and Davis, M., 2001. Exploring Llearning in a Technology-Enhanced Environment. Educational Technology & Society, Vol. 4, No. 3.
  5. Davies, R. S., 2003. Learner Intent and Online Courses. The Journal of Interactive Online Learning, Vol. 2, No. 1.
  6. Davis, F.D., 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, Vol. 13, pp. 319-340.
  7. Davis, D. F., Bagozzi, R.P. Warshaw,P.R. 1989.User Acceptance of Computer Technology: a Comparison of Two Theoretical Models. Management science, Vol. 35, No. 8, pp.982-1003.
  8. Davis, F.D., Davis, G.B. and Warshaw, P.R., 1992. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, Vol. 35, No. 12, pp. 982-1003.
  9. Deci, E L., and Ryan, R.M., 1985. Intrinsic Motivation and Self-determination in Human Behavior. Plenum, New York.
  10. Eklund, J. and Eklund, P. (1996), “Integrating the Web and The Teaching of Technology: Cases Across Two Universities,” Proceedings of the AusWeb96, The Second Australian WorldWideWeb Conference, Gold Coast, Australia. Available online at: http://wwwdev.scu.edu.au/sponsored/ausweb/ausweb9 6/educn/eklund2 (last accessed Feb, 2004).
  11. Feenberg, A., 1987. Computer Conferencing and the Humanities. Instructional Science, Vol. 16, pp. 169- 186.
  12. Faigley, L. 1990. Subverting the electronic workbook: Teaching writing using networked computers. In D. Baker & M. Monenberg (Eds.), The writing teacher as researcher: Essays in the theory and practice of classbased research. Portsmouth, NH: Heinemann.
  13. Field, A. (2000). Discovering Statistics using SPSS for Windows. London - Thousand Oaks -New Delhi: Sage publications.
  14. Hair, J.F. Jr. , Anderson, R.E., Tatham, R.L., & Black, W.C. (1998). Multivariate Data Analysis, (5thEdition). Upper Saddle River, NJ: Prentice Hall.
  15. Irani, T., 1998. Communication Potential, Information Richness and Attitude: A Study of Computer Mediated Communication in the ALN Classroom. ALM Magazine, Vol. 2, No. 1.
  16. Krendl, K. A., and Lieberman, D. A., 1988. Computers and Learning: A Review of Recent Research. Journal of Educational Computing Research, Vol. 4, No. 4, pp. 367-389.
  17. Kum, L. C., 1999. A Study Into Students' Perceptions of Web-Based Learning Environment. HERDSA ANNUAL International Conference, Melbourne, pp. 12-15.
  18. Lowell, R., 2001. The Pew Learning and Technology Program Newsletter. http://www.math.hawaii.edu/dale/pew.html (last accessed Feb, 2004).
  19. Marzano, R. J. & Pickering, D. J., 1997. Dimensions of learning (2nd ed.), Alexandria, VA: Association for Supervision and Curriculum Development.
  20. Picciano, G. A., 2002. Beyond Student Perceptions: Issues of Interaction, Presence and Performance in an Online Course. JALN, Vol. 6, No. 1, pp. 21-40.
  21. Poole, B. J. and Lorrie J., 2003. Education Online: Tools for Online Learning. Education for an information age, teaching in the computerized classroom, 4th edition.
  22. Richardson, C. J. and K. Swan, K., 2003. Examining Social Presence in Online Courses in Relation to Students' Perceived Learning and Satisfaction. JALN, Vol. 7, No. 1, pp. 68-88.
  23. Saadé, G. R., 2003. Web-based Educational Information System for Enhanced Learning, (EISEL): Student Assessment. Journal of Information Technology Education, (2), pp. 267-277. Available online: http://jite.org/documents/Vol2/v2p267-277-26.pdf (last accessed Feb, 2004).
  24. Stevens, J., 2002. Applied Multivariate Statistics for the Social Sciences (4th Edition). Mahwah, NJ: Lawrence Erlbaum Associates.
  25. Sunal, W. D., Sunal, S.C., Odell, R.M. and Sundberg, A.C., 2003. Research-Supported Best Practices for Developing Online Learning. The Journal of Interactive Online Learning, Vol. 2, No. 1, pp. 1-40.
  26. Triandis, C. H. (1979), “Values, Attitudes, and Interpersonal Behavior,” Nebraska Symposium on Motivation, 1979: Beliefs, Attitudes and Values, Lincoln, NE: University of Nebraska Press, pp. 159 - 295.
  27. Vallerand, R. J.,1997. Toward a Hierarchical Model of Intrinsic and Extrinsic Motivation. Advances in Experimental Social Psychology, Vol. 29, pp. 271- 374.
  28. Venkatesh, V., 1999. Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation. MIS Quarterly, Vol. 23, No. 2, pp. 239- 260.
  29. Venkatesh, V. and Davis, F. D., 2000., A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies., Management Science, Vol. 46, No. 2, pp. 186-204.
  30. Venkatesh, V., Speier, C. and Morris, M.G. 2002. User Acceptance Enablers in Individual Decision-Making About Technology: Toward an Integrated Model. Decision Sciences, Vol. 33, pp. 297-316.
  31. Venkatesh, V., Morris, M.G. Davis,F.D. and Davis, G.B., 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, Vol. 27, pp. 425-478.
  32. Wlodkowski, R. J ,1999. Enhancing Adult Motivation to Learn, Revised Edition, a Comprehensive Guide for Teaching All Adults. San Francisco, CA: JosseyBass.
Download


Paper Citation


in Harvard Style

Kira D., George Saadé R. and He X. (2005). IDENTIFYING FACTORS IMPACTING ONLINE LEARNING . In Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 972-8865-20-1, pages 457-465. DOI: 10.5220/0001232904570465


in Bibtex Style

@conference{webist05,
author={Dennis Kira and Raafat George Saadé and Xin He},
title={IDENTIFYING FACTORS IMPACTING ONLINE LEARNING},
booktitle={Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2005},
pages={457-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001232904570465},
isbn={972-8865-20-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - IDENTIFYING FACTORS IMPACTING ONLINE LEARNING
SN - 972-8865-20-1
AU - Kira D.
AU - George Saadé R.
AU - He X.
PY - 2005
SP - 457
EP - 465
DO - 10.5220/0001232904570465