Fuad A. A.TRAYEK Institute of Education International Islamic University Malaysia

Attitude Towards The Use Of Learning Management System 
Among University Students: A Case Study

Learning management system (LMS) is a learning platform for both full time and distant learning students at the International Islamic University in Malaysia (IIUM). LMS becomes a tool for IIUM to disseminate information and learning resources to the students. The objectives of this study were to Ø investigate students' attitudes toward the use of LMS, Ø to verify the impact of perceived usefulness and perceived ease of use on attitude towards use of learning management system, Ø to examine the differences in attitudes toward the use of LMS between distance learning and full time students. There were 120 (70 full time and 50 distance learning) students at the Institute of Education responded for the study. The collected data was analysed using descriptive statistics, t-test and Multiple Regression Analysis (MRA). The results of the study showed that perceived ease of use and perceived usefulness determine students' attitudes toward the use of LMS. However, this study did not find any significant differences between distance learning and full time students. According to the findings the study recommended that the University should continue using LMS because it is useful for both distance learning and full time students. Further suggestions are made to customize and upgrade the LMS suitable for innovative teaching and learning.

___

  • Al-Khalifa, H. S. (2010). E-learning in Saudi Arabia. In U. Demiray (Ed.). E-learning practices (Vol. 2). Eskisehir-Turkey: Anadolu University.
  • Bonwell, C. & Eison, J. (1991). Active learning: Creating excitement in the classroom
  • (ASHE-ERIC Higher Education Report No. 1). Washington, DC: George Washington University, p. 2. Bransford, J. (ed.). (1999). How people learn: Brain, Mind, Experience, and School. Washington, DC: National Research Council.
  • Brown, A., & Johnson, J. (2007). Five Advantages of Using a Learning Management
  • System. Microburst learning. Chen, H. R., & Huang, H. L. (2010). User Acceptance of Mobile Knowledge Management
  • Learning System: Design and Analysis. Educational Technology & Society, 13 (3), 70– Chuttur M. (2009). Overview of the Technology Acceptance Model: Origins,
  • Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. User acceptance of computer technology: a comparison of two theoretical models, Management Science 35(8), (1989) 982– 100
  • Dougiamas, M. & Taylor, P. (2003). Moodle: Using Learning Communities to Create an
  • Open Source Course Management system. In D. Lassner & C. McNaught (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2003 (pp. 171-178). Chesapeake, VA: AACE. Retrieved from http://www.editlib.org/p/13739
  • Ellis, R. K. (ed). (2009). Field Guide to Learning Management Systems. Learning
  • Circuits American Society for Training & Development. Gudanescu, N., (2012). E-Learning in Higher and Adult Education. www.intechopen.com
  • Iwasaki,C.; Tanaka,T. & Kubota (2003). Knowledge Management & E-Learning: An
  • International Journal, Vol.3, No.3. Kiraz, E., & Ozdemir, D. (2006). The relationship between educational ideologies and technology acceptance in pre-service teachers. Educational Technology and Society, 9(2), 152–165.
  • Klopping, I. M., & McKinney, E. J (2004) Information Technology, Learning, and Performance Journal, Vol. 24, No. 1.
  • Landry, B.J.L., Griffith, R. , & Hartman, S. (2006). Measuring student perceptions of blackboard using the technology acceptance model. Decision Sciences, 4(1), 87-99.
  • Liu, S.H., Liao, H. L., and Pratt, J.A. (2009). Impact of media richness and flow on e learning technology acceptance. Computers & Education , 52, 599–607.
  • Ma, Q., & Liu, L. (2004). The technology acceptance model: a meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16, 59–72.
  • Shroff, R. H., Deneen, Ch. C., and Eugenia M. W. Ng (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system. Australasian Journal of Educational Technology, 27(4), 600-618.
  • Smith, B. L., & MacGregor, J. T. (1992). What is collaborative learning? Olympia, WA:
  • WashingtonCenter for Improving the Quality of Undergraduate Education. Retrieved on 12 October 2012, from http://umdrive.memphis.edu/ggholson/public/collab.pdf.
  • Schaik, P. V. & Teo, T. (2009). Understanding Technology Acceptance in Pre-Service
  • Teachers: A Structural-Equation Modeling Approach. The Asia-Pacific Education Researcher, 18 (1), 47-66. Teo, T. (2009). Modelling technology acceptance in education: A study of pre- service teachers. Com-puters & Education, 52(1), 302-312.
  • Tims, C. (Ed.) (2010). Born Creative London: DEMOS.
  • Ustati, R., & Syed Hassan, Sh. S. (2013). Distance Learning Students’ Need: Evaluating
  • Interactions from Moore’s Theory of Transactional Distance. Turkish Online Journal of Distance Education-TOJDE, 14 (2). van Raaij, E. M. & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852.
  • Venkatesh, V. & Bala, H. (2008). Technology Acceptance Model 3 and a Research
  • Agenda on Interventions. Decision Sciences , (39:2) , 273–315. Yuen, A. H. K., Lee, M. W., Law, N. & Chan, A. (2008). Factors Predicting Impact of ICT
  • Use on Students: An Exploration of Teachers’ Perceptions. The proceedings of IRC. Yuen, H. K., & Ma, W.K. (2008) Exploring Teacher Acceptance of e-Learning
  • Technology. Asia-Pacific Journal of Teacher Education, 36(3), 229-243. Yunus, M. M. (2007). Malaysian ESL teachers’ use of ICT in their classrooms: expectations and realities, ReCALL, 19(1), 79-95.