Factors Affecting Students' Acceptance of e-Learning System in Higher Education

Nurhafizah Ahmad; Norazah Umar; Rozita  Kadar; Jamal  Othman

doi:10.24191/jcrinn.v5i2.134

Factors Affecting Students' Acceptance of e-Learning System in Higher Education

Authors

Nurhafizah Ahmad Universiti Teknologi MARA Pulau Pinang
Norazah Umar Universiti Teknologi MARA Pulau Pinang
Rozita Kadar Universiti Teknologi MARA Pulau Pinang
Jamal Othman Universiti Teknologi MARA Pulau Pinang

DOI:

https://doi.org/10.24191/jcrinn.v5i2.134

Keywords:

e-learning, LMS, TAM, MOOC, e-learning, LMS, TAM, MOOC

Abstract

e-Learning has become the most important supporting tool offering independent learning style among students. The main idea of this paper is to dismantle and analyse factors that influence the acceptance of e-Learning among students in higher education. An online questionnaire link was distributed to a sample comprising 123 respondents. Significant relationships and strength of relationship were observed between the e-Learning acceptance, quality, e-Learning self-efficacy, enjoyment, accessibility, and computer playfulness. The findings showed that all factors were positively correlated to the e-Learning system except the enjoyment of e-learning that did not affect the acceptance of e-learning. Conclusively, all factors stated were considered the main criteria in designing effective e-learning system. Future works such as embedding and integrating multimedia elements in the e-learning system will be additional attraction to learners and instructors for the effective learning style.

Downloads

Download data is not yet available.

References

Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391.

Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. 44, 263–275. https://doi.org/10.1016/j.im.2006.12.008

Al-Rahmi, W. M., Alias, N., Othman, M. S., Alzahrani, A. I., Alfarraj, O., Saged, A. A., & Rahman, N. S. A. (2018). Use of e-learning by University students in Malaysian higher educational institutions: A case in Universiti Teknologi Malaysia. IEEE Access, 6, 14268–14276.

Al-rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015). The Effectiveness of Using E-Learning in Malaysian Higher Education : A Case Study Universiti Teknologi Malaysia. (November). https://doi.org/10.5901/mjss.2015.v6n5s2p625

Andana, D., & Elvina, H. (2015). Analysis of Factors Affecting User Acceptance of the Implementation of ClassCraft E- Learning : Case Studies Faculty of Information Technology of Tarumanagara University. 73–78.

Ani, O. E., & Ahiauzu, B. (2008). Towards effective development of electronic information resources in Nigerian university libraries. Library Management.

Attis, J. (2014). An investigation of the variables that predict teacher e-learning acceptance.

Bates, T. (1997). Restructuring the university for technological change. Murdoch University.

Bolarinwa, O. A. (2015). Principles and methods of validity and reliability testing of questionnaires used in social and health science researches. Nigerian Postgraduate Medical Journal, 22(4), 195.

Chang, S., & Tung, F. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83.

Fianu, E., Blewett, C., Ampong, G., & Ofori, K. (2018). Factors Affecting MOOC Usage by Students in Selected Ghanaian Universities. Education Sciences, 8(2), 70.

Field, A. P. (2005). Discovering statistics using SPSS:(and sex, drugs and rock’n’roll). London.

George, D., & Mallery, M. (2003). Using SPSS for Windows step by step: a simple guide and reference.

Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the Technology Acceptance Model for Use of e- Learning Systems by Digital Learners. IEEE Access, PP(c), 1. https://doi.org/10.1109/ACCESS.2018.2881384

Harandi, S. R. (2015). Effects of e-learning on Students’ Motivation. Procedia-Social and Behavioral Sciences, 181, 423–430.

Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences (Vol. 663). Houghton Mifflin College Division.

Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367.

Indahyanti, U. (2015). Jurnal Teknologi Full paper Applying the Technology Acceptance Model to Measure the Learning Management System Acceptance by Students of Politeknik Sakti Surabaya. 4, 127–131.

Keller, J., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239.

Krishnan, K. S. T., & Hussin, H. (2017). E-learning readiness on Bumiputera SME’s intention for adoption of online entrepreneurship training in Malaysia. Management, 7(1), 35–39.

Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.

Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35, 157–170.

Richardson, J. C., & Newby, T. (2006). The role of students’ cognitive engagement in online learning. American Journal of Distance Education, 20(1), 23–37.

Rovai, A., Ponton, M., Wighting, M., & Baker, J. (2007). A comparative analysis of student motivation in traditional classroom and e-learning courses. International Journal on E-Learning, 6(3), 413–432.

Salloum, S. A. (2019). Factors Affecting Students ’ Acceptance of E-Learning System in Higher Education Using UTAUT and Structural Equation Modeling Approaches (Vol. 2). https://doi.org/10.1007/978-3-319-99010-1

Salloum, S. A. S., & Shaalan, K. (2018). Investigating students’ acceptance of E-learning system in Higher Educational Environments in the UAE: Applying the Extended Technology Acceptance Model (TAM). The British University in Dubai.

Sánchez-Franco, M. J., Martínez-López, F. J., & Martín-Velicia, F. A. (2009). Exploring the impact of individualism and uncertainty avoidance in Web-based electronic learning: An empirical analysis in European higher education. Computers & Education, 52(3), 588–598.

Sangrà, A., Vlachopoulos, D., & Cabrera, N. (2012). Building an inclusive definition of e-learning: An approach to the conceptual framework. The International Review of Research in Open and Distributed Learning, 13(2), 145–159.

Sharma, S. K., Gaur, A., & Saddikuti, V. (2017). Structural equation model ( SEM ) -neural network ( NN ) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 0(0), 1–14. https://doi.org/10.1080/0144929X.2017.1340973

Thejeswar, E. P., & Thenmozhi, M. S. (2015). Educational research-iPad system vs textbook system. Research Journal of Pharmacy and Technology, 8