Factors Influencing Students’ Acceptance and Satisfaction with LMS in Blended Learning Environment
Main Article Content
Ai-Ling Aileen Koh*
Ying-Leh Ling
Blended learning (BL) has become an essential approach in Malaysian Technical and Vocational Education and Training (TVET), with Learning Management Systems (LMS) serving as the core platform for delivering digital instruction and learner support. However, students’ acceptance and satisfaction with LMS remain uneven, particularly in competency-based environments where learners depend heavily on practical guidance and technological support. This study examined the extent to which technological experience and knowledge sharing influence students perceived ease of use (PEOU), perceived usefulness (PU), and overall satisfaction with LMS in a blended learning environment at a Malaysian polytechnic. Using a quantitative research design, data were collected from 290 students who had used the LMS for at least one semester. Multiple regression analyses revealed that both technological experience and knowledge sharing significantly predicted PEOU, PU, and satisfaction. Technological experience emerged as the strongest contributor across all models, suggesting that students who are more technologically competent perceive the LMS as easier to use, more beneficial, and more satisfying. Knowledge sharing also played a meaningful role by enhancing students’ confidence, engagement, and perceived learning value. These findings highlight the importance of strengthening digital competencies and fostering collaborative learning practices to support effective LMS adoption. The study provides practical implications for educators and administrators seeking to optimize LMS integration in TVET-based blended learning environments.
Ahmad, N. A., Elias, N. F., Sahari, N., & Mohamed, H. (2023). Learning Management System Acceptance Factors for Technical and Vocational Education Training (TVET) Institutions. TEM Journal, 12(2), 1156–1165. https://doi.org/10.18421/TEM122-61
Al-Azawei, A., Parslow, P., & Lundqvist, K. (2016). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2), 1–23. https://doi.org/10.14742/ajet.2741
Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating Technology Acceptance Model With Innovation Diffusion Theory: An Empirical Investigation on Students’ Intention to Use E-Learning Systems. IEEE Access, 7, 26797–26809. https://doi.org/10.1109/ACCESS.2019.2899368
Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155. https://doi.org/10.14569/IJACSA.2014.050120
Annamalai, N., & Kumar, J. A. (2020). Understanding Smartphone use Behavior among Distance Education Students in Completing their Coursework in English: A Mixed-Method Approach. The Reference Librarian, 61(3–4), 199–215. https://doi.org/10.1080/02763877.2020.1815630
Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior, 66, 388–399. https://doi.org/10.1016/j.chb.2016.10.009
Balakrishnan, V., & Gan, C. L. (2016). Students’ learning styles and their effects on the use of social media technology for learning. Telematics and Informatics, 33(3), 808–821. https://doi.org/10.1016/j.tele.2015.12.004
Cheng, G., & Chau, J. (2016). Exploring the relationships between learning styles, online participation, learning achievement and course satisfaction: An empirical study of a blended learning course. British Journal of Educational Technology, 47(2), 257–278. https://doi.org/10.1111/bjet.12243
Cohen, A., & Baruth, O. (2017). Personality, learning, and satisfaction in fully online academic courses. Computers in Human Behavior, 72, 1–12. https://doi.org/10.1016/j.chb.2017.02.030
Cohen, A., & Nachmias, R. (2006). A quantitative cost effectiveness model for Web-supported academic instruction. The Internet and Higher Education, 9(2), 81–90. https://doi.org/10.1016/j.iheduc.2006.03.007
Delone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001
Ghadirian, H., Fauzi Mohd Ayub, A., Daud Silong, A., Binti Abu Bakar, K., & Mohammad Hossein Zadeh, A. (2014). Knowledge Sharing Behaviour among Students in Learning Environments: A Review of Literature. Asian Social Science, 10(4), 38. https://doi.org/10.5539/ass.v10n4p38
Ghazal, S., Aldowah, H., & Umar, I. (2018). Critical Factors to Learning Management System Acceptance and Satisfaction in a Blended Learning Environment. In Recent Trends in Information and Communication Technology (pp. 688–698). Springer. https://doi.org/10.1007/978-3-319-59427-9_71
Goodfellow, R., & Lea, M. R. (2013). Literacy in the digital university. Taylor & Francis London, UK.
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005
Hilmi, M. F., & Mustapha, Y. (2022). Perceived Security of E-Learning Portal. http://arxiv.org/abs/2209.11196
Liao, L.-F. (2006). The impact of teacher’s powers to knowledge sharing behavior and learning satisfaction in distance-learning environment. Journal of Information, Technology and Society, 2(3), 1–14.
Luckin, R., Bligh, B., Manches, A., Ainsworth, S., Crook, C., & Noss, R. (2012). Decoding Learning: The Proof Promise and Potential of Digital Education. NESTA.
Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604–612. https://doi.org/10.1016/j.chb.2016.05.084
Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. https://doi.org/10.1080/10494820.2015.1122635








