Factors Affecting Students’ Intention to Use Massive Open Online Courses


  • An Minh Ngoc Pham Can Tho FPT University, Vietnam
  • Thao Lai Phuong Pham Can Tho FPT University, Vietnam
  • Gam Hong Huynh Can Tho FPT University, Vietnam
  • Thu Hoang Minh Vo Can Tho FPT University, Vietnam
  • Anh Ngoc Kim Nguyen Can Tho FPT University, Vietnam
  • Tri Minh Tran Can Tho FPT University, Vietnam



How to Cite

Pham, A. M. N., Pham, T. L. P., Huynh, G. H., Vo, T. H. M., Nguyen, A. N. K., & Tran, T. M. (2021). Factors Affecting Students’ Intention to Use Massive Open Online Courses . Vietnam Journal of Education, 5(3), 63–71. https://doi.org/10.52296/vje.2021.117


Massive Open Online Courses (MOOCs) attract many researchers because of their massiveness, openness, machine and peer assessment, yet there are still many questions to be answered. This study was conducted at FPT University in Can Tho during the 2020-2021 academic year using the quantitative approach. A purposeful sampling technique was used to select 226 participants who partook at least one MOOC on the Coursera platform. The questionnaire consists of 18 items adapted from Technology Acceptance Model (TAM) developed by Davis (1989), and Learning Strategies, by Marton and Säljö (1976). The findings showed that perceived ease of use (PEOU), and perceived usefulness (PU) have a great impact on students’ intention to use MOOCs in the future, PU, however, has a stronger and more direct correlation to the acceptability of MOOCs. Furthermore, surface learning strategy has a negative effect on the intention to enroll in MOOCs while deep learning strategy was not significantly correlated with intended future use of MOOCs. More importantly, a valuable finding was that surface learning strategy was in inverse proportion to courses variable and it can be lessened. Our findings are expected to offer a multi-dimensional view for students, especially those in the current context as well as MOOCs developers in order to design curricula.


Download data is not yet available.


Aharony, N., & Bar-Ilan, J. (2016). Students’ perceptions on MOOCs: An exploratory study. Interdisciplinary Journal of E-Learning and Learning Objects, 12(1), 145-162. Informing Science Institute. https://www.learntechlib.org/p/180848/

Aharony, N. (2009). The influence of LIS students’ personality characteristics on their perceptions towards Web 2.0 use. Journal of Librarianship and Information Science, 41(4), 227-241. https://doi.org/10.1177/0961000609345088

Aharony, N. (2014a). Factors affecting the adoption of e-books by information professionals. Journal of Librarianship and Information Science, 47(2), 131-144. https://doi.org/10.1177/0961000614532120

Aharony, N. (2014b). Library and information science students’ perceptions of m-learning. Journal of Librarianship and Information Science, 46(1), 48-61. https://doi.org/10.1177/0961000613518819

Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2019). Factors affecting student dropout in MOOCs: a cause and effect decision‐making model. Journal of Computing in Higher Education, 32(2), 429-454. https://doi.org/10.1007/s12528-019-09241-y

Al-Gahtani, S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001

Bates, T. (2014). MOOCs: Getting to know you better. Distance Education, 35(2), 145-148. https://doi.org/10.1080/01587919.2014.926803

Bayne, S., & Ross, J. (2014). MOOC pedagogy. Massive Open Online Courses: The MOOC Revolution, 11(2), 23-26. https://doi.org/10.5070/d4112027189

Belanger, Y., & Thornton, J. (2013). Bioelectricity: A quantitative approach. Duke University’s first MOOC. http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/6216/Duke_Bioelectricity_MOOC_Fall2012.pdf

Chen, Y. (2014). Investigating MOOCs through blog mining. International Review of Research in Open and Distance Learning, 15(2), 85-106. https://doi.org/10.19173/irrodl.v15i2.1695

Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user. acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

Dyer, R. A. D. (2014). Exploring the relevancy of massive open online courses (MOOCs): A Caribbean university approach. Information Resources Management Journal, 27(2), 61-77. https://doi.org/10.4018/irmj.2014040105

Glance, D., Forsey, M., & Riley, M. (2012). The pedagogical foundations of massive open online courses. First Monday Journal, 18(5). https://doi.org/10.5210/fm.v18i5.4350

Hoi, V. N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761. https://doi.org/10.1016/j.compedu.2019.103761

Jordan, K. (2013). MOOC completion rates: The data. http://www.katyjordan.com/MOOCproject.html

Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1), 134-160. https://doi.org/10.19173/irrodl.v15i1.1651

Kesim, M., & Altınpulluk, H. (2015). A theoretical analysis of MOOCs types from a perspective of learning theories. Procedia - Social and Behavioral Sciences, 186, 15-19. https://doi.org/10.1016/j.sbspro.2015.04.056

Khawater, F. A. A. (2021). The effects of online learning on students’ positive and negative online learning outcomes. International Journal of Research in Education Humanities and Commerce, 2(2), 23-26. https://www.ijrehc.com/doc/ijrehc02_08.pdf

Markoff, J. (2013). Essay-grading software offers professors a break. The New York Times. https://www.nytimes.com/2013/04/05/science/new-test-for-computers-grading-essays-at-college-level.html

Marton, F., & Säljö, R. (1976). On qualitative differences in learning: I-outcome and process. British Journal of Educational Psychology, 46(1), 4-11. https://doi.org/10.1111/j.2044-8279.1976.tb02980.x

Meyer, R. (2012). What it’s like to teach a MOOC (and what the heck’s a MOOC?). https://www.theatlantic.com/technology/archive/2012/07/what-its-like-to-teach-a-mooc-and-what-the-hecks-a-mooc/260000/

Nguyen, T. (2015). The effectiveness of online learning; Beyond no significant difference and future horizons. MERLOT Journal of Online Learning and Teaching, 11(2), 309-319. http://jolt.merlot.org/Vol11no2/Nguyen_0615.pdf

Pallant, J. (2007). SPSS Survival manual: A Step by Step Guide to data analysis using SPSS. Allen & Unwin, Sabon by Bookhouse, Sydney.

Ross, J., Sinclair, C., Knox, J., Bayne, S., & Macleod, H. (2014). Teacher experiences and academic identity: The missing components of MOOC pedagogy. MERLOT Journal of Online Learning and Teaching, 10(1), 56-68. https://doi.org/10.1007/s42438-020-00203-7

Sang, C., & Tai, D. (2017). Online education: Which model will succeed? https://nhipcaudautu.vn/biz-tech/giao-duc-truc-tuyen-mo-hinhnao-se-thanh-cong-3318137/

Sattari, A., Abdekhoda, M., & Gavgani, V. Z. (2017). Determinant factors affecting the web based training acceptance by health students, applying the UTAUT model. International Journal of Emerging Technologies in Learning (iJET), 12(10), 112-126. https://doi.org/10.3991/ijet.v12i10.7258

Siemens, G. (2013). Massive open online courses: Innovation in education? In R. McGreal, et al. (Eds). Open Educational Resources: Innovation, Research and Practice, 1, 5-15. https://doi.org/10.7551/mitpress/9202.003.0005

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144

Thompson, B. (2000). Ten Commandments of Structural Equation Modeling. In L. Grimm, & P. Yarnold (Eds.), Reading and Understanding More Multivariate Statistics, 261-284. Washington DC: American Psychological Association.

University of Pennsylvania Graduate School of Education, Press Room (2013). Penn GSE study shows MOOCs have relatively few active users, with only a few persisting to course end. https://www.gse.upenn.edu/pressroom/press-releases/2013/12/penn-gse-study-shows-MOOCs-have-relatively-few-active-users-only-few-persisti

Vululleh, P. (2018). Determinants of students’ e-learning acceptance in developing countries: An approach based on Structural Equation Modeling (SEM). International Journal of Education and Development using ICT, 14(1). Open Campus, The University of the West Indies, West Indies. https://www.learntechlib.org/p/183560/

Warburton, K. (2003). Deep learning and education for sustainability. International Journal of Sustainability in Higher Education, 4(1), 44-56. https://doi.org/10.1108/14676370310455332

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028

Yang, Y. F., & Tsai, C. C. (2010). Conceptions of and approaches to learning through online peer assessment. Learning and Instruction, 20(1), 72-83. doi:10.1016/j.learninstruc.2009.01.003




How to Cite

Pham, A. M. N., Pham, T. L. P., Huynh, G. H., Vo, T. H. M., Nguyen, A. N. K., & Tran, T. M. (2021). Factors Affecting Students’ Intention to Use Massive Open Online Courses . Vietnam Journal of Education, 5(3), 63–71. https://doi.org/10.52296/vje.2021.117