PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

 

e-ISSN 2231-8534
ISSN 0128-7702

Home / Regular Issue / JSSH Vol. 30 (4) Dec. 2022 / JSSH-8400-2021

 

Predictors of Massive Open Online Courses (MOOC) Learning Satisfaction: A Recipe for Success

Gan Tzyy Yang, Farrah Dina Yusop and Chin Hai Leng

Pertanika Journal of Social Science and Humanities, Volume 30, Issue 4, December 2022

DOI: https://doi.org/10.47836/pjssh.30.4.17

Keywords: Higher education, interactivity, Massive Open Online Courses (MOOC), online learner, online learning, student learning satisfaction, technology system, virtual learning effectiveness

Published on: 15 December 2022

Massive Open Online Courses (MOOCs) have recently gained great attention. However, the biggest challenge to the success of MOOCs is their low completion rate. During the lockdown of the COVID-19 pandemic, MOOCs were in high demand by many higher education institutions to replace their face-to-face lessons. MOOCs have great potential to grow and reinvent the way of learning in the 21st century. This study uses the Virtual Learning Environment (VLE) effectiveness model to understand how the five key factors (learner, instructor, course, technology system, and interactivity) influence student learning satisfaction from a holistic approach and determine the best predictor of student learning satisfaction in the MOOC learning environment. A set of online data based on a 5-point Likert scale was collected from 333 undergraduate students from the top five public universities in Malaysia whose students are actively using MOOCs in their learning. The Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was used to analyse the data. The empirical results revealed that all factors significantly influence student learning satisfaction positively. Learner and interactivity factors were the strongest predictors in determining student learning satisfaction in MOOCs. These findings provide an empirically justified framework for developing successful online courses such as MOOCs in higher education.

  • Abdel-Jaber, H. (2017). Experimental analysis of students’ satisfaction factors in e-learning environment: A case study on Saudi Arabian university. Journal of Information & Knowledge Management, 16(2), 1750018. https://doi.org/10.1142/s0219649217500186

  • Albelbisi, N. A., Al-Adwan, A. S., & Habibi, A. (2021). Self-regulated learning and satisfaction: A key determinants of MOOC success. Education and Information Technologies, 26(3), 3459-3481. https://doi.org/10.1007/s10639-020-10404-z

  • Albelbisi, N. A., & Yusop, F. D. (2019). Factors influencing learners’ self-regulated learning skills in a Massive Open Online Course (MOOC) environment. Turkish Online Journal of Distance Education, 20(3), 1-16. https://doi.org/10.17718/tojde.598191

  • Albelbisi, N. A., & Yusop, F. D. (2020). SWOT analysis on the implementation of MOOC in Malaysia. In F. D.Yusop, R. Kamalludeen, Z. F. A. Hassan, & M. S. Nordin (Eds), MOOCs in Malaysia: Towards globalised online learning (pp. 27-40). UPNM Press.

  • Albelbisi, N. A., Yusop, F. D., & Salleh, U. K. M. (2018). Mapping the factors influencing success of Massive Open Online Courses (MOOC) in higher education. Eurasia Journal of Mathematics, Science and Technology Education, 14(7), 2995-3012. https://doi.org/10.29333/ejmste/91486

  • Alkhateeb, M. A., & Abdalla, R. A. (2021). Factors influencing student satisfaction towards using learning management system Moodle. International Journal of Information and Communication Technology Education, 17(1), 138-153. http://doi.org/10.4018/IJICTE.2021010109

  • Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenge and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280. https://doi.org/10.1007/s10639-020-10219-y

  • Alqurashi, F. (2018). Learning strategies in L2 settings in Saudi Arabia: An annotated bibliography. International Journal of Applied Linguistics & English Literature, 7(7), 17-26. https://doi.org/10.7575/aiac.ijalel.v.7n.7p.17

  • Alzahrani, L., & Seth, K. P. (2021). Factors influencing students’ satisfaction with continuous use of learning management systems during the COVID-19 pandemic: An empirical study. Education and Information Technologies, 26(6), 1-19. https://doi.org/10.1007/s10639-021-10492-5

  • Asoodar, M., Vaezi, S., & Izanloo, B. (2016). Framework to improve e-learner satisfaction and further strengthen e-learning implementation. Computers in Human Behavior, 63, 704-716. https://doi.org/10.1016/j.chb.2016.05.060

  • Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8-34. https://doi.org/10.1007/s11747-011-0278-x

  • Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in Massive Open Online Courses: Examining aspects of language and social engagement. Computers & Education, 94, 49-60. https://doi.org/10.1016/j.compedu.2015.11.010

  • Bates, T. (2014). MOOCs: Getting to know you better. Distance Education, 35(2), 145-148.

  • Bryant, M. G. (2017). The development of the Massive Open Online Course Virtual Learning Environment Scale (MVLE) and model to measure satisfaction of MOOC online learning courses in higher education: A mixed methods study [Doctoral dissertation]. University of Louisiana at Lafayette.

  • Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105.

  • Chen, C. C., Lee, C. H., & Hsiao, K. L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan. Library Hi Tech, 36(4), 705-719. https://doi.org/10.1108/LHT-11-2016-0129

  • Chen, Y., Gao, Q., Yuan, Q., & Tang, Y. (2020). Discovering MOOC learner motivation and its moderating role. Behaviour & Information Technology, 39(12), 1257-1275. https://doi.org/10.1080/0144929X.2019.1661520

  • Chin, W. W., Marcolin, B., & Newsted, P. (2003). A partial least square latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217. https://doi.org/10.1287/isre.14.2.189.16018

  • Chung, E., Subramaniam, G., & Dass, L. C. (2020). Online learning readiness among university students in Malaysia amidst COVID-19. Asian Journal of University Education, 16(2), 46-58. https://doi.org/10.24191/ajue.v16i2.10294

  • Cidral, W. A., Oliveira, T., Felice, M. D., & Aparicio, M. (2018). E-learning success determinant: Brazilian empirical study. Computers & Education, 122, 273-290. https://doi.org/10.1016/j.compedu.2017.12.001

  • Daneji, A. A., Ayub, A. F., & Khambari, M. N. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using Massive Open Online Course (MOOC). Knowledge Management & E-Learning, 11(2), 201-214.

  • Das, K., & Das, P. (2020). Online teaching-learning in higher education during lockdown period of COVID-19 pandemic in India. International Journal on Orange Technologies, 2(6), 5-10.

  • Dehghani, S., Sheikhi, F. A., Zeinalipour, H., & Rezaei, E. (2020). The competencies expected of instructors in massive open online courses (MOOCs). Interdisciplinary Journal of Virtual Learning in Medical Sciences, 11(2), 69-83. https://dx.doi.org/10.30476/ijvlms.2020.86482.1036

  • Dubosson, M., & Emad, S. (2015). The forum community, the connectivist element of an xMOOC. Universal Journal of Educational Research, 3(10), 680-690.

  • Eom, S. B., & Ashill, N. (2016). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An update. Decision Sciences Journal of Innovative Education, 14(2), 185-215. https://doi.org/10.1111/dsji.12097

  • Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Science Journal of Innovative Education, 4(2), 215-235. https://doi.org/10.1111/j.1540-4609.2006.00114.x

  • Fawaz, M., & Samaha, A. (2020). E-learning: Depression, anxiety, and stress symptomatology among Lebanese university students during COVID-19 quarantine. Nursing Forum, 56(1), 52-57. https://doi.org/10.1111/nuf.12521

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

  • Gameel, B. G. (2017). Learner satisfaction with Massive Open Online Courses. American Journal of Distance Education, 31(2), 98-111. https://doi.org/10.1080/08923647.2017.1300462

  • Goh, C., Leong, C., Kasmin, K., Hii, P., & Tan, O. (2017). Students’ experiences, learning outcomes and satisfaction in e-Learning. Journal of e-Learning and Knowledge Society, 13(2), 117-128.

  • Gomez-Zermeno, M. G., & de La Garza, L. A. (2016). Research analysis on MOOC course dropout and retention rates. Turkish Online Journal of Distance Education, 17(2), 3-14. https://doi.org/10.17718/tojde.23429

  • Guichon, N. (2010). Preparatory study for the design of a desktop videoconferencing platform for synchronous language teaching. Computer Assisted Language Learning, 23(2), 169-182. https://doi.org/10.1080/09588221003666255

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE.

  • Hartnett, M. (2016). Motivation in online education. Springer.

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-320. https://doi.org/10.1108/S1474-7979(2009)0000020014

  • Hew, K. F. (2018). Unpacking the strategies of ten highly rated MOOCs: Implications for engaging students in large online courses. Teachers College Record, 120(1), 1-40. https://doi.org/10.1177/016146811812000107

  • Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724.

  • Hulland, J. (1999). Use of Partial Least Squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1016/j.compedu.2019.103724

  • Jansen, D., Rosewell, J., & Kear, K. (2016). Quality frameworks for MOOCs. In M. Jemni, Kinshuk & M. K. Khribi (Eds.), Open education: From OERs to MOOCs. Springer.

  • Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., Dawson, S., Graesser, A. C., & Brooks, C. (2018). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43-86. https://doi.org/10.3102/0034654317740335

  • Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272. https://doi.org/10.1016/j.compedu.2018.01.003

  • Knox, J. (2018). Beyond the “c” and the “x”: Learning with algorithms in massive open online courses (MOOCs). International Review of Education, 64, 161-178. https://doi.org/10.1007/s11159-018-9707-0

  • Kuo, Y. C., & Belland, B. R. (2016). An exploratory study of adult learners’ perceptions of online learning: Minority students in continuing education. Educational Technology Research and Development, 64(4), 661-680. https://doi.org/10.1007/s11423-016-9442-9

  • Kuo, Y. C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001

  • Leary, M. R. (2014). Introduction to behavioral research methods (6th ed.). Pearson.

  • Leidner, D. E., & Jarvenpaa, S. L. (1995). The use of information technology to enhance management school education: A theoretical view. MIS Quarterly, 19(3), 265-291. https://doi.org/10.2307/249596

  • Lepper, M. R., & Malone, T. W. (1987). Intrinsic motivation and instructional effectiveness in computer-based education. In R. E. Snow & M. J. Farr (Eds.), Aptitude, learning and instruction (pp. 255-286). London: Routledge. https://doi.org/10.4324/9781003163244

  • Li, Y., Yang, H. H., Cai, J., & MacLeod, J. (2017). College students’ computer self-efficacy, intrinsic motivation, attitude, and satisfaction in blended learning environments. In S. Cheung, L. Kwok, W. Ma, L. K. Lee, & H. Yang (Eds.), 10th International Conference on Blended Learning (pp. 65-73). Springer.

  • Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105-117.

  • McLoughlin, C., & Lee, M. J. (2010). Personalised and self-regulated learning in the Web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 28-43. https://doi.org/10.14742/ajet.1100

  • Moore, M., & Kearsley, G. (1996). Distance education: A systems view. Wadsworth.

  • Murphy, E., & Rodriguez-Manzanares, M. A. (2009). Teachers’ perspectives on motivation in high-school distance education. International Journal of E-Learning & Distance Education, 23(3), 1-24.

  • Myrtveit, I., & Stensrud, E. (2012). Validity and reliability of evaluation procedures in comparative studies of effort prediction models. Empirical Software Engineering, 17(1-2), 23-33. https://doi.org/10.1007/s10664-011-9183-7

  • Naveed, Q. N., Muhammad, A., Sattam, P., & Abdulaziz, B. (2017). A mixed method study for investigating critical factors (CSFs) of e-learning in Saudi Arabian universities. International Journal of Advanced Computer Science and Application, 8(5), 171-178.

  • Ozkan, S., & Koseler, R. (2009). Multidimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296. https://doi.org/10.1016/j.compedu.2009.06.011

  • Paul, N., & Glassman, M. (2017). Relationship between internet self-efficacy and internet anxiety: A nuanced approach to understanding the connection. Australasian Journal of Educational Technology, 33(4), 147-165. https://doi.org/10.14742/ajet.2791

  • Pica, T., Young, R. F., & Doughty, C. (1987). The impact of interaction on comprehension. TESOL Quarterly, 21(4), 737-758. https://doi.org/10.2307/3586992

  • Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401-426. https://doi.org/10.2307/3250989

  • Pozón-López, I., Kalinic, Z., Higueras-Castillo, E., & Liébana-Cabanillas, F. (2019). A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC). Interactive Learning Environments, 28(8), 1003-1021. https://doi.org/10.1080/10494820.2019.1636074

  • Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26, 2623-2656. https://doi.org/10.1007/s10639-020-10375-1

  • Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2020). Online university teaching during and after the COVID-19 crisis: Refocusing teaching presence and learning activity. Postdigital Science and Education, 2, 923-945. https://doi.org/10.1007/s42438-020-00155-y

  • Renninger, K. A. (2000). Individual interest and its implications for understanding intrinsic motivation. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic and extrinsic motivation: The search for optimal motivation and performance (pp. 373-404). Academic Press.

  • Rodriguez, C. O. (2012). MOOCs and the AI-Stanford like courses: Two successful and distinct course formats for massive open online courses. European Journal of Open, Distance and E-learning, 67-73.

  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://psycnet.apa.org/doi/10.1037/0003-066X.55.1.68

  • Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modelling (2nd ed.). Erlbaum.

  • Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413. https://doi.org/10.1016/j.compedu.2005.09.004

  • Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers and Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007

  • Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967. https://doi.org/10.1016/j.promfg.2018.03.137

  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. https://doi.org/10.1016/j.csda.2004.03.005

  • Venkatesh, S., Rao, Y. K., Nagaraja, H., Woolley, T., Alele, F. O., & Malau-Aduli, B. S. (2020). Factors influencing medical students: Experiences and satisfaction with blended integrated e-learning. Medical Principles and Practice, 29(4), 396-402. https://doi.org/10.1159/000505210

  • Weng, C., Tsai, C. C., & Weng, A. (2015). Social support as a neglected e-learning motivator affecting trainee’s decisions of continuous intentions of usage. Australasian Journal of Educational Technology, 31(2), 177-192. https://doi.org/10.14742/ajet.1311

  • 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, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65(5), 1195-1214. https://doi.org/10.1007/s11423-017-9513-6

  • Yusop, F. D., Abu Hassan, Z. F., Hamzaid, N. A., Firdaus, A., Danaee, M., Chen, Y. M., Kahmis, M. H. K., Hassim, N., Abdul Ghaffar, F., & Sulaiman, A. H. (2020). Preparing academics for open access education: UM’s journey from Moodle to MOOCs. In F. D. Yusop, R. Kamalludeen, Z. F. Abu Hassan, & M. S. Nordin (Eds.), MOOCs in Malaysia: Towards globalised online learning (pp. 41-48). UPNM Press.

  • Yousef, A. M. F., Chatti, M. A., Schroeder, U., & Wosnitza, M. (2014). What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. In IEEE 14th International Conference on Advanced Learning Technologies (pp. 44-48). IEEE.

  • Zhang, Y., & Lin, C. H. (2020). Student interaction and the role of the teacher in a state virtual high school: What predicts online learning satisfaction? Technology, Pedagogy and Education, 29(1), 1-15. https://doi.org/10.1080/1475939X.2019.1694061

  • Zhao, H. (2016). Factors influencing self-regulation in e-learning 2.0: Confirmatory factor model. Canadian Journal of Learning and Technology, 42(2), 2-22. https://doi.org/10.21432/T2C33K

  • Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://psycnet.apa.org/doi/10.1037/0022-0663.81.3.329

ISSN 0128-7702

e-ISSN 2231-8534

Article ID

JSSH-8400-2021

Download Full Article PDF

Share this article

Related Articles