48
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Effects of personal and instructor goals on MOOC continuance intention

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Received 22 Jan 2024, Accepted 12 Mar 2024, Published online: 13 Jun 2024

References

  • 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
  • Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562
  • Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28–38. https://doi.org/10.1016/j.compedu.2014.08.006
  • Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), Article 103168. https://doi.org/10.1016/j.im.2019.05.003
  • Cheng, Y. (2020). Students’ satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education & Training, 62(9), 1037–1059. https://doi.org/10.1108/ET-10-2019-0245
  • Cho, M.-H., Yang, T., Niu, Z. & Kim, J. (2022). Investigating what learners value in marketing MOOCs: A content analysis. Journal of Computing in Higher Education. Advance online publication.] = https://doi.org/10.1007/s12528-022-09347-w
  • Conde Gafaro, B. (2022). First steps towards self-regulated learning: Setting goals in MOOCs. In B. Rienties, R. Hampel, S. Eileen & W. Denise (Eds.), Open world learning: Research, innovation and the challenges of high-quality education (pp. 63–75). Routledge. https://doi.org/10.4324/9781003177098-6
  • Dai, H. M., Teo, T., & Rappa, N. A. (2020). Understanding continuance intention among MOOC participants: The role of habit and MOOC performance. Computers in Human Behavior, 112, Article 106455. https://doi.org/10.1016/j.chb.2020.106455
  • Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers and Education, 150, Article 103850. https://doi.org/10.1016/j.compedu.2020.103850
  • Dinh, N. B. K., Zhu, C., Nguyet, D. A., & Qi, Z. (2023). Uncovering factors predicting the effectiveness of MOOC-based academic leadership training. Journal of Computers in Education, 10, 721–747. https://doi.org/10.1007/s40692-022-00241-z
  • 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/10.2307/3151312
  • Gu, W., Xu, Y., & Sun, Z. (2021). Does MOOC quality affect users’ continuance intention? Based on an integrated model. Sustainability, 13(22), Article 12536. https://doi.org/10.3390/su132212536
  • Hair, J. F., Jr., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
  • Henderikx, M. A., Kreijns, K., & Kalz, M. (2017). Refining success and dropout in massive open online courses based on the intention–behavior gap. Distance Education, 38(3), 353–368, https://doi.org/10.1080/01587919.2017.1369006
  • Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45–58. https://doi.org/10.1016/j.edurev.2014.05.001
  • Huang, L., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), 84–91. https://doi.org/10.1016/j.ijinfomgt.2016.12.002
  • 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
  • Kala, D., & Cahubey, D. S. (2023). Examination of relationships among technology acceptance, student engagement, and perceived learning in tourism-related MOOCs. Journal of Teaching in Travel & Tourism, 23(1), 39–56. https://doi.org/10.1080/15313220.2022.2038342
  • Kim, R., & Song, H. D. (2022). Examining the influence of teaching presence and task-technology fit on continuance intention to use MOOCs. Asia-Pacific Education Researcher, 31, 395–408. https://doi.org/10.1007/s40299-021-00581-x
  • Kim, W., Watson, S. L., & Watson, W. W. (2016). Perceived learning in three MOOCs targeting attitudinal change. Educational Media International, 53(3),168–183. https://doi.org/10.1080/09523987.2016.1236890
  • Li, K. (2019). MOOC learners’ demographics, self-regulated learning strategy, perceived learning and satisfaction: A structural equation modeling approach. Computers and Education, 132, 16–30. https://doi.org/10.1016/j.compedu.2019.01.003
  • Li, K., Johnsen, J., & Canelas, D. A. (2021). Persistence, performance, and goal setting in massive open online courses. British Journal of Educational Technology, 52(3), 1215–1229. https://doi.org/10.1111/bjet.13068
  • Moore, R. L., & Blackmon, S. J. (2022). From the learner’s perspective: A systematic review of MOOC learner experiences (2008–2021). Computers & Education, 190(C), Article 104596. https://doi.org/10.1016/j.compedu.2022.104596
  • Oh, E. G., Chang, Y., & Park, S.W. (2020). Design review of MOOCs: Application of e-learning design principles. Journal of Computing in Higher Education, 32, 455–475. https://doi.org/10.1007/s12528-019-09243-w
  • Oh, E. G., Cho, M.-H., & Chang, Y. (2023). Learners’ perspectives on MOOC design. Distance Education, 23(3), 476–494. https://doi.org/10.1080/01587919.2022.2150126
  • Reich, J., & Ruiperez-Valiente, J. A. (2019). The MOOC pivot. Science, 363(6423), 130–131. https://doi.org/10.1126/science.aav7958
  • Rekha, I. S., Shetty, J., & Basri, S. (2023). Students’ continuance intention to use MOOCs: Empirical evidence from India. Education and Information Technologies, 28, 4265–4286. https://doi.org/10.1007/s10639-022-11308-w
  • Rohloff, T., Sauer, D., & Meinel, C. (2020, August). Students’ achievement of personalized learning objectives in MOOCs. In Proceedings of the Seventh ACM Conference on Learning @ Scale conference (pp. 147–156). ACM. Virtual Event. https://doi.org/10.1145/3386527.3405918
  • Romero-Rodríguez, L. M., Ramírez-Montoya, M. S., & Valenzuela González, J. R. (2020). Correlation analysis between expectancy-value and achievement goals in MOOCs on energy sustainability: Profiles with higher engagement. Interactive Technology and Smart Education, 17(4), 417–434. https://doi.org/10.1108/ITSE-01-2020-0017
  • Shah, D. (2021, December 14). A decade of MOOCs: A review of MOOC stats and trends in 2021. The Report. https://www.classcentral.com/report/moocs-stats-and-trends-2021/
  • Shah, D., Pickard, L., & Ma, R. (2023, April 10). Massive list of MOOC platforms around the world in 2023. The Report. https://www.classcentral.com/report/mooc-platforms/
  • Tsai, Y., Lin, C., Hong, J., Tsai, K. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers and Education, 121, 18–29. https://doi.org/10.1016/j.compedu.2018.02.011
  • Watson, S. L., Loizzo, J., Watson, W. R., Mueller, C., Lim, J., & Ertmer, P. A. (2016). Instructional design, facilitation, and perceived learning outcomes: An exploratory case study of a human trafficking MOOC for attitudinal change. Educational Technology Research and Development, 64, 1273–1300. https://doi.org/10.1007/s11423-016-9457-2
  • Wei, X., Saab, N., & Admiraal, W. (2023). Do learners share the same perceived learning outcomes in MOOCs? Identifying the role of motivation, perceived learning support, learning engagement, and self-regulated learning strategies. The Internet and Higher Education, 56, Article 100880. https://doi.org/10.1016/j.iheduc.2022.100880
  • 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 & Development, 65, 1195–1214. https://doi.org/10.1007/s11423-017-9513-6
  • Zhu, M., & Doo, M. Y. (2022). The relationship among motivation, self-monitoring, self-management, and learning strategies of MOOC learners. Journal of Computing in Higher Education, 34(2), 321–342. https://doi.org/10.1007/s12528-021-09301-2
  • Zhu, M., Bonk, C. J., & Berri, S. (2022). Fostering self-directed learning in MOOCs: Motivation, learning strategies, and instruction. Online Learning, 26(1), 153–173. https://doi.org/10.24059/olj.v26i1.2629

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.