Relationship between motivations, personality traits and intention to continue using MOOCs

Abstract

MOOCs represent an emerging model for delivering education services. Notwithstanding their potential, they suffer significant dropout rates, which have been attributed to the low motivation of the registered students. This research seeks to understand the variance in the levels of intention to continue using MOOCs (ICM) in relation to motivation (internal and external) and personality traits (agreeableness, extraversion, and conscientiousness). In this study, Structural Equation Modelling was applied to conduct an analysis of 212 professionals from Saudi Arabia who had used MOOCs in the previous three months. Internal motivations, but not the external ones, affect the ICM. Conscientiousness directly affects ICM and external motivation. Agreeableness affects the ICM with full mediation of internal motivations. Extraversion and agreeableness affect internal motivations. The main implication of this research is that, to use MOOCs in the future, different personalities need different motivations in their first use of them.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Al-Qirim, N., Rouibah, K., Tarhini, A., Serhani, M. A., Yammahi, A. R., & Yammahi, M. A. (2018). Towards a personality understanding of information technology students and their IT learning in UAE university. Education and Information Technologies, 23(1), 29–40. https://doi.org/10.1007/s10639-017-9578-1.

    Article  Google Scholar 

  2. Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers in Education, 80, 28–38. https://doi.org/10.1016/j.compedu.2014.08.006.

    Article  Google Scholar 

  3. Ashton, M. C., Lee, K., & Paunonen, S. V. (2002). What is the central feature of extraversion? Social attention versus reward sensitivity. Journal of Personality and Social Psychology, 83(1), 245–252. https://doi.org/10.1037/0022-3514.83.1.245.

    Article  Google Scholar 

  4. Blackwell, D., Leaman, C., Tramposch, R., Osborne, C., & Liss, M. (2017). Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personality and Individual Differences, 116, 69–72.://doi.org/10.1016/J.PAID.2017.04.039.

  5. Brahimi, T., & Sarirete, A. (2015). Learning outside the classroom through MOOCs. Computers in Human Behavior, 51, 604–609. https://doi.org/10.1016/j.chb.2015.03.013.

    Article  Google Scholar 

  6. Bratman, M. (1987). Intention, plans, and practical reason. Boston: Harvard Business School Press.

    Google Scholar 

  7. Bratt, C., Sidanius, J., Abrams, D., van der Toorn, J., Jost, J. T., & Chaikalis-Petritsis, V. (2011). Why men (and women) do and Don’t rebel. Personality and Social Psychology Bulletin, 38(2), 197–208. https://doi.org/10.1177/0146167211422544.

    Article  Google Scholar 

  8. Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005.

    Article  Google Scholar 

  9. Byrne, B. M. (1989). A primer of LISREL : Basic applications and programming for confirmatory factor analytic models (1st ed.). New York: Springer Verlag.

    Book  Google Scholar 

  10. Chamorro-Premuzic, T., & Furnham, A. (2003). Personality predicts academic performance: Evidence from two longitudinal university samples. Journal of Research in Personality, 37(4), 319–338. https://doi.org/10.1016/S0092-6566(02)00578-0.

    Article  Google Scholar 

  11. Chen, G., Davis, D., Hauff, C., & Houben, G.-J. (2016). On the impact of personality in massive open online learning. In Proceedings of the 2016 conference on user modeling adaptation and personalization - UMAP ‘16 (pp. 121–130). Halifax, NS, Canada: ACM Press. https://doi.org/10.1145/2930238.2930240.

    Google Scholar 

  12. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668. https://doi.org/10.1037/0033-2909.125.6.627.

    Article  Google Scholar 

  13. Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272–281. https://doi.org/10.1016/j.chb.2015.03.022.

    Article  Google Scholar 

  14. Deng, R., Benckendorff, P., & Gannaway, D. (2019). Progress and new directions for teaching and learning in MOOCs. Computers in Education, 129, 48–60. https://doi.org/10.1016/j.compedu.2018.10.019.

    Article  Google Scholar 

  15. DeYoung, C. G. (2010). Toward a theory of the big five. Psychological Inquiry, 21(1), 26–33. https://doi.org/10.1080/10478401003648674.

    Article  Google Scholar 

  16. DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the big five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880.

    Article  Google Scholar 

  17. DeYoung, C. G., Carey, B. E., Krueger, R. F., & Ross, S. R. (2016). Ten aspects of the big five in the personality inventory for DSM-5. Personality Disorders, Theory, Research, and Treatment, 7(2), 113–123. https://doi.org/10.1037/per0000170.

    Article  Google Scholar 

  18. Feiler, D. C., & Kleinbaum, A. M. (2015). Popularity, similarity, and the network extraversion bias. Psychological Science, 26(5), 593–603. https://doi.org/10.1177/0956797615569580.

    Article  Google Scholar 

  19. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–55. https://doi.org/10.2307/3151312.

    Article  Google Scholar 

  20. Gomez-Zermeno, M., & Aleman, L. (2016). Research analysis on Mooc course dropout and R. Turkish Online Journal of Distance Education, 17(4), 3–14.

    Google Scholar 

  21. Hair, J. F., Tatham, R. L., Anderson, R. E., & William, B. (1998). Multivariate data analysis (5th ed.). New York: Prentice Hall.

    Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.

    Article  Google Scholar 

  24. Hudiburg, R. A., Pashaj, I., & Wolfe, R. (1999). Preliminary investigation of computer stress and the big five personality factors. Psychological Reports, 85(2), 473–480. https://doi.org/10.2466/PR0.85.6.473-480.

    Article  Google Scholar 

  25. 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 in Education, 122, 260–272. https://doi.org/10.1016/j.compedu.2018.01.003.

    Article  Google Scholar 

  26. Jost, J. T., & Burgess, D. (2000). Attitudinal ambivalence and the conflict between group and system justification motives in low status groups. Personality and Social Psychology Bulletin, 26(3), 293–305. https://doi.org/10.1177/0146167200265003.

    Article  Google Scholar 

  27. Jost, J. T., Banaji, M. R., & Nosek, B. A. (2004). A decade of system justification theory: Accumulated evidence of conscious and unconscious bolstering of the status quo. Political Psychology, 25(6), 881–919. https://doi.org/10.1111/j.1467-9221.2004.00402.x.

    Article  Google Scholar 

  28. Jost, J. T., Liviatan, I., Van Der Toorn, J., Alison Ledgerwood, A. M., & Nosek, B. A. (2011). The psychology of justice and legitimacy. In R. Bobocel, A. C. Kay, M. P. Zanna, & J. M. Olson (Eds.), The psychology of justice and legitimacy (1st ed., pp. 79–102). New York: Psychology Press. https://doi.org/10.4324/9780203837658.

    Google Scholar 

  29. Kim, T. d., Yang, M.,. y., Bae, J., Min, B. a., Lee, I., & Kim, J. (2017). Escape from infinite freedom: Effects of constraining user freedom on the prevention of dropout in an online learning context. Computers in Human Behavior, 66, 217–231. https://doi.org/10.1016/j.chb.2016.09.019.

  30. Kortum, P., & Oswald, F. L. (2018). The impact of personality on the subjective assessment of usability. International Journal of Human Computer Interaction, 34(2), 177–186. https://doi.org/10.1080/10447318.2017.1336317.

    Article  Google Scholar 

  31. Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., & Graepel, T. (2014). Manifestations of user personality in website choice and behaviour on online social networks. Machine Learning, 95(3), 357–380. https://doi.org/10.1007/s10994-013-5415-y.

    MathSciNet  Article  Google Scholar 

  32. Laurin, K., Kay, A. C., Proudfoot, D., & Fitzsimons, G. J. (2013). Response to restrictive policies: Reconciling system justification and psychological reactance. Organizational Behavior and Human Decision Processes, 122(2), 152–162. https://doi.org/10.1016/j.obhdp.2013.06.004.

    Article  Google Scholar 

  33. Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48. https://doi.org/10.1016/J.IHEDUC.2015.12.003.

    Article  Google Scholar 

  34. Liu, D., & Campbell, W. K. (2017). The big five personality traits, big two metatraits and social media: A meta-analysis. Journal of Research in Personality, 70, 229–240. https://doi.org/10.1016/j.jrp.2017.08.004.

    Article  Google Scholar 

  35. Longstaff, E. (2017). Ritual in online communities: A study of post-voting in MOOC discussion forums. International Journal of Human Computer Interaction, 33(8), 655–663. https://doi.org/10.1080/10447318.2016.1277639.

    Article  Google Scholar 

  36. Lung-Guang, N. (2019). Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers in Education, 134, 50–62. https://doi.org/10.1016/j.compedu.2019.02.004.

    Article  Google Scholar 

  37. Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psychological Bulletin, 97(3), 562–582. https://doi.org/10.1037/0033-2909.97.3.562.

    Article  Google Scholar 

  38. McCrae, R. R., & Costa, P. T. (1997). Personality trait structure as a human universal. American Psychologist, 52(5), 509–516. https://doi.org/10.1037/0003-066X.52.5.509.

    Article  Google Scholar 

  39. Mccrae, R. R., & Löckenhoff, C. E. (2010). Self-regulation and the five-factor model of personality traits. In Handbook of Personality and Self-Regulation (pp. 145–168). Wiley-Blackwell. https://doi.org/10.1002/9781444318111.ch7.

  40. Milligan, C., & Littlejohn, A. (2017). Why study on a MOOC? The motives of students and professionals. International Review of Research in Open and Distance Learning, 18(2), 92–102. https://doi.org/10.19173/irrodl.v18i2.3033.

    Article  Google Scholar 

  41. Picazo-Vela, S., Chou, S. Y., Melcher, A. J., & Pearson, J. M. (2010). Why provide an online review? An extended theory of planned behavior and the role of big-five personality traits. Computers in Human Behavior, 26(4), 685–696. https://doi.org/10.1016/j.chb.2010.01.005.

    Article  Google Scholar 

  42. Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879.

    Article  Google Scholar 

  43. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020.

    Article  Google Scholar 

  44. Seemann, E. A., Buboltz, W. C., Thomas, A., Soper, B., & Wilkinson, L. (2005). Normal personality variables and their relationship to psychological reactance. Individual Differences Research, 3(2), 88–98.

    Google Scholar 

  45. Simmering, M. G., Arseneault, J. M., Ross, C., Sisic, M., Orr, E. S., & Orr, R. R. (2009). Personality and motivations associated with Facebook use. Computers in Human Behavior, 25(2), 578–586. https://doi.org/10.1016/j.chb.2008.12.024.

    Article  Google Scholar 

  46. Svendsen, G. B., Johnsen, J.-A. K., Almås-Sørensen, L., & Vittersø, J. (2013). Personality and technology acceptance: The influence of personality factors on the core constructs of the technology acceptance model. Behaviour & Information Technology, 32(4), 323–334. https://doi.org/10.1080/0144929X.2011.553740.

    Article  Google Scholar 

  47. Tabak, F., & Nguyen, N. T. (2013). Technology acceptance and performance in online learning environments: Impact of self-regulation. Journal of Online Learning and Teaching, 9(1), 116–130.

    Google Scholar 

  48. Torrance, E. P., & Brehm, J. W. (1968). A theory of psychological reactance. The American Journal of Psychology, 81(1), 133. https://doi.org/10.2307/1420824.

    Article  Google Scholar 

  49. Tsai, Y. h., Lin, C. h., Hong, J. c., & Tai, K. h. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers in Education, 121, 18–29. https://doi.org/10.1016/j.compedu.2018.02.011.

    Article  Google Scholar 

  50. Wang, J.-L., Jackson, L. A., Zhang, D.-J., & Su, Z.-Q. (2012). The relationships among the big five personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students’ uses of social networking sites (SNSs). Computers in Human Behavior, 28(6), 2313–2319. https://doi.org/10.1016/j.chb.2012.07.001.

    Article  Google Scholar 

  51. Watted, A., & Barak, M. (2018). Motivating factors of MOOC completers: Comparing between university-affiliated students and general participants. The Internet and Higher Education, 37, 11–20. https://doi.org/10.1016/j.iheduc.2017.12.001.

    Article  Google Scholar 

  52. Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G. J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human Computer Interaction, 35(4–5), 356–373. https://doi.org/10.1080/10447318.2018.1543084.

    Article  Google Scholar 

  53. 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.

    Article  Google Scholar 

  54. Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding Student Motivation, Behaviors and Perceptions in MOOCs. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing - CSCW ‘15 (pp. 1882–1895). New York, USA: ACM Press. https://doi.org/10.1145/2675133.2675217.

  55. Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92–93, 194–203. https://doi.org/10.1016/j.compedu.2015.10.012.

    Article  Google Scholar 

  56. Zimmerman, B. J. (1995). Self-regulation involves more than metacognition: A social cognitive perspective. Educational Psychologist, 30(4), 217–221. https://doi.org/10.1207/s15326985ep3004_8.

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by research grant e-Madrid-CM of the CAM (ref. P2018/TCS-4307). The e-Madrid-CM grant also is co-financed by Structural Funds FSE and FEDER.

Author information

Affiliations

Authors

Corresponding author

Correspondence to J. Ángel Velázquez-Iturbide.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abdullatif, H., Velázquez-Iturbide, J.Á. Relationship between motivations, personality traits and intention to continue using MOOCs. Educ Inf Technol 25, 4417–4435 (2020). https://doi.org/10.1007/s10639-020-10161-z

Download citation

Keywords

  • MOOCs
  • Personality traits
  • Motivations
  • Intention to use