Skip to main content
SpringerLink
Log in

Motivation in a MOOC: a probabilistic analysis of online learners’ basic psychological needs

  • Published:
Social Psychology of Education Aims and scope Submit manuscript

Abstract

Self-determination theory (SDT) is one of the most well-known approaches to achievement motivation. However, the three basic psychological needs of SDT have not received equivalent attention in the literature: priority has been given to autonomy, followed by the need for competence, with research into relatedness lacking (Bachman and Stewart in Teach Psychol 38: 180–187, 2011. doi:10.1177/0098628311411798). One new educational setting where relatedness may be particularly important is massive open online courses (MOOCs), which provide unprecedented opportunities for either relatedness or isolation. The purpose of the research was to use Bayesian networks (BN) to establish probabilistic relationships between learners’ basic psychological needs in the context of one MOOC. The majority (59 %) of participants (N = 1037; 50 % female and 50 % male) were under 45 (age range was 18–74 years). This sample represented approximately 88 regions and countries. Participants completed a revised Basic Student Needs Scale (Betoret and Artiga in Electron J Res Educ Psychol 9(2): 463–496, 2011). In order to reveal the best structural understanding of SDT within a MOOC learning environment, analysis of the data involved the development of a BN probabilistic model. The best fitting BN model included autonomy, competence, and relatedness—resulting in a probabilistic accuracy of 77.41 %. Analyses revealed participants with high autonomy had an 80.01 % probability of having a moderate level of competence. Relatedness was distinct from the autonomy and competence relationship. The strong inter-connections between autonomy and competence support existing research. The notion that relatedness may be a distinct need, at least in this context, was supported and warrants future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The authors were not Dino 101 course instructors or students, but were provided access as researchers.

  2. Contact the authors if you are interested in the code that generated the results in this paper.

  3. To calculate the accuracy score, we used ~ 80 % of the data (n = 750) to develop the model and then used the remaining 20 % (n = 200) to calculate the accuracy of the model.

References

  • Acock, A. C. (2005). Working with missing values. Journal of Marriage and Family, 67(4), 1012–1028. doi:10.1111/j.1741-3737.2005.00191.x.

    Article  Google Scholar 

  • Ainley, M., & Armatas, C. (2006). Motivational perspectives on students’ responses to learning in virtual learning environments. In J. Weiss, J. Nolan, J. Hunsinger, & P. Trifonas (Eds.), The international handbook of virtual learning environments (Vol. 1, pp. 365–394). The Netherlands: Springer.

    Chapter  Google Scholar 

  • Bachman, C. M., & Stewart, C. (2011). Self-determination theory and web-enhanced course template development. Teaching of Psychology, 38, 180–187. doi:10.1177/0098628311411798.

    Article  Google Scholar 

  • Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

    Google Scholar 

  • Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529.

    Article  Google Scholar 

  • Bayes, T. P. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.

    Article  Google Scholar 

  • Beaven, T., Hauck, M., Comas-Quinn, A., Lewis, T., & de los Arcos, B. (2014). MOOCS: Strking the right balance between facilitation and self-determination. MERLOT Journal of Online Learning and Teaching, 10. http://jolt.merlot.org/

  • Betoret, F. D., & Artiga, A. G. (2011). The relationship among students psychological need satisfaction, approaches to learning, reporting of avoidance strategies and achievement. Electronic Journal of Research in Educational Psychology, 9(2), 463–496.

    Google Scholar 

  • Chen, K.-C., & Jang, S.-J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26, 741–752. doi:10.1016/j.chb.2010.01.011.

    Article  Google Scholar 

  • Chesney, J. (2013a). MOOC v2.0: How Dino 101 is different and what we’ve observed so far (Part 1). Edmonton, Alberta: University of Alberta News and Events [Webpage]. http://uofa.ualberta.ca/digital-learning/digital-learning-at-ualberta/news-and-events/2013/december/mooc-v2-how-Dino-101-is-different-and-what-weve-observed-so-far-part1

  • Chesney, J. (2013b). MOOC v2.0: How Dino 101 is different and what we’ve observed so far (Part 2). Edmonton, Alberta: University of Alberta News and Events [Webpage]. http://uofa.ualberta.ca/digital-learning/digital-learning-at-ualberta/news-and-events/2013/december/mooc-v2-how-Dino-101-is-different-and-what-weve-observed-so-far-part2

  • Clarke, T. (2013). The advance of the MOOCs (massive open online courses): The impending globalization of business education? Emerald Insight: Education and Training, 55(4/5), 403–413.

    Google Scholar 

  • Cusumano, M. A. (2013). Technology strategy and management: Are the costs of ‘free’ too high in online education? Communications of the ACM, 56(4), 26. doi:10.1145/2436256.2436264.

    Article  Google Scholar 

  • Daniels, L. M., Adams, C., & McCaffrey, A. (2016). Emotional and social engagement in an massive open online course: An examination of Dino 101. In S. Y. Tettegah & M. P. McCreery (Eds.), Emotions, technology, and learning (pp. 25–41). London, UK: Elsevier.

    Chapter  Google Scholar 

  • Darwiche, A. (2010). What are Bayesian networks and why are their applications growing across all fields? Communications of the Association for Computing Machinery, 53, 80–90. doi:10.1145/1859204.1859227.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being across life’s domain. Canadian Psychology, 49, 14–23. doi:10.1037/0708-5591.49.1.14.

    Article  Google Scholar 

  • Deci, E. L., Ryan, R. M., Gagné, M., Leone, D. R., Usunov, J., & Kornazheva, B. P. (2001). Need satisfaction, motivation, and well-being in the work organizations of a former Eastern Bloc Country: A cross-cultural study of self-determination. Personality and Social Psychology Bulletin, 27, 930–942. doi:10.1177/0146167201278002.

    Article  Google Scholar 

  • Faye, C., & Sharpe, D. (2008). Academic motivation in university: The role of basic psychological needs and identity formation. Canadian Psychology, 40, 189–199. doi:10.1037/a0012858.

    Google Scholar 

  • Fuster-Parra, P., Garcia-Mas, A., Ponseti, F. J., Palou, P., & Cruz, J. (2014). A Bayesian Network to discover relationships between negative features in sport: A case study of teen players. Quality & Quantity, 48, 1473–1491. doi:10.1007/s11135-013-9848-y.

    Article  Google Scholar 

  • Goodenow, C. (1992). Strengthening the links between educational psychology and the study of social contexts. Educational Psychologist, 27, 177–196.

    Article  Google Scholar 

  • Gravetter, F. J., & Wallnau, L. B. (2009). Statistics for the behavioral sciences. Belmont, CA: Wadsworth.

    Google Scholar 

  • Griffits, A., & Raschella, A. (2014, November 11). Sydney’s new UTS business school building defies convention. Australian Broadcasting Corporation. http://www.abc.net.au/news/2014-11-11/uts-new-business-school-building-defies-convention/5883506

  • Hartnett, M., St. George, A., & Dron, J. (2014). Exploring motivation in an online context: A case study. Contemporary Issues in Technology and Teacher Education, 14. http://www.citejournal.org/vol14/iss1/general/article1.cfm

  • Ho, A. D., Reich, J., Nesterko, S. O., Seaton, D. T., Mullaney, T., Waldo, J. & Chuang, I. (2014, January 21). HarvardX and MITx: The first year of open online courses, Fall 2012-summer 2013 (HarvardX and MITx Working Paper No. 1). http://dx.doi.org/10.2139/ssrn.2381263

  • Ilardi, B. C., Leone, D., Kasser, R., & Ryan, R. M. (1993). Employee and supervisor ratings of motivation: Main effects and discrepancies associated with job satisfaction and adjustment in a factory setting. Journal of Applied Social Psychology, 23, 1789–1805.

    Article  Google Scholar 

  • Klassen, R. M., Perry, N. E., & Frenzel, A. C. (2012). Teachers’ relatedness with students: An underemphasized aspect of teachers’ basic psychological needs. Journal of Educational Psychology, 104, 150–165. doi:10.1037/a0026253.

    Article  Google Scholar 

  • Klassen, R. M., Yerdelen, S., & Durksen, T. L. (2013). Measuring teacher engagement: The development of the Engaged Teacher Scale (ETS). Frontline Learning Research, 1, 33–52.

    Article  Google Scholar 

  • Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Kolowich, S. (2014, January 14). Completion rates aren’t the best way to judge MOOCs, researchers say. The Chronicle of Higher Education. http://chronicle.com/blogs/wiredcampus/completion-rates-arent-the-best-way-to-judge-moocs-researchers-say/49721

  • Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008–2012. The International Review of Research in Open and Distance Learning, 14(3), 202–227.

    Google Scholar 

  • Milyavskaya, M., & Koestner, R. (2011). Psychological needs, motivation, and well-being: A test of self-determination theory across multiple domains. Personality and Individual Differences, 50, 387–391. doi:10.1016/j.paid.2010.10.029.

    Article  Google Scholar 

  • Moe, R. (2014, May 15). The MOOC problem. Hybrid Pedagogy: A Digital Journal of Learning, Teaching, and Technology. http://www.hybridpedagogy.com/journal/mooc-problem/

  • Moller, A. C., Deci, E. L., & Elliot, A. J. (2010). Person-level relatedness and the incremental value of relating. Personality and Social Psychology Bulletin, 36, 754–767. doi:10.1177/0146167210371622.

    Article  Google Scholar 

  • Murphy, K. P. (1997–2002). Bayes Net Toolbox for Matlab [Website]. https://github.com/bayesnet/bnt

  • Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press.

    Google Scholar 

  • Newstok, S. L. (2013). A plea for “close learning”. Liberal Education, 99, 16–19.

    Google Scholar 

  • Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination to educational practice. Theory and Research in Education, 7, 133–144. doi:10.1177/1477878509104318.

    Article  Google Scholar 

  • Noble, D. (2001). The future of the faculty in the digital diploma mill. Academe, 87, 27–32.

    Article  Google Scholar 

  • Oliver, E. J., Markland, D., Hardy, J., & Petherick, C. M. (2008). The effects of autonomy- supportive versus controlling environments on self-talk. Motivation and Emotion, 32, 200–212. doi:10.1007/s11031-008-9097-x.

    Article  Google Scholar 

  • Osterman, K. F. (2000). Students’ need for belonging in the school community. Review of Educational Research, 70, 323–367.

    Article  Google Scholar 

  • Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Pekrun, R., Goetz, T., & Perry, R. P. (2005). Achievement Emotions Questionnaire (AEQ): User’s manual. Munich, Germany: Department of Psychology, University of Munich.

    Google Scholar 

  • Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., & Ryan, R. M. (2000). Daily well-being: The role of autonomy, competence and relatedness. Personality and Social Psychology Bulletin, 26, 419–435. doi:10.1177/0146167200266002.

    Article  Google Scholar 

  • Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach. London, UK: Pearson Education.

    Google Scholar 

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

    Article  Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136.

    Article  Google Scholar 

  • Siemens, G. (2004). Connectivism: A learning theory for the digital age [Blog]. elearnspace. http://www.elearnspace.org/Articles/connectivism.htm

  • Siemens, G. (2012). What is the theory that underpins our MOOCs? [Blog]. Elearnspace. http://www.elearnspace.org/

  • Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52, 1003–1017. doi:10.1177/0013164492052004025.

    Article  Google Scholar 

  • Waldrop, M. M. (2013). Campus 2.0: Massive open online courses are transforming education—and providing fodder for scientific research. Nature, 495, 161–163.

    Article  Google Scholar 

  • Warusavitarana, P. A., Lokuge Dona, K., Piyathilake, H. C., Epitawela, D. D., & Edirisinghe, M. U. (2014). MOOC: A higher education game changer in developing countries. In B. Hegarty, J. McDonald, and S.-K. Loke (Eds.), Rhetoric and reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 359–366). http://ascilite2014.otago.ac.nz/files/fullpapers/321-Warusavitarana.pdf

  • West, P., Rutstein, D. W., Mislevy, R. J., Liu, J., Choi, Y., & Levy, R., et al. (2010). A Bayesian network approach to modeling learning progressions and task performance. (CRESST Report 776). Los Angeles, CA: University of California, National Center for Research on Evaluation, Standards, Student Testing (CRESST). http://www.cse.ucla.edu/products/reports/R776.pdf

  • Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6, 49–78.

    Article  Google Scholar 

  • Williams, G. C., & Deci, E. L. (1996). Internalization of biopsychosocial values by medical students: A test of self-determination theory. Journal of Personality and Social Psychology, 70, 767–779.

    Article  Google Scholar 

  • Yang, Q. (2014). Students motivation in asynchronous online discussions with MOOC Mode. American Journal of Educational Research, 2, 325–330. doi:10.12691/education-2-5-13.

    Article  Google Scholar 

  • Zyphur, M. J., & Oswald, F. L. (2015). Bayesian estimation and inference: A user’s guide. Journal of Management, 41, 390–420. doi:10.1177/0149206313501200.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Education, University of New South Wales, John Goodsell Building, Sydney, NSW, 2052, Australia

    Tracy L. Durksen

  2. Werklund School of Education, University of Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada

    Man-Wai Chu

  3. Department of Computing Science, University of Alberta, 2-21 Athabasca Hall, Edmonton, AB, T6G 2E8, Canada

    Zaheen F. Ahmad

  4. Department of Educational Psychology, University of Alberta, 6-102 Education North, Edmonton, AB, T6G 2G5, Canada

    Amanda I. Radil & Lia M. Daniels

Authors
  1. Tracy L. Durksen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Man-Wai Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zaheen F. Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amanda I. Radil
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lia M. Daniels
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tracy L. Durksen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Durksen, T.L., Chu, MW., Ahmad, Z.F. et al. Motivation in a MOOC: a probabilistic analysis of online learners’ basic psychological needs. Soc Psychol Educ 19, 241–260 (2016). https://doi.org/10.1007/s11218-015-9331-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11218-015-9331-9

Keywords

Search

Navigation