Journal of Formative Design in Learning

, Volume 2, Issue 2, pp 102–113 | Cite as

Motivating Students in Massive Open Online Courses (MOOCs) Using the Attention, Relevance, Confidence, Satisfaction (ARCS) Model

  • Kun LiEmail author
  • David Richard Moore


Massive Open Online Courses (MOOCs) often have low persistence rates, which may be attributed to a learners’ lack of motivation. In this design-based research study, Keller’s Attention, Relevance, Confidence, Satisfaction (ARCS) motivational design model was integrated into two MOOCs as an initial exploration of how to design effective motivational interventions in MOOC environment. The Instructional Motivation Materials Scale (IMMS) was used to measure learners’ perceptions and reactions to the course components, in terms of the ARCS model, in both MOOCs. The whole design, implementation, and evaluation process was documented and reflected upon to provide practical guidance on designing motivational-enhanced materials in MOOC environments. The results revealed patterns of learners selectively paying attention, drawing relevance for self-determined reasons, having high confidence, and deriving satisfaction from multiple sources.


ARCS model Design-based research MOOCs Motivation Motivational design 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. Belanger, Y., & Thornton, J. (2013). Bioelectricity: a quantitative approach Duke University’s first MOOC (Report). Retrieved from:
  2. Burd, E. L., Smith, S. P., & Reisman, S. (2015). Exploring business models for MOOCs in higher education. Innovative Higher Education, 40(1), 37–49. Scholar
  3. Butler, B. (2012). Massive open online courses: legal and policy issues for research libraries (pp. 1–15). Association of Research Libraries.Google Scholar
  4. Chang, M.-M., & Lehman, J. D. (2002). Learning foreign language through an interactive multimedia program: an experimental study on the effects of the relevance component of the ARCS model. Calico Journal, 20(1), 81–98.Google Scholar
  5. ChanLin, L.-J. (2009). Applying motivational analysis in a Web-based course. Innovations in Education & Teaching International, 46(1), 91–103. Scholar
  6. Evans, B. J., Baker, R. B., & Dee, T. S. (2016). Persistence patterns in massive open online courses (MOOCs). The Journal of Higher Education, 87(2), 206–242. Scholar
  7. Gerson, S. M. (2000). E-CLASS: creating a guide to online course development for distance learning faculty. Online Journal of Distance Learning Administration, 3(4), 1–18.Google Scholar
  8. Gibson, D., Ostashewski, N., Flintoff, K., Grant, S., & Knight, E. (2015). Digital badges in education. Education and Information Technologies, 20(2), 403–410. Scholar
  9. Glance, D. G., Forsey, M., & Riley, M. (2013). The pedagogical foundations of massive open online courses. First Monday, 18(5).
  10. Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: an empirical study of mooc videos. In Proceedings of the first ACM conference on Learning@ scale conference (pp. 41–50). ACM. Retrieved from
  11. Hart, C. (2012). Factors associated with student persistence in an online program of study: a review of the literature. Journal of Interactive Online Learning, 11(1), 19–42.Google Scholar
  12. Hodges, C. B., & Kim, C. (2013). Improving college students’ attitudes toward mathematics. TechTrends, 57(4), 59–66. Scholar
  13. Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: a survey study. Computers & Education, 98, 157–168. Scholar
  14. Huett, J. B., Moller, L., Young, J., Bray, M., & Huett, K. C. (2008b). Supporting the distant student: the effect of arcs-based strategies on confidence and performance. Quarterly Review of Distance Education, 9(2), 113–126.Google Scholar
  15. 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).
  16. Keller, J. M. (1987a). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2–10. Scholar
  17. Keller, J. M. (1987b). Instructional materials motivation scale (IMMS). Unpublished Manuscript, The Florida State University.Google Scholar
  18. Keller, J. M. (2006). What is motivational design? (pp. 1–12). Florida: Florida State University.Google Scholar
  19. Keller, J. M. (2008). An integrative theory of motivation, volition, and performance. Technology, Instruction, Cognition & Learning, 6(2), 79–104.Google Scholar
  20. Keller, J. M. (2010). Motivational design for learning and performance: the ARCS model approach (1st ed.). New York: Springer.CrossRefGoogle Scholar
  21. Keller, J. M., & Suzuki, K. (2004). Learner motivation and e-learning design: a multinationally validated process. Journal of Educational Media, 29(3), 229–239.CrossRefGoogle Scholar
  22. Kelly, A. (2004). Design research in education: yes, but is it methodological? The Journal of the Learning Sciences, 13(1), 115–128.CrossRefGoogle Scholar
  23. Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 170–179). ACM. Retrieved from:
  24. Koper, R., & Olivier, B. (2004). Representing the learning design of units of learning. Journal of Educational Technology & Society; Palmerston North, 7(3), n/a.Google Scholar
  25. Lei, S. A. (2010). Intrinsic and extrinsic motivation: evaluating benefits and drawbacks from college instructors’ perspectives. Journal of Instructional Psychology, 37(2), 153–160.Google Scholar
  26. Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: a literature review. Computers & Education, 122, 54–62. Scholar
  27. Liu, O. L., Bridgeman, B., & Adler, R. M. (2012). Measuring learning outcomes in higher education: motivation matters. Educational Researcher, 41(9), 352–362. Scholar
  28. Ma, Y., & Harmon, S. W. (2009). A case study of design-based research for creating a vision prototype of a technology-based innovative learning environment. Journal of Interactive Learning Research, 20(1), 75–93.Google Scholar
  29. Masters, K. (2011). A brief guide to understanding MOOCs. The Internet Journal of Medical Education, 1(2). Retrieved from:
  30. Means, T. B., Jonassen, D. H., & Dwyer, F. M. (1997). Enhancing relevance: embedded ARCS strategies vs. purpose. Educational Technology Research and Development, 45(1), 5–17. Scholar
  31. Nawrot, I., & Doucet, A. (2014). Building engagement for MOOC students: introducing support for time management on online learning platforms. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (pp. 1077–1082). International World Wide Web Conferences Steering Committee. Retrieved from
  32. O’Toole, R. (2013). Pedagogical strategies and technologies for peer assessment in Massively Open Online Courses (MOOCs) (Unpublished Discussion Paper). University of Warwick, Coventry, UK: University of Warwick. Retrieved from:
  33. Ocak, M. A., & Akçayır, M. (2013). Do motivation tactics work in blended learning environments?: the ARCS model approach. International Journal of Social Sciences & Education, 3(4), 1058–1070.Google Scholar
  34. Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd ed.). Thousand Oaks: Sage.Google Scholar
  35. Reeves, T. C. (2000). Enhancing the worth of instructional technology research through “design experiments” and other development research strategies. Presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA, USA.Google Scholar
  36. Richardson, J., & Swan, K. (2003). Examining social presence in online courses in relation to students’ perceived learning and satisfaction. JALN, 7(1), 68–88.Google Scholar
  37. Sankaran, S. R., & Bui, T. (2001). Impact of learning strategies and motivation on performance: a study in web-based instruction. Journal of Instructional Psychology, 28(3), 191–198.Google Scholar
  38. Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist, 25(1), 71–86.CrossRefGoogle Scholar
  39. Shapiro, H. B., Lee, C. H., Roth, N. E. W., Li, K., Cetinkaya-Rundel, M., & Canelas, D. A. (2017). Understanding the massive open online course (MOOC) student experience: an examination of attitudes, motivations, and barriers. Computers & Education, 110, 35–50. Scholar
  40. Small, R. V., & Gluck, M. (1994). The relationship of motivational conditions to effective instructional attributes: a magnitude scaling approach. Educational Technology, 34(8), 33–40.Google Scholar
  41. Song, S. H. (2000). Research issues of motivation in web-based instruction. Quarterly Review of Distance Education, 1(3), 225–229.Google Scholar
  42. Starcher, K., & Proffitt, D. (2011). Encouraging students to read: what professors are (and aren’t) doing about it. International Journal of Teaching & Learning in Higher Education, 23(3), 396–407.Google Scholar
  43. Swan, K., & Shih, L. F. (2005). On the nature and development of social presence in online course discussions. Journal of Asynchronous Learning Networks, 9(3), 115–136.Google Scholar
  44. Touré-Tillery, M., & Fishbach, A. (2014). How to measure motivation: a guide for the experimental social psychologist. Social and Personality Psychology Compass, 8(7), 328–341.CrossRefGoogle Scholar
  45. Tschofen, C., & Mackness, J. (2012). Connectivism and dimensions of individual experience. International Review of Research in Open & Distance Learning, 13(1), 124–143.CrossRefGoogle Scholar
  46. Visser, L., Plomp, T., Amirault, R. J., & Kuiper, W. (2002). Motivating students at a distance: the case of an international audience. Educational Technology Research and Development, 50(2), 94–110.CrossRefGoogle Scholar
  47. Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53(4), 5–23.CrossRefGoogle Scholar
  48. 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 (pp. 1882–1895). New York: ACM.

Copyright information

© Association for Educational Communications & Technology 2018

Authors and Affiliations

  1. 1.Duke UniversityDurhamUSA
  2. 2.College of EducationOhio UniversityAthensUSA

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