Massive Open Online Courses (MOOCs) have and continue to change the way in which nontraditional learners’ access education. Although the free element of these has been linked to low completion rates due to no invested interest, the MOOC platform enables innovative technologies and practices to be trialled. Therefore, rather than attributing varied intentions of learners for high drop-out rates, it is suggested that an increase in completion can be achieved through more focussed pedagogical practices. In this way, it is necessary to understand the wider benefits of MOOC engagement for learners and what factors are key to their engagement and retention. The current research qualitatively analysed open feedback obtained from learners that corresponded to their goals of course participation. The feedback was also matched to categorical data that related to initial course intentions, the value of course materials and activities, the preferred extent of instructor interaction, unit completion and their overall rating of the MOOC. Thematic analysis revealed eight key themes that can be linked to engagement and wider benefits of course participation and widely related to professional and educational development, for example, supplementary learning for undergraduate students. Moreover, the MOOC appeared to have encouraged learners to revaluate their perspectives of and attitudes towards Dementia and those diagnosed with it, demonstrating another key element of this course. The open feedback revealed that quality assured MOOCs have significant impact on the lives of enrolled learners and pedagogical design and advances in these courses are considered, particularly in relation to collaborative learning. Finally, the application of MOOCs to wider learning and teaching at Higher Education Institutions (HEIs) is discussed, with emphasis placed on the advantages of readily available resources and scope for scholarly activity.
1. Caufield, M., Collier, A., & Halawa, S. (2013). Rethinking online community in MOOCs used for blended learning. Educause Review Online, 1-11.
2. DeBoer, J., Ho, A., Stump, G., & Breslow, L. (2014). Changing “course:” reconceptualizing educational variables for massive open online courses. Educational Researcher, 43(2), 74-84.
3. Gorard, S. (2001). Quantitative Methods in Educational Research: The role of numbers.
4. Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal (Online First).
5. Guardia, L., Maina, M., & Sangra, A. (2013). MOOC design principles: A pedagogical approach from the learner’s perspective. eLearning Papers, 33.
6. Guo, P. J., & Reinecke, K. (2014). Demographic differences in how students navigate through MOOCs.
7. Hadi, S. M., & Gagen, P. (2016). New model for measuring MOOCs completion rates. Paper presented at the European MOOCs Stakeholder Summit. Retrieved from http://www.academia.edu/20434486/New_model_for_measuring_MOOCs_completion_rates
8. Hadi, S. M., & Rawson, R. (2016). Driving learner engagement and completion within MOOCs: a case for structured learning support. Paper presented at the European MOOCs Stakeholder Summit.
9. Hew, K. F. (2014). Promoting engagement in online courses: what strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology (Online First).
10. Hone, K. S., & El Said, G, R. (2016). Exploring the factors affecting MOOC retention: a survey study. Computers and Education, 98, 157-168.
11. Jansen, D., & Schuwer, R. (2015). Institutional MOOC strategies in Europe. Status report based on a mapping survey conducted in October - December 2014. EADTU. Retrieved from http://www.eadtu.eu/documents/Publications/OEenM/Institutional_MOOC_strategies_in_Europe.pdf
12. Koller, D., Ng, A., Do, C., & Chen, Z. (2013). Retention and intention in massive open online courses: In depth. Educause Review.
13. Leach, M., Hadi, S. M., & Bostock, A. (2016). Supporting diverse learner goals through modular design and micro-learning. Paper presented at the European MOOCs Stakeholder Summit. London: Continuum, made easy.
14. Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29(4), 531-563.
15. Petronzi, D., Hadi, S. M., & Leach, M. (2016). Do self-assigned learning intentions reflect upon or predict MOOC engagement? Unpublished Research Paper.