Abstracts
Abstract
Massive Open Online Courses (MOOCs), capable of providing free (or low cost) courses for millions of learners anytime and anywhere, have gained the attention of researchers, educational institutions, and learners worldwide. Even though they provide several benefits, there are still some criticisms of MOOCs. For instance, MOOCs’ high dropout rates or predominantly elite participation are considered to be important problems. In order to develop solutions for these problems, a deeper understanding of MOOCs is required. Today, despite the availability of several research studies about MOOCs, there is a shortage of in-depth research on course characteristics, learner characteristics, and predictors of certification rates. This study examined MOOC and learner characteristics in detail and explored the predictors of course certification rates based on data from 122 Massachusetts Institute of Technology MOOCs (MITx) on edX platform as well as data about the 2.8 million participants registered in these MOOCs. The results indicated that as the number of courses offered and the number of learners enrolled increased in years, there was a decrease in the certification rates among enrolled learners. According to our results, the number of average chapters completed, total forum messages, and mean age predicted course certification rates positively. On the other hand, the total number of chapters in a course predicted the course certification rates negatively. Based on these results, shorter and more interactive MOOCs are recommended by considering the needs of the learners, course content design, and strategies encouraging the enrolled students to enter the courses.
Keywords:
- MOOCs,
- MITx,
- online learning,
- massive open online courses
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Bibliography
- Adair, D., Alman, S. W., Budzick, D., Grisham, L. M., Mancini, M. E., & Thackaberry, A. S. (2014). Many shades of MOOCs. Internet Learning, 3(1), 53-72. doi: 10.1.1.883.3241
- Aparicio, M., Oliveira, T., Bacao, F., & Painho, M. (2019). Gamification: A key determinant of massive open online course (MOOC) success. Information & Management, 56(1), 39-54. doi: 10.1016/j.im.2018.06.003
- Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: Examining aspects of language and social engagement. Computers & Education, 94, 49-60. doi: 10.1016/j.compedu.2015.11.010
- Bayeck, R. (2016). Exploratory study of MOOC learners’ demographics and motivation: The case of students involved in groups. Open Praxis, 8(3), 223-233. doi: 10.5944/openpraxis.8.3.282
- Beaven, T., Codreanu, T., & Creuzé, A. (2014). Motivation in a language MOOC: Issues for course designers. In M. Elena & B. Elena (Eds.), Language MOOCs: Providing learning, transcending boundaries (pp. 48-66). Berlin, Germany: De Gruyter Open.
- Bonafini, F. C. (2017). The effects of participants’ engagement with videos and forums in a MOOC for teachers’ professional development. Open Praxis, 9(4), 433-447. doi: 10.5944/openpraxis.9.4.637
- Cagiltay, K., Esfer, S., & Celik, B. (2020). Insights into a nationwide pdMOOC portal: bilgeis.net of Turkey. In K. Zhang, C. J. Bonk, T. C. Reeves, & T. H. Reynolds (Eds.), MOOCs and open education in the global south: Challenges, successes, and Opportunities (pp.130-139). New York, NY: Routledge. doi: 10.4324/9780429398919
- Christensen, G., Steinmetz, A., Alcorn, B., Bennett, A., Woods, D., & Emanuel, E. (2013). The MOOC phenomenon: Who takes massive open online courses and why? (Working paper). Retrieved from Elsiver website: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350964
- Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education. New York, NY: Routledge.
- Cohen, A., Shimony, U., Nachmias, R., & Soffer, T. (2019). Active learners’ characterization in MOOC forums and their generated knowledge. British Journal of Educational Technology, 50(1), 177-198. doi: 10.1111/bjet.12670
- De Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218-231. doi: 10.1111/jcal.12130
- DeBoer, J., Stump, G. S., Seaton, D., & Breslow, L. (2013). Diversity in MOOC students’ backgrounds and behaviors in relationship to performance in 6.002 x. In Proceedings of the Sixth Learning International Networks Consortium Conference (Vol. 4, pp. 16-19). Retrieved from https://linc.mit.edu/linc2013/proceedings/Session2/Session2DeBoer-Stump.pdf
- Deng, R., Benckendorff, P., & Gannaway, D. (2019). Progress and new directions for teaching and learning in MOOCs. Computers & Education, 129, 48-60. doi: 10.1016/j.compedu.2018.10.019
- Deshpande, A., & Chukhlomin, V. (2017). What makes a good MOOC: A field study of factors impacting student motivation to learn. American Journal of Distance Education, 31(4), 275-293. doi: 10.1080/08923647.2017.1377513
- Despujol, I. M., Turró, C., Castañeda, L., & Busquets, J. (2017). Effect of free certificate discontinuation in completion rates of MOOC. In European Conference on Massive Open Online Courses (pp. 182-187). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-59044-8_21
- Diver, P., & Martinez, I. (2015). MOOCs as a massive research laboratory: Opportunities and challenges. Distance Education, 36(1), 5-25. doi: 10.1080/01587919.2015.1019968
- Ferguson, R., & Clow, D. (2015). Examining engagement: Analysing learner subpopulations in massive open online courses (MOOCs). In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 51-58). Poughkeepsie, NY, USA: ACM. doi: 10.1145/2723576.2723606
- Fraenkel, J. R., & Wallen, N. E. (2009). How to design and evaluate research in education (7th ed.). New York, NY: McGraw-Hill.
- Goldwasser, M., Mankoff, C., Manturuk, K., Schmid, L., & Whitfield, K. E. (2016). Who is a student: Completion in Coursera courses at Duke University. Current Issues in Emerging eLearning, 3(1), 125-138. Retrieved from https://scholarworks.umb.edu/ciee/vol3/iss1/8/
- Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925-955. doi: 10.3102/0002831215584621
- Guo, P. J., & Reinecke, K. (2014). Demographic differences in how students navigate through MOOCs. In Proceedings of the First ACM conference on Learning@ Scale Conference (pp. 21-30). Atlanta, Georgia, USA: ACM. doi: 10.1145/2556325.2566247
- 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. doi: 10.1016/j.edurev.2014.05.001
- Ho, A., Chuang, I., Reich, J., Coleman, C., Whitehill, J., Northcutt, C.,... & Petersen, R. (2015). HarvardX and MITx: Two years of open online courses fall 2012-summer 2014 (Working Paper). Retrieved from HarvardX website: doi: 10.2139/ssrn.2586847
- Ho, A., Reich, J., Nesterko, S., Seaton, D., Mullaney, T., Waldo, J., & Chuang, I. (2014). HarvardX and MITx: The first year of open online courses, fall 2012-summer 2013 (Working Paper). Retrieved from HarvardX and MITx website: doi: 10.2139/ssrn.2381263
- Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157-168. doi: 10.1016/j.compedu.2016.03.016
- Hsu, Y., & Shiue, Y. (2005). The effect of self-directed learning readiness on achievement comparing face-to face and two-way distance learning instruction. International Journal of Instructional Media, 32(2), 143-156. Retrieved from https://eric.ed.gov/?id=EJ698905
- 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), 133-160. doi: 10.19173/irrodl.v15i1.1651
- Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. The International Review of Research in Open and Distributed Learning, 16(3), 341-358. doi: 10.19173/irrodl.v16i3.2112
- Jung, E., Kim, D., Yoon, M., Park, S., & Oakley, B. (2019). The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education, 128, 377-388. doi: 10.1016/j.compedu.2018.10.001
- Junjie, Z. (2017). Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective. Australasian Journal of Educational Technology, 33(5). doi: 10.14742/ajet.2914
- Kizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners: Toward data-driven design with the OLEI scale. ACM Transactions on Computer-Human Interaction, 22(2), 6. doi: 10.1145/2699735
- Khalil, H., & Ebner, M. (2014). MOOCs completion rates and possible methods to improve retention: A literature review. In EdMedia+ Innovate Learning (pp. 1305-1313). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/147656
- Knupfer, N. N. & McLellan, H. (1996). Descriptive research methodologies. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 1196-1212). New York, NY: Macmillan.
- Kop, R., Fournier, H., & Mak, J. S. F. (2011). A pedagogy of abundance or a pedagogy to support human beings? Participant support on massive open online courses. International Review of Research in Open and Distributed Learning, 12(7), 74-93. doi: 10.19173/irrodl.v12i7.1041
- Li, Q., & Baker, R. (2018). The different relationships between engagement and outcomes across participant subgroups in massive open online courses. Computers & Education, 127, 41-65. doi: 10.1016/j.compedu.2018.08.005
- Loizzo, J., & Ertmer, P. A. (2016). MOOCocracy: The learning culture of massive open online courses. Educational Technology Research and Development, 64(6), 1013-1032. doi: 10.1007/s11423-016-9444-7
- Macleod, H., Haywood, J., Woodgate, A., & Alkhatnai, M. (2015). Emerging patterns in MOOCs: Learners, course designs and directions. TechTrends, 59(1), 56-63. doi: 10.1007/s11528-014-0821-y
- Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses. Computers & Education, 80, 77-83. doi: 10.1016/j.compedu.2014.08.005.
- Morris, N. P., Hotchkiss, S., & Swinnerton, B. (2015). Can demographic information predict MOOC learner outcomes? In Proceedings of the European MOOC Stakeholder Summit, (pp. 199-207) Mons, Belgium. Retrieved from http://eprints.whiterose.ac.uk/86184
- Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Essex, England: Pearson Education.
- Pardos, Z. A., Bergner, Y., Seaton, D. T., & Pritchard, D. E. (2013). Adapting bayesian knowledge tracing to a massive open online course in edX. EDM, 13, 137-144. doi: 10.1.1.668.6548
- Porter, W. W., Graham, C. R., Spring, K. A., & Welch, K. R. (2014). Blended learning in higher education: Institutional adoption and implementation. Computers & Education, 75, 185-195. doi: 10.1016/j.compedu.2014.02.011
- Rodriguez, C. O. (2012). MOOCs and the AI-Stanford like courses: Two successful and distinct course formats for massive open online courses. European Journal of Open, Distance and E-learning, 2012(1). Retrieved from https://files.eric.ed.gov/fulltext/EJ982976.pdf
- Shah, D. (2018a, December 11). By the numbers: MOOCs in 2018 (Blog post). Class Central. Retrieved from https://www.class-central.com/report/mooc-stats-2018
- Shah, D. (2018b, August 20). Class central’s top 50 MOOCs of all time (2018 edition; Blog post). Class Central. Retrieved from https://www.class-central.com/report/top-50-moocs-2018-edition
- Sinha, N. (2017, December). Year in review: 10 most popular courses in 2017 (Blog post). CourseraBlog. Retrieved from https://blog.coursera.org/year-review-10-popular-courses-2017
- Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston, MA: Pearson Education.
- 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(6), 1273-1300. doi: 10.1007/s11423-016-9457-2
- Watson, S. L., Watson, W. R., Yu, J. H., Alamri, H., & Mueller, C. (2017). Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study. Computers & Education, 114, 274-285. doi: 10.1016/j.compedu.2017.07.005
- 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 and Development, 65(5), 1195-1214. doi: 10.1007/s11423-017-9513-6
- Young, J. R. (2018, June). How blockbuster MOOCs could shape the future of teaching. EdSurge. Retrieved from https://www.edsurge.com/news/2018-06-19-how-blockbuster-moocs-could-shape-the-future-of-teaching