Participants and completers in programming MOOCs
- 96 Downloads
There are millions of MOOC participants who vary in gender, age, educational level, employment status, intentions, etc. Although MOOC participants’ characteristics have been studied, there is still a lack of knowledge of the divergence between the participants and completers of MOOCs with different levels of difficulty. The term ‘level of difficulty’ as used in this paper encompasses, besides the difficulty of covered topics, the variety of supportive teaching methods and different course durations. The aim of this study was to determine the demographic and social background characteristics of participants and completers in three programming MOOCs with different difficulty levels. It was found that the difficulty of a topic is related to gender, age and educational level distribution in MOOCs. According to our results, previous experience in the topic and the difficulty level of the MOOC influence completion. However, our results were less clear-cut regarding the correlation of age, education and employment status with difficulty level of MOOC. The results can be useful for MOOC instructors in supporting different participant groups, for example, by allowing more flexibility for specific participant groups.
KeywordsMassive open online course MOOC Demographics Programming
Compliance with ethical standards
Conflict of interest
- Adamopoulos, P. (2013). What makes a great MOOC? An interdisciplinary analysis of student retention in online courses. In Proceeding of 34th International Conference on Information Systems: ICIS 2013. Association for Information Systems.Google Scholar
- Biggs, J. (2006). Teaching for quality learning at university: What the student does. Maidenhead: Open University Press.Google Scholar
- Despujol, I. M., Turró, C., Busquets, J., & Cañero, A. (2014). Analysis of demographics and results of student’s opinion survey of a large scale MOOC deployment for the Spanish speaking community. In Frontiers in Education Conference (FIE). Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=andarnumber=7044102andtag=1 Accessed 11.01.2019.
- Goldberg, L. R., Bell, E., King, C., O’Mara, C., McInerney, F., Robinson, A., & Vickers, J. (2015). Relationship between participants’ level of education and engagement in their completion of the Understanding Dementia Massive Open Online Course. BMC Medical Education, 15(60). https://doi.org/10.1186/s12909-015-0344-z.
- Lepp, M., Luik, P., Palts, T., Papli, K., Suviste, R., Säde, M., & Tõnisson, E. (2017a). MOOC in programming: A success story. In Proceedings of the International Conference on e-Learning (ICEL) (pp. 138–147). USA: Academic Publishing International.Google Scholar
- Lepp, M., Luik, P., Palts, T., Papli, K., Suviste, R., Säde, M., et al. (2017b). Self- and automated assessment in programming MOOCs. In D. Joosten-ten Brinke & M. Laanpere (Eds.), Communications in computer and information science. Vol. 653. Technology enhanced assessment (pp. 72–85). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-57744-9_7.CrossRefGoogle Scholar
- Luik, P., Lepp, M., Palts, T., Säde, M., Suviste, R., Tõnisson, E., & Gaiduk, M. (2018). Completion of programming MOOC or dropping out: Are there any differences in motivation? In K. Ntalianis, A. Andreatos & C. Sgouropoulou (Eds.), Proceedings of the 17th European Conference on e-Learning ECEL 2018 (pp. 329–337). Reading: Academic Conferences and Publishing International Limited.Google Scholar
- Morris, N. P., Hotchkiss, S., & Swinnerton, B. (2015). Can demographic information predict MOOC learner outcomes? Paper presented at EMOOCs 2015, Mons, Belgium.Google Scholar
- Onah, D. F. O., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses : behavioural patterns. In Proceedings of 6th International Conference on Education and New Learning Technologies (EDULEARN14) (pp. 5825–5834.) IATED Academy.Google Scholar
- Yukselturk, E., & Bulut, S. (2007). Predictors for Student Success in an Online Course. Educational Technology & Society, 10(2), 71–83.Google Scholar