Participants and completers in programming MOOCs

• 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.

• Allione, G., & Stein, R. M. (2016). Mass attrition: An analysis of drop out from principles of microeconomics MOOC. The Journal of Economic Education, 47(2), 174–186.

  Article  Google Scholar 

• Bayeck, R. Y. (2016). Exploratory study of MOOC learners’ demographics and motivation: The case of students involved in groups. Open Praxis, 8(3), 223–233.

  Article  Google Scholar 

• Biggs, J. (2006). Teaching for quality learning at university: What the student does. Maidenhead: Open University Press.

  Google Scholar 

• 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.

  Article  Google Scholar 

• Castano-Munoz, J., Kreijns, K., Kalz, M., & Punie, Y. (2017). Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey. Journal of Computing in Higher Education, 29(1), 28–46. https://doi.org/10.1007/s12528-016-9123-z.

  Article  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.

• Downes, S. (2017). New models of open and distributed learning. In M. Jemni, Kinshuk, & M. Khribi (Eds.), Lecture Notes in Educational Technology. Open education: from OERs to MOOCs (pp. 1–22). Berlin: Springer-Verlag. https://doi.org/10.1007/978-3-662-52925-6_1.

  Chapter  Google Scholar 

• Engle, D., Mankoff, C., & Carbrey, J. (2015). Coursera’s Introductory Human Physiology Course: Factors that Characterize Successful Completion of a MOOC. International Review of Research in Open and Distributed Learning, 16(2), 46–68.

  Article  Google Scholar 

• 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. https://doi.org/10.1080/00221546.2016.11777400.

  Article  Google Scholar 

• Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127–203. https://doi.org/10.1007/s11257-018-9203-z.

  Article  Google Scholar 

• Glass, C. R., Shiokawa-Baklan, M. S., & Saltarelli, A. J. (2016). Who takes MOOCs? New Directions for Institutional Research, 167, 41–55. https://doi.org/10.1002/ir.20153.

  Article  Google Scholar 

• 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.

• 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. https://doi.org/10.3102/0002831215584621.

  Article  Google Scholar 

• 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. https://doi.org/10.1016/j.edurev.2014.05.001.

  Article  Google Scholar 

• Ho, A. D., Reich, J., Nesterko, S., Seaton, D. T., Mullaney, T., Waldo, J., & Chuang, I. (2014). HarvardX and MITx: The first year of open online courses (HarvardX and MITx Working Paper No. 1). Available: https://ssrn.com/abstract=2381263. https://doi.org/10.2139/ssrn.2381263 Accessed 11.01.2019.

• Hone, K. S., & Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers in Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016.

  Article  Google Scholar 

• Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. International Review of Research in Open and Distance Learning, 16(3), 341–358.

  Article  Google Scholar 

• 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.

• 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.

  Chapter  Google Scholar 

• Liyanagunawardena, T. R., Lundqvist, K. Ø., & Williams, S. A. (2015). Who are with us: MOOC learners on a FutureLearn course. British Journal of Educational Technology, 46(3), 557–569. https://doi.org/10.1111/bjet.12261.

  Article  Google 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.

• Macleod, H., Haywood, J., & Woodgate, A. (2015). Emerging patterns in MOOCs: Learners, course designs and directions. TechTrends, 59(1), 56–63.

  Article  Google Scholar 

• Morris, N. P., Hotchkiss, S., & Swinnerton, B. (2015). Can demographic information predict MOOC learner outcomes? Paper presented at EMOOCs 2015, Mons, Belgium.

• 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.

• Perna, L. W., Ruby, A., Boruch, R. F., Wang, N., Scull, J., Ahmad, S., & Evans, C. (2014). Moving through MOOCs: understanding the progression of users in massive open online courses. Educational Researcher, 43(9), 421–432.

  Article  Google Scholar 

• Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., & Velegol, D. (2016). Understanding MOOC students: motivations and behaviours indicative of MOOC completion. Journal of Computer Assisted Learning, 32, 202–217.

  Article  Google Scholar 

• Reeves, T. D., Tawfik, A. A., Msilu, F., & Şimşek, I. (2017). What's in It for Me? Incentives, Learning, and Completion in Massive Open Online Courses. Journal of Research on Technology in Education, 49(3–4), 245–259. https://doi.org/10.1080/15391523.2017.1358680.

  Article  Google Scholar 

• Stich, A. E., & Reeves, T. D. (2017). Massive open online courses and underserved students in the United States. The Internet and Higher Education, 32, 58–71. https://doi.org/10.1016/j.iheduc.2016.09.001.

  Article  Google Scholar 

• van de Oudeweetering, K., & Agirdag, O. (2018). Demographic data of MOOC learners: Can alternative survey deliveries improve current understandings? Computers in Education, 122, 169–178. https://doi.org/10.1016/j.compedu.2018.03.017.

  Article  Google Scholar 

• Veletsianos, G., & Shepherdson, P. (2016). A Systematic Analysis and Synthesis of the Empirical MOOC Literature Published in 2013–2015. International Review of Research in Open and Distributed Learning, 17(2), 1–16.

  Article  Google Scholar 

• Yukselturk, E., & Bulut, S. (2007). Predictors for Student Success in an Online Course. Educational Technology & Society, 10(2), 71–83.

  Google Scholar 

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