Survival and Risk Analysis in MOOCS

Year 2019, Volume: 20 Issue: 4, 149 - 159, 01.10.2019

Manuel Medina Labrador Gustavo R. Garcia Vargas Jorge Alvarado Mario Caıcedo

https://doi.org/10.17718/tojde.640561

Cited By: 5

Abstract

The most significant disadvantage in the use of MOOCs is their high drop out rates, which reaches between 88.3% and 90%. This research examined the desertion from 15 MOOCs, the following factors and their relation to desertion were assessed, characteristics of the students (gender, language, educational level, age, interest in the final certificate) and aspects of the courses (data lost at the time of registration, duration of the MOOC, course discipline, number of total questions and by evaluation). The results, based on survival analysis, indicate the probability of desertion is the highest on the first and last weeks of the MOOC. Our findings show that the probability of dropout is affected by the number of questions in each evaluation, the educational level of the participants, their age and their interest in the certificate. Missing data in the registration form was found to be associated with desertion being a significant pre-dictor of risk of quitting. Finally, the lack of interest in the certificate was found to be a predictor of the highest risk of dropping out. Possible strategies to reduce the desertion behavior at MOOCs are discussed.

Keywords

MOOC, desertion, survival analysis, risk analysis

References

• Adamopoulos, P. (2013, Diciembre ). What Makes a Great MOOC? An Interdisciplinary Analysis of Student Retention in Online Courses. Paper presented at the Thirty Fourth Interna-tional Conference on Information Systems, 1–21.Milan, Italy. Angelino, L. M., Williams, F. K., & Natvig, D. (2007). Strategies to engage online students and reduce attrition rates. Journal of Educators Online, 4(2), 1-14. Recuperado de https://files.eric.ed.gov/ fulltext/EJ907749.pdf Allison, P. D. (2014). Event history and survival analysis: Regression for longitudinal event data (Vol.46). London: SAGE publications. Berge, Z. L., & Huang, Y. P. (2004). 13: 5 A Model for Sustainable Student Retention: A Holistic Perspective on the Student Dropout Problem with Special Attention to e-Learning. Doesnews (online), 13(5). Recuperado de www. re-searchgate.net/profile/Zane_Berge/publication/237429805. Carey, K. (2012). Into the future with MOOCs. The Chronicle of Higher Education, 59(2),29. Re-cuperado de https://www.chronicle.com/article/Into-the-Future-With-MOOCs/134080 Chang, J. W., & Wei, H. Y. (2016). Exploring Engaging Gamification Mechanics in Mas-sive Online Open Courses. Educational Technology & Society, 19 (2), 177–203. Retrieved from [J]2015_Exploring%20 Engaging%20Gamification%20Mechanics%20in%20Massive%20Online%20Open%20Course. pdf Chiappe-Laverde, A., Hine, N., & Martinez-Silva, J. A. (2015). Literatura y practica: una revision critica acerca de los MOOC. Comunicar, 22(44). doi 10.3916/C44-2015-01 Ferschke, O., Yang, D., Tomar, G., & Rose, C. P. (2015, June). Positive impact of collaborative chat participation in an edX MOOC. Trabajo presentado en International Conference on Artificial Intelligence in Education, 115-124. doi: 10.1007/978-3-319-19773-9_12 158 Greene, JA, Oswald, CA, y Pomerantz, J. (2015). Predictors of Retention and Achievement in a Massive Open Online Course. American Educational Research Diario , 52, 925-955. doi: 10.3102/0002831215584621 Gomez-ZermeNo, M., & Aleman De la Garza, L. (2016). Research Analysis On Mooc Course Dropout And Retention Rates. Turkish Online Journal of Distance Education-TOJDE . 17(2), 3-11. doi: 10.17718/tojde.23429 Gomez, G., & Langohr, K. (2004). Hablemos de… Analisis de supervivencia. GH continuada, 3(4), 51-57. Gütl, C., Chang, V., Hernandez Rizzardini, R., & Morales, M. (2014). Must we be concerned with the Massive Drop-outs in MOOC? - An Attrition Analysis of Open Courses.(561533-EPP-1-2015-1- ES-EPPKA2-CBHE-JP). En memorias del International Conference of In-teractive Collaborative Learning, ICL2014. Recuperado de http://www.mooc-maker.org/wp- content/files/WPD1.6_ INGLES.pdf Gütl, C., Rizzardini, R. H., Chang, V., & Morales, M. (2014). Attrition in MOOC: Lessons Learned from Drop-Out Students. In Learning Technology for Education in Cloud-MOOC and Big Data: Third International Workshop (Vol. 446, pp. 37–48). doi:10.1007/978-3- 319-10671-7_4 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 Harrell Jr, F. E. (2015). Regression modeling strategies: with applications to linear models, lo-gistic and ordinal regression, and survival analysis.Biometrics, 72, 1006-1009 doi: 10.1007/978-3-319- 19425-7 Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses Massive Open Online Courses. International Review of Research in Open and Distance Learning, 15(1), 133- 160. doi: 10.19173/irrodl.v15i1.1651 Lacny, S., Wilson, T., Clement, F., Roberts, D. J., Faris, P., Ghali, W. A., & Marshall, D. A. (2017).KaplanMeier Survival Analysis Overestimates Cumulative Incidence of Health-related Events in the Presence of Competing Risks: A Meta-analysis. Journal of Clinical Epidemiology, 93, 25-35. doi: 10.1016/j.jclinepi.2017.10.006 Lewin, T. (20 de febrero de 2013). Universities abroad join partnerships on the web. The New York Times. Recuperado de https://www.nytimes.com/2013/02/21/education/universities-abroad-join- mooccourse-projects.html Loewenstein, G., Leslie, J., & Volpp, K. (2010). Aprender de los errores de los consumidores para ayudarles a tomar mejores decisiones. Harvard Deusto business review, 190, 31-38. Recuperado de https:// www.harvard-deusto.com/aprender-de-los-errores-de-los-consumidores-para-ayudarles-a-tomarmejores-decisiones Mabuan, R. (2018). Confessions of a MOOCER: an autoethnographic inquiry on online distance education. Turkish Online Journal of Distance Education-TOJDE. 19(4), 198-213. doi: 10.17718/ tojde.471916 Martinez-Gonlez, M.A., Sanchez-Villegas, A. y Basterna-Gortari F.J. (2014). Introduccion al analisis de supervivencia. En M. A. Martinez-Gonzalez, y F. J. Faulin (Eds). Bioestadistica amigable. EspaNa: Ediciones Diaz de Santos. Rose, C. P., Carlson, R., Yang, D., Wen, M., Resnick, L., Goldman, P., & Sherer, J. (2014, March).Social factors that contribute to attrition in MOOCs. Poster presentado en the first ACM conference on Learning@ scale conference (pp. 197-198). doi: 10.1145/2556325.2567879 Rubio, E., & Martinez, T. (2016). Manual de analisis de supervivencia: curvas de supervivencia y regresion de Cox (Vol. 261). Zaragoza, EspaNa: Prensas de la Universidad de Zarago-za. Sanchez-Gordon, S., Calle-Jimenez, T., & Lujan-Mora, S. (2015, June). Relevance of MOOCs for training of public sector employees. Memorias de congreso Information Technology Based Higher Education and Training (ITHET), 1-5. doi: 10.1109/ITHET.2015.7218016 159 Sanchez-Gordon, S., & Lujan-Mora, S. (2016). Barreras y estrategias de utilizacion de los MOOC. En HP Gomez, G.B. Alba y M.L. Carlos (Eds). La cultura de los MOOC para la in-novacion en educacion superior desde contextos iberoamericanos, (pp.141-160). Ma-drid, EspaNa: Editorial Sintesis. Stoolmiller, M. (2016). An introduction to using multivariate multilevel survival analysis to study coercive family process. En T.J. Dishion y J. Snyder (Eds). The Oxford handbook of co-ercive relationship dynamics. doi:10.1093/oxfordhb/9780199324552.013.27 Yang, D., Wen, M., Howley, I., Kraut, R., & Rose, C. (2015, marzo). Explorar el efecto de confu-sion en los foros de discusion de mooc. In Proceedings of the second (2015) ACM con-ference on learning@ scale (pp. 121-130). doi: 10.1145/2724660.272467