Exploring predictors of success in Massive Open Online Courses (MOOC)
DOI:
https://doi.org/10.5944/ried.28.1.40195Keywords:
Massive Open Online Courses, social and emotional learning, stress, satisfaction, MOOCs, predictor variablesAbstract
Massive Open Online Courses (MOOC) play an important role in educational equity and lifelong learning, offering accessible education free from barriers such as time constraints or geographical limitations. Consequently, the number of MOOC enrollments is high, as is the rate at which students withdraw from the course. Indeed, the typical completion rate is less than 10%, underscoring the necessity to ascertain the factors that precipitate early withdrawal. The present research aims to determine the extent to which social and emotional competencies, perceived stress, expectations, and satisfaction predict MOOC completion. An ex post facto methodological design was employed, in which 416 students completed the Social and Emotional Learning Scale, the Sociodemographic Data Questionnaire, the Expectations Questionnaire, the Perceived Stress Scale, and the Satisfaction Questionnaire. Additionally, data were gathered on the successful completion of the MOOC for each participant. Subsequently, five models were constructed using binomial logistic regression analysis. While satisfaction was identified as the most robust predictor of course completion, social and emotional competencies, perceived stress, and expectations also demonstrated significant results. This study represents the only research to date that has explored the predictive ability of these variables, offering a novel perspective on predictors of MOOC success.
Downloads
References
Alario-Hoyos, C., Estévez-Ayres, I., Pérez-Sanagustín, M., Delgado Kloos, C. y Fernández-Panadero, C. (2017). Understanding learners’ motivation and learning strategies in MOOCs. The International Review of Research in Open and Distributed Learning, 18(3). https://doi.org/10.19173/irrodl.v18i3.2996
Albelbisi, N. A., Al-Adwan, A. S. y Habibi, A. (2021). Self-regulated learning and satisfaction: A key determinants of MOOC success. Education and Information Technologies, 26(3), 3459-3481. https://doi.org/10.1007/s10639-020-10404-z
Aldowah, H., Al-Samarraie, H., Alzahrani, A. I. y Alalwan, N. (2019). Factors affecting student dropout in MOOCs: a cause and effect decision‐making model. Journal of Computing in Higher Education, 32(2), 429-454. https://doi.org/10.1007/s12528-019-09241-y
Alonso-Mencía, M. E., Alario-Hoyos, C., Estévez-Ayres, I. y Delgado-Kloos, C. (2021). Analysing self-regulated learning strategies of MOOC learners through self-reported data. Australasian Journal of Educational Technology, 56-70. https://doi.org/10.14742/ajet.6150
Alonso-Mencía, M. E., Alario-Hoyos, C., Maldonado-Mahauad, J., Estévez-Ayres, I., Pérez-Sanagustín, M. y Delgado Kloos, C. (2019). Self-regulated learning in MOOCs: lessons learned from a literature review. Educational Review, 72(3), 319-345. https://doi.org/10.1080/00131911.2019.1566208
Ato, M., López, J. J. y Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en Psicología. Anales de Psicología, 29(3), 1038-1059. https://doi.org/10.6018/analesps.29.3.178511
Castaño-Muñoz, J., Kalz, M., Kreijns, K. y Punie, Y. (2018). Who is taking MOOCs for teachers’ professional development on the use of ICT? A cross-sectional study from Spain. Technology, Pedagogy and Education, 27(5), 607-624. https://doi.org/10.1080/1475939X.2018.1528997
Castrillo, M. D. y Sedano, B. (2021). Joining forces toward social inclusion. CALICO Journal, 38(1), 79-102. https://doi.org/10.1558/cj.40900
Chaves-Montero, A., Corchuelo-Fernández, C., Cejudo-Cortés, C. M. A. y Gadea-Aiello, W. F. (2020). MOOC como disrupción educativa. Propuestas de mejora a partir del análisis de una web. Innovación Educativa, 30, 127-145. https://doi.org/10.15304/ie.30.6467
Crane, R. A. y Comley, S. (2020). Influence of social learning on the completion rate of massive online open courses. Education and Information Technologies, 26(2), 2285-2293. https://doi.org/10.1007/s10639-020-10362-6
Dalipi, F., Imran, A. S. y Kastrati, Z. (2018). MOOC dropout prediction using machine learning techniques: Review and research challenges. 2018 IEEE Global Engineering Education Conference (EDUCON). https://doi.org/10.1109/EDUCON.2018.8363340
Fernández, F. D., Flores, L. y Arco, J. L. (2022). Coping strategies among undergraduates: Spanish adaptation and validation of the Brief-COPE Inventory. Psychology Research and Behavior Management, 15, 991-1003. https://doi.org/10.2147/PRBM.S356288
Fernández, F. D., Moreno, A. J., Marín, J. A. y Romero, J. M. (2022). Adolescents’ emotions in Spanish education: Development and validation of the social and emotional learning scale. Sustainability, 14(7), 3755. https://doi.org/10.3390/su14073755
Fincham, E., Whitelock-Wainwright, A., Kovanović, V., Joksimović, S., van Staalduinen, J.-P. y Gašević, D. (2019). Counting clicks is not enough. Proceedings of the 9th International Conference on Learning Analytics & Knowledge. https://doi.org/10.1145/3303772.3303775
Galikyan, I., Admiraal, W. y Kester, L. (2021). MOOC discussion forums: The interplay of the cognitive and the social. Computers & Education, 165, 104133. https://doi.org/10.1016/j.compedu.2021.104133
Hone, K. S. y El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157-168. https://doi.org/10.1016/j.compedu.2016.03.016
Huang, H., Jew, L. y Qi, D. (2023). Take a MOOC and then drop: A systematic review of MOOC engagement pattern and dropout factor. Heliyon, 9(4), e15220. https://doi.org/10.1016/j.heliyon.2023.e15220
Huett, J. B., Moller, L., Young, J., Bray, M. y Huett, K. C. (2008). Supporting the distant student: the effect of arcs-based strategies on confidence and performance. Quarterly Review of Distance Education, 9(2), 113-126. https://www.proquest.com/scholarly-journals/supporting-distant-student-effect-arcs-based/docview/231182900/se-2?accountid=14542
Jiménez-Álvarez, L. S., Ortiz, C., Maldonado, J. C., Capa-Mora, E. D., Fierro-Jaramillo, N. D. C. y Quichimbo-Miguitama, P. G. (2018). Aprendizaje introductorio sobre la ciencia del suelo a través de un curso MOOC. Ciencia y Tecnología Agropecuaria, 19(3). https://doi.org/10.21930/rcta.vol19_num3_art:649
Kalton, G. (2020). Introduction to survey sampling. Sage Publications. https://doi.org/10.4135/9781071909812.n5
Kovanović, V., Joksimović, S., Gašević, D., Owers, J., Scott, A.-M. y Woodgate, A. (2016). Profiling MOOC course returners. Proceedings of the Third (2016) ACM Conference on Learning@ Scale. https://doi.org/10.1145/2876034.2893431
Li, K. y Moore, D. R. (2018). Motivating students in Massive Open Online Courses (MOOCs) using the Attention, Relevance, Confidence, Satisfaction (ARCS) model. Journal of Formative Design in Learning, 2(2), 102-113. https://doi.org/10.1007/s41686-018-0021-9
Liu, S., Liu, S., Liu, Z., Peng, X. y Yang, Z. (2022). Automated detection of emotional and cognitive engagement in MOOC discussions to predict learning achievement. Computers & Education, 181, 104461. https://doi.org/10.1016/j.compedu.2022.104461
McAuley, A., Stewart, B., Siemens, G. y Cormier, D. (2010). The MOOC model for digital practice. University of Prince Edward Island.
Min, L. y Jingyan, L. (2017). Assessing the effectiveness of self-regulated learning in MOOCs using macro-level behavioural sequence data. En CEUR Workshop Proceedings, 1-9. https://ceur-ws.org/Vol-1841/E01_26.pdf
Mulik, S., Srivastava, M. y Yajnik, N. (2020). Flow experience and MOOC acceptance: mediating role of MOOC satisfaction. NMIMS Management Review, 28(1), 52-68. https://management-review.nmims.edu/wp-content/uploads/2020/01/MR-1-52-68.pdf
Narayanasamy, S. K. y Elçi, A. (2020). An effective prediction model for online course dropout rate. International Journal of Distance Education Technologies, 18(4), 94-110. https://doi.org/10.4018/IJDET.2020100106
Ogunyemi, A. A., Quaicoe, J. S. y Bauters, M. (2022). Indicators for enhancing learners’ engagement in massive open online courses: A systematic review. Computers and Education Open, 3, 100088. https://doi.org/10.1016/j.caeo.2022.100088
Pappano, L. (2012). The year of the MOOC. The New York Times. https://www.nytimes.com/2012/11/04/education/edlife/massive-open-online-courses-are-multiplying-at-a-rapid-pace.html
Reich, J. y Ruipérez-Valiente, J. A. (2019). The MOOC pivot. Science Education, 363(6423), 130-131. https://doi.org/10.1126/science.aav7958
Sarabia, C. M. (2016). Nuevas culturas educativas: los MOOC en las universidades españolas. Cultura y Educación, 28(1), 196-212. https://doi.org/10.1080/11356405.2015.1120451
Soper, D. S. (2024). A-priori sample size calculator for regression. [Software] https://www.danielsoper.com/statcalc/calculator.aspx?id=1
The Jamovi Project. (2022). jamovi. (Version 2.3) [Computer Software]. https://www.jamovi.org.
Trujillo, H. M. y González-Cabrera, J. (2007). Propiedades psicométricas de la versión española de la “Escala de estrés percibido” (EEP). Psicología Conductual, 15(3), 457-477. https://www.researchgate.net/publication/281744012
World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191-2194. https://doi.org/10.1001/jama.2013.281053
Xu, B., Chen, N.-S. y Chen, G. (2020). Effects of teacher role on student engagement in WeChat-Based online discussion learning. Computers & Education, 157, 103956. https://doi.org/10.1016/j.compedu.2020.103956
Yilmaz, Y., Sarikaya, O., Senol, Y., Baykan, Z., Karaca, O., Demiral Yilmaz, N., Altintas, L., Onan, A. y Sayek, İ. (2021). RE-AIMing COVID-19 online learning for medical students: a massive open online course evaluation. BMC Medical Education, 21(1). https://doi.org/10.1186/s12909-021-02751-3
Zhu, M., Sari, A. R. y Lee, M. M. (2020). A comprehensive systematic review of MOOC research: Research techniques, topics, and trends from 2009 to 2019. Educational Technology Research and Development, 68(4), 1685-1710. https://doi.org/10.1007/s11423-020-09798-x
