Abstract
Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of Massive Open Online Courses (MOOCs) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real-world conventional education setting. The educational pedagogy is tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issues in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. First, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Second, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Change history
23 June 2019
In the original version of the book, the following belated corrections are to be incorporated.
References
Onah, D. F. (2017). Investigating self-regulated learning in massive open online courses: a design science research approach (Doctoral dissertation, University of Warwick).
Onah, D. F., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: Behavioural patterns. In EDULEARN14 Proceedings, 5825–5834.
Onah, D. F. O., & Sinclair, J. E. (2017). Assessing self-regulation of learning dimensions in a stand-alone MOOC platform. International Journal of Engineering Pedagogy (iJEP), 7(2), 4–21.
Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Muñoz-Organero, M., & Rodríguez-de-las-Heras, A. (2013, September). Analysing the impact of built-in and external social tools in a MOOC on educational technologies. In European Conference on Technology Enhanced Learning (pp. 5–18). Berlin, Heidelberg: Springer.
Alario‐Hoyos, C., Muñoz‐Merino, P. J., Pérez‐Sanagustín, M., Delgado Kloos, C., & Parada G, H. A. (2016). Who are the top contributors in a MOOC? Relating participants’ performance and contributions. Journal of Computer Assisted Learning, 32(3), 232–243.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13–39).
Onah, D. F. O., Sinclair, J., Boyatt, R., & Foss, J. (2014). Massive open online courses: learner participation. In Proceeding of the 7th International Conference of Education, Research and Innovation (pp. 2348–2356).
Dawson, S., & Siemens, G. (2014). Analytics to literacies: The development of a learning analytics framework for multiliteracies assessment. The International Review of Research in Open and Distributed Learning, 15(4).
Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014, March). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 231–240). ACM.
Reich, J. (2015). Rebooting MOOC research. Science, 347(6217), 34–35.
Clow, D. (2013, April). MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 185–189). ACM.
Knox, J. (2014). From MOOCs to learning analytics: Scratching the surface of the’visual’. ELearn, 2014(11), 3.
Moissa, B., Gasparini, I., & Kemczinski, A. (2015). A systematic mapping on the learning analytics field and its analysis in the massive open online courses context. International Journal of Distance Education Technologies (IJDET), 13(3), 1–24.
Vogelsang, T., & Ruppertz, L. (2015, March). On the validity of peer grading and a cloud teaching assistant system. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 41–50). ACM.
Kloos, C. D., Alario-Hoyos, C., Fernández-Panadero, C., Estévez-Ayres, I., Muñoz-Merino, P. J., Cobos, R., & Chicaiza, J. (2016, September). eMadrid project: MOOCs and learning analytics. In 2016 International Symposium on Computers in Education (SIIE) (pp. 1–5). IEEE.
Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Gascón-Pinedo, J. A., & Kloos, C. D. (2017). Scaling to massiveness with analyse: A learning analytics tool for open edX. IEEE Transactions on Human-Machine Systems, 47(6), 909–914.
Fournier, H., Kop, R., & Sitlia, H. (2011, February). The value of learning analytics to networked learning on a personal learning environment. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 104–109). ACM.
Koller, D., & Ng, A. (2012). The Online Revolution: Education at Scale. L Educ.
Kolowich, S. (2012). Who takes MOOCs. Inside Higher Ed, 5, 2012.
Vries, P. (2013). Online learning and higher engineering education the MOOC phenomenon’, European Society for Engineering Education (SEFI), [Brussels]. In Paper Presented at the 41st SEFI Conference.
Acknowledgements
The first author wishes to acknowledge Mr. Adakole S. Onah’s financial support in his research and family members and friends for their moral support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Onah, D.F.O., Pang, E.L.L., Sinclair, J.E., Uhomoibhi, J. (2019). Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources. In: Auer, M., Tsiatsos, T. (eds) Mobile Technologies and Applications for the Internet of Things. IMCL 2018. Advances in Intelligent Systems and Computing, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-11434-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-11434-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11433-6
Online ISBN: 978-3-030-11434-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)