Abstract
This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this drop-out. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider OpenClassrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. The performed experimentations cover three main axes; (1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners’ engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. The findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
You can access the selected parameters for each model after hyper-parameter tuning by consulting the following link: http://www.laurent-brisson.fr/publication/2018-understanding-learner-dropout-mooc/.
References
Belanger, Y., Thornton, J.: Bioelectricity: a quantitative approach duke university’s first MOOC. Technical report (2013)
Biggs, J.B.: Teaching for Quality Learning at University: What the Student Does. McGraw-Hill Education (UK), London (2011)
Colman, D.: MOOC interrupted: top 10 reasons our readers didn’t finish a massive open online course. Open Culture (2013)
Devlin, K.: MOOCs and the myths of dropout rates and certification. Huff Post College (2013). Accessed 2 March 2013
Emanuel, E.J.: Online education: MOOCs taken by educated few. Nature 503(7476), 342–342 (2013)
Hlosta, M., Zdrahal, Z., Zendulka, J.: Ouroboros: early identification of at-risk students without models based on legacy data. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 6–15. ACM (2017)
Jayaprakash, S.M., Moody, E.W., Lauría, E.J., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1(1), 6–47 (2014)
Khalil, H., Ebner, M.: MOOCs completion rates and possible methods to improve retention - a literature review. In: Viteli, J., Leikomaa, M. (eds.) Proceedings of EdMedia + Innovate Learning 2014, Tampere, Finland, pp. 1305–1313. Association for the Advancement of Computing in Education (AACE), June 2014
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 60–65 (2014)
Lackner, E., Ebner, M., Khalil, M.: MOOCs as granular systems: design patterns to foster participant activity (2015). Accessed 10 September 2015
Onah, D.F., Sinclair, J., Boyatt, R.: Dropout rates of massive open online courses: behavioural patterns. In: EDULEARN 2014 Proceedings, pp. 5825–5834 (2014)
Ramesh, A., Goldwasser, D., Huang, B., Daumé III, H., Getoor, L.: Learning latent engagement patterns of students in online courses. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1272–1278. AAAI Press (2014)
Rijsbergen, C.J.V.: Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton (1979)
Rivard, R.: Measuring the MOOC dropout rate. Inside High. Ed. 8, 2013 (2013)
Tabaa, Y., Medouri, A.: LASyM: a learning analytics system for MOOCs. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 4(5) (2013)
Wen, M., Yang, D., Rose, C.: Sentiment analysis in MOOC discussion forums: what does it tell us? In: Educational Data Mining 2014 (2014)
Wen, M., Yang, D., Rosé, C.P.: Linguistic reflections of student engagement in massive open online courses. In: ICWSM (2014)
Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., Tingley, D.: Delving deeper into MOOC student dropout prediction. arXiv preprint arXiv:1702.06404 (2017)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Itani, A., Brisson, L., Garlatti, S. (2018). Understanding Learner’s Drop-Out in MOOCs. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-03493-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03492-4
Online ISBN: 978-3-030-03493-1
eBook Packages: Computer ScienceComputer Science (R0)