Abstract
Massive Open Online Courses (MOOCs) have become an important online learning tool for educators and learners, but one of the major issues are the high drop-out rates. Recent research suggests not only to identify and support learners at-risk to drop-out but also to differentiate between the group of healthy attrition (intentionally leaving the MOOC) and unhealthy attrition (struggling to complete the MOOC). In this paper, we focus on two research questions: Firstly, can we already identify learners at-risk to drop-out a MOOC in an early stage? Secondly, can we differentiate between the group of healthy attrition and unhealthy attrition? Experimentation with Support Vector Machines based on learners logs from eleven MOOCs on the Telescope platform show first promising results.
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Acknowledgment
This work is partially supported by European Union through the project MOOC-Maker www.moocmaker.org. Reference: 561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP.
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Vitiello, M., Gütl, C., Amado-Salvatierra, H.R., Hernández, R. (2017). MOOC Learner Behaviour: Attrition and Retention Analysis and Prediction Based on 11 Courses on the TELESCOPE Platform. In: Uden, L., Liberona, D., Liu, Y. (eds) Learning Technology for Education Challenges. LTEC 2017. Communications in Computer and Information Science, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-62743-4_9
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