Abstract
With the growing number of digital learners, we are facing new challenges for teachers. For instance, the evolution of the level of a student is difficult to appreciate in an e-learning course. Big data analytics offer statistical tools which permit to monitor the level of students. Big data analytics can increase smartness of smart education. In this work, we propose a predictive method to prevent the potential stall of students registered in a MOOC. In the first part, we compare different mathematical regression methods applied to the data available from a SCORM package recorded as a Moodle activity. Next, we describe the implementation of the method that we have chosen and the results we obtain.
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Acknowledgements
This work was supported by ANR (Agence Nationale de la Recherche/Research National Agency) as part of the FR2I project (ANR-15-IDFN-0009-01).
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Gerval, JP., Saumard, M. (2019). Quantitative Follow-Up of MOOC Students Using Big Data Techniques. In: Uskov, V., Howlett, R., Jain, L. (eds) Smart Education and e-Learning 2019. Smart Innovation, Systems and Technologies, vol 144. Springer, Singapore. https://doi.org/10.1007/978-981-13-8260-4_2
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DOI: https://doi.org/10.1007/978-981-13-8260-4_2
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