Abstract
The emergence of MOOCs (Massive Open Online Courses) makes available big amounts of data about students’ interaction with online educational platforms. This allows for the possibility of making predictions about future learning outcomes of students based on these interactions. The prediction of certificate accomplishment can enable the early detection of students at risk, in order to perform interventions before it is too late. This study applies different machine learning techniques to predict which students are going to get a certificate during different timeframes. The purpose is to be able to analyze how the quality metrics change when the models have more data available. From the four machine learning techniques applied finally we choose a boosted trees model which provides stability in the prediction over the weeks with good quality metrics. We determine the variables that are most important for the prediction and how they change during the weeks of the course.
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Acknowledgments
Work partially funded by the Madrid Regional Government with grant No. S2013/ICE-2715, the Spanish Ministry of Economy and Competitiveness projects RESET (TIN2014-53199-C3-1-R) and Flexor (TIN2014-52129-R) and the European Erasmus+ projects MOOC Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP) and SHEILA (562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD). This research work was made possible thanks to Universidad Autónoma de Madrid, which provided us with the dataset, and to Prof. Pedro García, who was the instructor of the selected MOOC.
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Ruipérez-Valiente, J.A., Cobos, R., Muñoz-Merino, P.J., Andujar, Á., Delgado Kloos, C. (2017). Early Prediction and Variable Importance of Certificate Accomplishment in a MOOC. In: Delgado Kloos, C., Jermann, P., Pérez-Sanagustín, M., Seaton, D., White, S. (eds) Digital Education: Out to the World and Back to the Campus. EMOOCs 2017. Lecture Notes in Computer Science(), vol 10254. Springer, Cham. https://doi.org/10.1007/978-3-319-59044-8_31
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