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Early Prediction and Variable Importance of Certificate Accomplishment in a MOOC

Ruipérez-Valiente, José A. and Cobos, Ruth and Muñoz-Merino, Pedro J. and Andújar, Álvaro and Delgado Kloos, Carlos (2017) Early Prediction and Variable Importance of Certificate Accomplishment in a MOOC. In: European Conference on Massive Open Online Courses (EMOOCs 2017), 22-26 May 2017, Madrid, Spain.

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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 w hen 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.

Item Type: Conference or Workshop Papers (Paper)
Uncontrolled Keywords: Educational Data Mining; learning analytics; prediction; machine learning; MOOCs.
Subjects: UNSPECIFIED
Divisions: UNSPECIFIED
Depositing User: Jose Antonio Ruiperez
Date Deposited: 02 Feb 2018 15:39
Last Modified: 02 Feb 2018 15:39
URI: http://eprints.networks.imdea.org/id/eprint/1759

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