A predictive model for the identification of learning styles in MOOC environments

  • Brahim HmednaEmail author
  • Ali El Mezouary
  • Omar Baz


Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and big data analysis have created new opportunities for a better understanding of how learners behave and learn in environments known for their massiveness and openness. The work is about predicting learners’ learning styles based on their learning traces. The Felder Silverman learning style model (FSLSM) is adopted since it is one of the most commonly used models in technology-enhanced learning. In order to attend our objective, we analyzed data collected from the edX course “statistical learning” (session Winter 2015 and Winter 2016), administered via Stanford’s Logunita platform. The results show that decision tree performs best for all 3 dimensions, with an accuracy of higher than 98% and a reduced risk of overfitting the training data.


MOOC Learning styles FSLSM Machine learning 



The authors are grateful to CAROL (the center for advanced research through online learning), university of Stanford, for providing the Dataset necessary for accomplishing this research.


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Authors and Affiliations

  1. 1.IRF-SIC Laboratory Ibn Zohr University Agadir MoroccoAgadirMorocco

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