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Adaptation and individualization of learning is a major challenge when using flipped class as a teaching method. In this paper, we propose a recommendation system for flipped classroom to individualize learning in the classroom based on Data Mining algorithms. This system allows the teacher to predict a classification of learners before administering the tasks to be accomplished and the adapted teaching resources, using attributes related to the activity logs on the e-learning platform, to the online evaluations (Quiz) and to demographic data. The results show that the use of this model as a learning strategy optimizes the time of learning and improves the learner's performance.

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