Abstract
Personalized learning is a teaching method that allows the content and
course of online training to be adapted according to the individual profile of learners.
The main task of adaptability is the selection of the most appropriate content for the
student in accordance with his digital footprint. In this work, we build a machine
learning model to recommend the appropriate learning resources according to the
student profile. To this end, we use Sequential forward selection (SFS) as a feature
selection technique with AdaBoost as a classifier. The obtained results prove the
efficiency of the proposed model with 91.33% of accuracy rate and 91.43% of
precision rate.
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