Abstract
E-learning systems have undergone a mutation thanks to the integration of several technologies, especially those of artificial intelligence, in order to improve their efficiency. The concept of adaptive learning has been the subject of this evolution. Several researches focus on the suggestion of new approaches ensuring a better method of presentation of the learning content according to the characteristics of each learner. However, the learning process is not limited to the adaptive content presentation phase.
For this purpose, this work proposes a hybrid approach based on recommendation systems and machine learning to ensure an adaptive remediation according to the deficiencies identified during the evaluation phase. In order to implement the proposed approach, we have adopted a new method based primarily on the classification of the identified errors using the Naïve Bayes algorithm. Each class that represents a learning difficulty has a remediation strategy planned, that will be used to recommend the most adaptive remediation activity through the collaborative filtering technique.
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Lhafra, F.Z., Abdoun, O. (2022). Hybrid Approach to Recommending Adaptive Remediation Activities Based on Assessment Results in an E-learning System Using Machine Learning. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1417. Springer, Cham. https://doi.org/10.1007/978-3-030-90633-7_57
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