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Hybrid approach of the fuzzy C-Means and the K-Nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized Follow-up of learners in real time
Author(s) -
El Ghouch Nihad,
El Mokhtar En-Naimi,
Abdelhamid Zouhair,
Al Achhab Mohammed
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i6.pp4939-4950
Subject(s) - computer science , artificial intelligence , machine learning , process (computing) , fuzzy logic , adaptive learning , unsupervised learning , instance based learning , active learning (machine learning) , operating system
The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors.

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