@article {Aissaoui2020117,
	title = {Toward a hybrid machine learning approach for extracting and clustering learners{\textquoteright} behaviours in adaptive educational system},
	journal = {International Journal of Computing Science and Mathematics},
	volume = {12},
	number = {2},
	year = {2020},
	note = {cited By 0},
	pages = {117-131},
	abstract = {The learning style is a vital learner{\textquoteright}s characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners{\textquoteright} sequences. The captured learners{\textquoteright} sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system{\textquoteright}s log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results. Copyright {\textcopyright} 2020 Inderscience Enterprises Ltd.},
	keywords = {Adaptive systems, Classification (of information), Classifiers, Computer aided instruction, Confusion matrices, e-learning, E-learning environment, Educational systems, Hybrid machine learning, K-means clustering, Learning materials, Learning Style, Machine learning, Naive Bayes classifiers, Preprocess},
	doi = {10.1504/IJCSM.2020.111113},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096034864\&doi=10.1504\%2fIJCSM.2020.111113\&partnerID=40\&md5=e39786511c32be21cc0fef5be68a0822},
	author = {Aissaoui, O.E. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
