@article {ElAissaoui2020655,
	title = {Mining Learners{\textquoteright} Behaviors: An Approach Based on Educational Data Mining Techniques},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1076},
	year = {2020},
	note = {cited By 2},
	pages = {655-670},
	abstract = {Educational data mining is a research field that aims to apply data mining techniques in educational environments. Many data mining techniques such as clustering, classification, and prediction can be performed on educational data in order to analyze the learner behaviors. In this work, we have used the clustering and classification techniques to predict the learners{\textquoteright} learning styles. The students{\textquoteright} behaviors while using the e-learning system have been captured from the log file and given as an input of a clustering algorithm to group them into 16 clusters. The resulted clusters were labeled with learning styles combinations based on the Felder and Silverman learning style model. Then the labeled behaviors were given as input to four classifiers: naive Bayes, Cart, Id3, and C4.5 to compare their performance in predicting students{\textquoteright} learning styles. The four classifiers were performed using Weka data mining tool, and the obtained results showed that Id3 yielded better results than the other classifiers. {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.},
	keywords = {Artificial intelligence, Classification technique, Clustering algorithms, Data mining, Data-mining tools, Educational data mining, Educational environment, Embedded systems, Forecasting, Learning Style, Learning systems, Log file, Naive bayes, Research fields, Students},
	doi = {10.1007/978-981-15-0947-6_62},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085173246\&doi=10.1007\%2f978-981-15-0947-6_62\&partnerID=40\&md5=0cb9665b2720bd9b06e011902e8708b5},
	author = {El Aissaoui, O. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
