@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.}
}
@article {ElAissaoui20209,
	title = {A Multiple Linear Regression-Based Approach to Predict Student Performance},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1102 AISC},
	year = {2020},
	note = {cited By 15},
	pages = {9-23},
	abstract = {Predicting students{\textquoteright} academic outcome is useful for any educational institution that aims to ameliorate students{\textquoteright} performance. Based on the resulted predictions, educators can provide support to students at risk of failure. Data mining and machine learning techniques were widely used to predict students{\textquoteright} performance. This process called Educational data mining. In this work, we have proposed a methodology to build a student{\textquoteright} performance prediction model using a supervised machine learning technique which is the multiple linear regression (MLR). Our methodology consists of three major steps, the first step aims to analyze and preprocess the students{\textquoteright} attributes/variables using a set of statistical analysis methods, and then the second step consists in selecting the most important variables using different methods. The third step aims to construct different MLR models based on the selected variables and compare their performance using the k-fold cross-validation technique. The obtained results show that the model built using the variables selected from the Multivariate Adaptive Regression Splines method (MARS), outperforms the other constructed models. {\textcopyright} Springer Nature Switzerland AG 2020.},
	keywords = {Data mining, Education computing, Educational data mining, Forecasting, Intelligent systems, K fold cross validations, Learning systems, Linear regression, Machine learning techniques, Multiple linear regressions, Multivariate adaptive regression splines, Performance prediction models, planning, Predictive analytics, Statistical analysis methods, Students, Supervised learning, Supervised machine learning, Sustainable development},
	doi = {10.1007/978-3-030-36653-7_2},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078413089\&doi=10.1007\%2f978-3-030-36653-7_2\&partnerID=40\&md5=893772562739d5d20e6b6ae21ebae325},
	author = {El Aissaoui, O. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
@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.}
}
@conference {Aissaoui201987,
	title = {Combining supervised and unsupervised machine learning algorithms to predict the learners{\textquoteright} learning styles},
	booktitle = {Procedia Computer Science},
	volume = {148},
	year = {2019},
	note = {cited By 0},
	pages = {87-96},
	doi = {10.1016/j.procs.2019.01.012},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062675875\&doi=10.1016\%2fj.procs.2019.01.012\&partnerID=40\&md5=bac9366951ce3839b307d3270ef38ae2},
	author = {Aissaoui, O.E. and El Madani, Y.E.A. and Oughdir, L. and Allioui, Y.E.}
}
