Educational Data Mining Techniques for Detecting Undesirable Students’ Behaviors and Predicting Students’ Performance: A Comparative Study

Author(s):  
Imane El Mourabit ◽  
Said Jai-Andaloussi ◽  
Noreddine Abghour
2012 ◽  
Vol 38 (2) ◽  
pp. 375-397 ◽  
Author(s):  
Karel Dejaeger ◽  
Wouter Verbeke ◽  
David Martens ◽  
Bart Baesens

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.


Author(s):  
Ouafae El Aissaoui ◽  
Yasser El Alami El Madani ◽  
Lahcen Oughdir ◽  
Ahmed Dakkak ◽  
Youssouf El Allioui

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