“…In 2014 we found papers in which data mining techniques were used, such as decision trees, clustering, association rules, neural networks and classification to address educational situations. The first one is "Data Mining: A prediction for Student's Performance Using Classification Method" (Badr, Din, & Elaraby, 2014) work developed by Ahmed A. and Elaraby I. where decision trees are used to predict the final students grade; on "Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques" (Chalaris, Gritzalis, Maragoudakis, & Sgouropoulou, 2014) Chalaris M., Gritzalis S. and Maragoudakis M. use association rules to provide knowledge related to educational institutions processes; additionally, Belsis P., Chalaris I., Chalaris M., Skourlas C. and Tsolakidis A. published in "The Analysis of the Length of Studies in Higher Education based on Clustering and the Extraction of Association Rules" (Belsis, Chalaris, Chalaris, & Skourlas, 2014) how, from clustering and association rules extraction can analyze the study lenght in higher education; in the same year, Guruler H. and Istanbullu A. published "Modeling Student Performance in Higher Education Using Data Mining" (Mugla, 2014) where employ decision trees to identify factors that impact the success of students in higher education; also found a job Rabbany R.,Elatia S., Takaffoli M. and Zaiane O. entitled "Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective" (Rabbany, Elatia, Takaffoli, & Zaïane, 2014) where the clustering technique is used to analyze the social networks usage(ARS) and student interaction in forums; additionally, Yukselturk E., Ozekes S. and Türel Y. publish "Predicting dropout student an application of data mining methods in an online education program" (Yukselturk & Education, 2014) a writing where through the use of decision trees, neural networks and classification, examine the students dropout in online programs; in the same year, Chen X., Vorvoreanu M. and Madhavan K. published " Mining Social Media Data for Understanding Students' Learning Experiences" (X. Chen, Member, Vorvoreanu, & Madhavan, 2014a) in this paper with the use of classification technique analyze the students learning experience based on the information discussed in social networks; and finally, Hu Y., Lo C. and Shih S. in the paper entitled "Developing early warning systems to predict students' online learning performance" (Hu, Lo, & Shih, 2014) used classification and regression trees to create an early warning system of students performance in the LMS.…”