Running summarizing tools on a new article

Abstract: Educational data has grown over the years with the increased use of technology within educational environments. This has led to a huge amount of data being stored in various data sources representing the student, his/her activities, and other aspects relevant to the learning process. To meet this analytical need, Educational Data Mining (EDM) has emerged to assist educational institutions in identifying key benefits such as students at risk, the level of student engagement or predicting student performance. The aim of this research was to explore the various aspects of student interaction data using data mining techniques to identify relevant patterns of behaviors and possible key attributes that have higher degrees of influence on distance learning students. The main findings identified several patterns of user online profiles based on a set of adopted learning strategies, the research proposed a framework for analyzing such interaction data based on R and Hadoop platforms to correlate online profiles with student performance.