Analyzing Student Behavior in Moodle System

Educational data mining is an interesting discipline that focuses on developing methods to extract knowledge and discover patterns from online learning systems.This work is an application of data mining in learning management systems.Our objective is to introduce educational data mining by describing a step-by-step process using a variety of techniques such as Attribute Weighting, Classification, Clustering, and Association Rules to achieve the goal of discovering useful knowledge from Moodle.For association rules, we will present a comparison between two data mining algorithms, Apriori and FP-Growth, to justify our choice of the FP-Growth algorithm.Analyzing mining results enables teachers to better allocate resources and understand student behavior.