Analyzing E-Learning Systems Using Educational Data Mining Techniques

Abstract

Recently, Educational Data Mining has become an emerging research field used to extract knowledge and discover patterns
from E-learning systems. The educational system in Albania is currently facing a number of issues such as identifying students’ needs,
personalization of training and predicting the quality of student interactions. Educational Data Mining provides a set of techniques,
which can help the educational system to overcome these issues. The objective of this research is to introduce Educational Data Mining,
by describing a step-by-step process using a variety of techniques such as Attribute Weighting (Weighting by Information Gain, Relief,
Hi-Squared, Uncertainty), Clustering (K-Means), Classification(Tree Induction), Association Mining (Apriori, FPGrowth, Create
Association Rule, GSP) in order to achieve the goal to discover useful knowledge from the Moodle LMS. Analyzing mining results
enables educational institutions to better allocate resources and organize the learning process in order to improve the learning experience of
students as well as increase their profits. The experimental results have shown that the data mining model presented in this research was
able to obtain comprehensible and logical feedback from the LMS data describing students’ learning behavior patterns. For this work,
Rapid Miner (v5.0) and Weka (v3.6.2) data mining tools were used to mine data from the Moodle system, used in “C Programming -
CEN112” course taken by Computer Engineering students at Epoka University, during Spring Semester 2009-2010.