From Decision Trees to Classification Rules with Data Representing User Traffic from an e-Learning Platform

The paper presents two state-of-the-art techniques of analyzing data. The employed techniques are decision trees and classification rules. The analyzed data is represented by user traffic gathered from an e-learning platform. User traffic data is represented by actions performed by platform´s users. In our analysis we are interested only in student´s performed actions. The analysis process creates a decision tree from collected data and then derives the classification rules on the same dataset. We investigate the accuracy and interestingness of the two models