Using decision trees for discovering problems on adaptive courses

Abstract

Copyright by AACE. Reprinted from the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Nov 17, 2008, with permission of AACE (http://www.aace.org).Adaptive Hypermedia Systems personalize the learning experience of each user, by providing learning materials adapted to his/her needs, preferences, personal characteristics, etc. The goal is to make the learning process easier or more efficient. However, on the teacher side the improvement and evaluation of these systems are difficult tasks, especially when there are multiple student profiles or huge amount of interaction data of students. In this work, data mining methods, and specifically decision trees, are used for helping in both improvement and evaluation. Our work consists of analyzing two data sets by using decision trees. The first data set contains the interaction data of 24 real students, and the second data set is composed of synthetic data about 100 students. The results of these analyses demonstrated that 24 students is a small data set when decision trees are used. However, the tree showed information relating to the practical activities in which students had more problems for completing them providing useful feedback to the course designer.This work has been funded by Spanish Ministry of Science and Education through the HADA project TIN2007-64718. Cesar Vialardi is also funded by Fundación Carolina