Barnes, T., Desmarais, M., Romero, C., & Ventura, S.
Abstract
This is an electronic version of the paper presented at the International Conference on Educational Data Mining (EDM'09), held in Cordoba (Spain) on 2009E-Learning systems offer students innovative and attractive ways of
learning through augmentation or substitution of traditional lectures and
exercises with online learning material. Such material can be accessed at any
time from anywhere using different devices, and can be personalized according
to the individual student’s needs, goals and knowledge. However, authoring and
evaluation of this material remains a complex a task. While many researchers
focus on the authoring support, not much has been done to facilitate the
evaluation of e-Learning applications, which requires processing of the vast
quantity of data generated by students. We address this problem by proposing an
approach for detecting potential symptoms of low performance in e-Learning
courses. It supports two main steps: generating the production rules of C4.5
algorithm and filtering the most representative rules, which could indicate low
performance of students. In addition, the approach has been evaluated on the log
files of student activity with two versions of a Web-based quiz system.This work has been funded by Spanish Ministry of Science and Education through the
HADA project TIN2007-64718