Detecting Symptoms of Low Performance Using Production Rules

E-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 lo...

• Type: Journal
• Year: 2009
• Where: EDM
• Authors: Javier Bravo Agapito, Alvaro Ortigosa
• Keywords: Data Mining, E-learning, E-learning Systems, EDM 2009, Low Performance