A System for Multi-label Classification of Learning Objects

Abstract

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of Learning Object (LO), which provides ubiquitous access to multiple and distributed educational resources in many repositories. This article presents a system that enables the recovery and classification of LO and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives. For this classification, it is used a special multi-label data mining designed for the LO ranking tasks. According to each position, the system is responsible for presenting the results to the end user. The learning process is supervised, using two major tasks in supervised learning from multi-label data: multi-label classification and label ranking.