A Multi-Agent Model for Personalizing Learning Material for Collaborative Groups

Abstract

The use of computer-supported collaborative learning (CSCL) environments in teaching and learning processes has increased during the last decade. These environments have various collaboration, communication and coordination tools that students and teachers can use without depending on the time and place where they are. However, having software tools that support group learning does not guarantee successful collaboration because factors such as insufficient knowledge of study contents can impair learning. The analysis of group interactions should allow teachers to recognize obstacles in the learning process, but when there are a lot of interactions the manual analysis is unfeasible owing to time and effort required. This chapter presents a multi-agent model that personalizes the delivery of learning material when groups of collaborative students manifest lack of knowledge. In addition, this chapter describes results of experiments conducted to evaluate the feasibility of using Lucene for retrieving learning material written in English and Spanish.