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

ROSANNA COSTAGUTA; SILVIA SCHIAFFINO; PABLO SANTANA

Optimizing Human- Computer Interaction With Emerging Technologies

IGI Global

Año: 2017; p. 343 - 375

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, communicationand 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 guaranteesuccessful collaboration because factors such as insufficient knowledge of study contents can impairlearning. The analysis of group interactions should allow teachers to recognize obstacles in the learningprocess, but when there are a lot of interactions the manual analysis is unfeasible owing to time and effortrequired. This chapter presents a multi-agent model that personalizes the delivery of learning materialwhen groups of collaborative students manifest lack of knowledge. In addition, this chapter describesresults of experiments conducted to evaluate the feasibility of using Lucene for retrieving learning materialwritten in English and Spanish.