Blended learning baseado na inteligência coletiva - BLBIC: análise de um curso de formação judiciária

Abstract

In the second half of the 21st century, Education tries to establish harmony with the cultural transformations that began in the 1950s with the computer revolution. In the current sociotechnical context, two tendencies emerge: Blended Learning and Collective Intelligence. The purpose of this thesis is to investigate the contributions of the union between Blended Learning and collective intelligence for individual and collective learning. Qualitative data comes from a bibliographical survey and data analysis from a Blended course in the scope of judicial training. To address the problem, I got theoretical input in several areas of knowledge and authors. However, I focused on the collective intelligence, especially Pierre Levy's studies, Lev Vygotsky's Sociocontrutivism, the Blended Learning investigations by Michael B. Horn and Heather Staker, as well as my Masters research on operators of Collective Intelligence in a virtual learning environment. Data analysis validated the initial hypothesis that the union between Blended Learning and collective intelligence provides a richer learning space with collaboration, participation, reflection and self-regulation of learning. Based on these results, I propose Blended Learning Based on Collective Intelligence (BLBCI).