Dynamic Collaborative Learning Based on Recommender Systems and Emergent Collective Intelligence in Online Learning Communities

Abstract

AbstractThe presence of interaction and mutual support functionalities offered by online social networks has increased in e-learning systems. Current collaborative learning platforms have witnessed the emergence of many resources, relationships and interactions provided by social information systems, leading to an information overload. Indeed, this phenomenon has made users unable to cope with information overload. As a solution, we aim to recommend relevant resources based on the interaction between users. We present a multilayer, graph-based recommender system that enables pedagogical resources to be recommended by relying on the connections between individuals in collaborative online learning communities. The multilayer, graph-based recommender system harnesses emergent collective intelligence in online learning communities. Our proposal describes dynamic collaborative learning that uses a recommender system based on emerging semantic graphs by investigating collective intelligence within online learning communities. Our findings reveal relevant performance results of the multilayer, graph-based recommender system of pedagogical resources. Future studies on network analysis aim to improve the performance of recommendations.KeywordsGraph-based recommender systemDynamic collaborative learningCollective intelligenceOnline learning communities