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Training collaboration skills to improve group dynamics

Computer Supported Collaborative Learning (CSCL) systems have recognized advantages. However, using these systems does not guarantee an effective collaborative learning. Success or failure of the learning experience depends on the collaborative skills the students show in the group. This work presents a multiagent model applied to CSCL environment, which aims both at recognizing conflicts occurring in group dynamics and at providing personalized training of collaborative skills demonstrated by group members. Conflicts are recognized by applying the Interaction Process Analysis method. Personalization is achieved through Bayesian networks that consider students' collaborative characteristics to elucidate the most suitable training strategy. The model was implemented in a distance learning environment and showed great efficacy.