Estrategias basadas en análisis de redes sociales para optimizar el clima de aprendizaje en la universidad

The aim of this study was to detect leaders among a group of university students based on their centrality scores and, from these scores, to distribute roles to help enhance learning climate. We understood that learning climate improved when the construction of networked knowledge was stimulated from the perspective of Computer-Supported Collaborative Learning. We conducted the educational experiment in a virtual environment. An online discussion forum was set up in a loo- sely structured format, and the students’ centrality scores were calculated from the social network they generated in the forum. Our findings show that student connectivity increased significantly and that several leadership styles were detected. Based on these leadership styles we designed strategies for optimizing learning climate in a self-regulated and stable way. Based on the type of centrality, we detected leaders in terms of their popularity, sociability, closeness to others, control of information flowing through the network, and influence. The novelty of this study resides in the incipient production of educational technology based on Social Network Analysis and, specifically, on the design of centrality-based strategies for optimizing climate in the university classroom.