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Use of social metrics to discover interaction patterns that impact learning

Abstract:

Social learning analytics is an emerging discipline that provides new methods to explore data from social educational environments and a better understanding of the student behaviour. In this work we propose the discovery of interaction patterns that impact learning, as a result of the interpretation of social metrics calculated on the graph that models the interactions between students, between students and teachers, as well as between students and educational resources that support their learning. The general architecture of DIIA is presented, an environment oriented to support teachers who use formal and informal social networks in their teaching activities. The environment has an interface that allows the visualization and simple interpretation of the patterns detected. In particular, social metrics implemented and their suggested interpretation applied to discover patterns that impact learning in either a favourably or unfavourably way are presented.