Combining social network analysis and actor-network theory into a more comprehensive method to study complex classroom interactions between human and non-human actors

Learning sciences have emphasized teaching practices that engage the student in deeper and more demanding processes. The resulting learning activities are inherently complex, particularly in modern, digitally intensive science classrooms where students actively engage in collaborative investigations with digital tools. The resulting complex learning settings challenge current research methods. Inspired by methodological and theoretical developments in science education research, namely network analysis and materiality, we present a new methodological approach that is based on a combination of social network analysis and actor-network theory. Actor-network is conceptually different from social network, however it is possible to introduce the non-human actors of actor-network theory into social networks and then apply methods of social network analysis. The methodological development is presented through a case in which three students use computer-based data logging and investigate the motion of a car on a track. From this case, we report the roles, importance, and interaction patterns of actors engaged in collaborative knowledge construction. The results show the benefits and the importance of including the non-human actors from actor-network theory in quantifying the importance and the roles of the different actors. This combination allows better understanding of learning in a complex setting of human and non-human actors.