ABSTRACT

Past research has shown relationships between student dispositions and motivational states and productive interaction with learning technologies. Much of this work has treated learning as an individual activity, without explicit consideration of how the social context influences student motivational states or how the social context reflects the ways in which these states wax and wane over time. An important limitation of this paradigm is that it does not consider the ways in which the presence of the technology within the social context affects social interactions within that context, despite the fact that such effects are undoubtedly present for better or for worse. The danger is that iterative development methodologies that are blind to these effects may produce technologies that are optimized for effective individual use but with unknown, and possibly negative, effects on the social environment in which the learning takes place. In this chapter, we propose a technical infrastructure and evaluation methodology in which instructional technology is situated in an intelligent environment where social interactions are monitored using machine learning technology, and those analyses prompt the environment to mediate the interaction between students and the technology with the goal of maintaining a positive effect on those social interactions. This infrastructure has been used effectively within the field of computer supported collaborative learning. Here we propose its broader use in connection with interactive learning interventions more generally.