Gaining Insights from Sociolinguistic Style Analysis for Redesign of Conversational Agent Based Support for Collaborative Learning

Data from an early stage of development of conversational agent based support for collaborative learning provides an ideal resource for demonstrating the value of sociolinguistic style analysis paired with time series visualizations as part of an iterative design process. The methodology illustrated in this chapter was introduced in earlier publications focusing separately on the sociolinguistic style analysis (Howley and Rose, Modeling the rhetoric of human-computer interaction. In: HCII’11 Proceedings of the 14th international conference on Human-computer interaction: interaction techniques and environments, pp 341–350, Springer-Verlag, Berlin, Heidelberg, 2011; Howley et al., A multivocal process analysis of social positioning in study groups. In: Suthers et al. (eds). Productive multivocality in the analysis of group interactions, Springer, 2013) and the time series visualization using the Tatiana tool (Dyke et al., Challenging assumptions: using sliding window visualizations to reveal time-based irregularities in CSCL processes. In: Proceedings of the international conference of the learning sciences. Sydney, Australia, 2012). However this chapter is unique in its application to data that is at such an early stage in a development process. The data is admittedly raw, and contains many examples of interaction gone awry. Nevertheless, the value in this analysis is in a demonstration of what insights can be gained through detailed stylistic analysis of conversational behavior that informs the next steps of intervention development.