Triggering effective social support for online groups

  • Authors:

  • Rohit Kumar;Carolyn P. Rosé

  • Affiliations:

  • Raytheon BBN Technologies, Cambridge, MA, USA;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • ACM Transactions on Interactive Intelligent Systems (TiiS)
  • Year:
  • 2014

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Abstract

Conversational agent technology is an emerging paradigm for creating a social environment in online groups that is conducive to effective teamwork. Prior work has demonstrated advantages in terms of learning gains and satisfaction scores when groups learning together online have been supported by conversational agents that employ Balesian social strategies. This prior work raises two important questions that are addressed in this article. The first question is one of generality. Specifically, are the positive effects of the designed support specific to learning contexts? Or are they in evidence in other collaborative task domains as well? We present a study conducted within a collaborative decision-making task where we see that the positive effects of the Balesian social strategies extend to this new context. The second question is whether it is possible to increase the effectiveness of the Balesian social strategies by increasing the context sensitivity with which the social strategies are triggered. To this end, we present technical work that increases the sensitivity of the triggering. Next, we present a user study that demonstrates an improvement in performance of the support agent with the new, more sensitive triggering policy over the baseline approach from prior work. The technical contribution of this article is that we extend prior work where such support agents were modeled using a composition of conversational behaviors integrated within an event-driven framework. Within the present approach, conversation is orchestrated through context-sensitive triggering of the composed behaviors. The core effort involved in applying this approach involves building a set of triggering policies that achieve this orchestration in a time-sensitive and coherent manner. In line with recent developments in data-driven approaches for building dialog systems, we present a novel technique for learning behavior-specific triggering policies, deploying it as part of our efforts to improve a socially capable conversational tutor agent that supports collaborative learning.