Boosting Human-to-Human Interaction for Collaborative Learning: Social Exchange Theory Perspective

In this paper, a method of robot-assisted exchange between team members is proposed. This method is based on Social Exchange Theory, which originates from communication research. To boost human-to-human interaction in the context of collaborative learning, the robot collects activity data in order to determine the learners' psychological state and the perceived benefits and costs of interaction as indicators of intention to interact. Using an estimate of intention, the robot then selects strategies to improve the interaction quality.