For more information, see the Cornell Website as well.
Today, people connect with others from around the world in chatrooms, discussion lists, blogs, virtual game communities and other Internet locales. In the work domain, firms are increasingly taking advantage of computer-mediated communication (CMC) tools to establish global teams with members from a diverse set of nations. In education, schools are implementing virtual campuses and immersing students in other cultures. Bridging nations via technology does not, however, guarantee that the cultures of the nations involved are similarly bridged. Mismatches in social conventions, work styles, power relationships and conversational norms can lead to misunderstandings that negatively affect the interaction, relationships among team members, and ultimately the quality of group work. This project seeks to offer a novel, dynamic approach to promoting intercultural communication, adapted from the field of Computer-Supported Collaborative Learning (CSCL) that relies on context sensitive interventions triggered on an as-needed basis. Specifically, the proposed work focuses on communication problems related to what has been called transactivity, or the extent to which messages in a conversation build on one another in appropriate ways. In the CSCL literature, this communication-oriented approach has been used to tailor interventions for on-line collaborative learning dialogues. The proposed work extends this approach to the problem of intercultural communication by (a) identifying and categorizing the types of problems that arise in intercultural dialogues and delineating how these problems impact subjective and objective group outcomes; (b) applying machine learning techniques to coded dialogues with the aim of automatically recognizing when problems arise (or are likely to arise) in an intercultural conversation; and (c) developing and testing interventions to improve intercultural communication that can be triggered by this automatic analysis. These goals are addressed by a combination of laboratory studies of intercultural CMC and machine learning research.