Understanding the role of interpersonal rapport in collaborative learning
Interpersonal social phenomena, such as interpersonal rapport, is an important element of human interaction, particularly in educational contexts. However, much learning science research focuses on cognitive phenomena in learning, without attending to how those cognitive phenomena are impacted by the social relationship at work between students. In the "Rapport-Aware Peer Tutor" (or, RAPT) project, we study the process and outcomes of interpersonal rapport-building in collaborative learning. In one component of this research, we draw on methods from discourse analysis, computational linguistics, and learning science theories to understand the nature and patterns of use of various types of discourse moves that contribute to - and are impacted by - interpersonal rapport during learning.
Madaio, M., Peng, K., Ogan, A., & Cassell, J. (2018). A climate of support: a process-oriented analysis of the impact of rapport on peer tutoring. In Proceedings of the 12th International Conference of the Learning Sciences (ICLS) , 2017. [*Best Paper Award Winner*] [*Best Student Paper Nominee*] [pdf]
Madaio, M., Cassell, J., & Ogan, A. (2017, June). The Impact of Peer Tutors’ Use of Indirect Feedback and Instructions. In Proceedings of the Twelfth International Conference of Computer-Supported Collaborative Learning, 2017. [*Best Student Paper*] [pdf]
Madaio, M., Cassell, J., & Ogan, A. (2017). “I think you just got mixed up”: confident peer tutors hedge to support partners’ face needs. In International Journal of Computer-Supported Collaborative Learning, 1-21. [pdf]
Madaio, M., Ogan, A., & Cassell, J. (2016, June). The Effect of Friendship and Tutoring Roles on Reciprocal Peer Tutoring Strategies. In International Conference on Intelligent Tutoring Systems (pp. 423-429). Springer International Publishing. [pdf]
In another component of this research, we use methods from multimodal machine learning to better understand and test theories of interpersonal rapport in collaborative learning, and use these models to contribute to theories of social phenomena in learning by identifing verbal and nonverbal behaviors associated with interpersonal rapport.
Zhao, Z., Madaio, M., Pecune, F., Matsuyama, Y., & Cassell, J. (2018). Socially-Conditioned Task Reasoning for a Virtual Tutoring Agent. In Proceedings of the 17th International Conference of Autonomous Agents and Multi-Agent Systems (AAMAS). [pdf]
Goel, P., Matsuyama, Y., Madaio, M., & Cassell, J. (2018). “I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation. In Proceedings of the International Workshop of Spoken Dialogue Systems (IWSDS). [pdf]
Madaio, M., Lasko, R., Ogan, A., Cassell, J. (2017). Using Temporal Association Rule Mining to Predict Dyadic Rapport in Peer Tutoring. In Proceedings of the 10th International Conference on Educational Data Mining, 2017. [pdf]
Yu, H., Gui, L., Madaio, M., Ogan, A., Cassell, J., & Morency, L.P. (2017). Temporally Selective Attention Model for Social and Affective State Recognition in Multimedia Content. In Association for Computing Machinery Conference on Multimedia, 2017. [pdf]