Applying our Language Processing Work in an Educational Context

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Applying our Language Processing Work in an Educational Context

Our research focuses on understanding the inner workings of groups and communities by computationally modeling the way social and psychological processes are reflected through conversational interactions. We are pursuing this work in a wide variety of very different social settings, but group learning is where we have invested the bulk of our effort. Exploring the same conversational constructs in multiple very different settings allows us to form abstractions and test their generality in a rigorous way. In our work we draw from rich theoretical models from sociolinguistics, and pair them down to precise operationalizations that capture the most important essence of what is happening. The challenge is in identifying what that most important essence is. From a computational perspective, we are drawing from the literature on domain adaptation and latent variable modeling to address issues of generalization across social contexts. In the computer supported collaborative learning community our work is used to monitor group learning interactions and dynamically trigger contextually appropriate supportive interventions.

  1. Howley, I., Mayfield, E. & Rosé, C. P. (to appear). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.

  2. Jain, M., McDonogh, J., Gweon, G., Raj, B., Rosé, C. P. (2012). An Unsupervised Dynamic Bayesian Network Approach to Measuring Speech Style Accommodation, in the Proceedings of the European Association for Computational Linguistics

  3. Gweon, G., Jain, M., McDonogh, J., Raj, B., Rosé, C. P. (2012). Predicting Idea Co-Construction in Speech Data using Insights from Sociolinguistics, in Proceedings of the International Conference of the Learning Sciences.

  4. Kumar, R. & Rosé, C. P. (2011). Architecture for building Conversational Agents that support Collaborative Learning, IEEE Transactions on Learning 4(1), pp 21-34

  5. Wang, H. C., Rose, C. P., Chang, C. Y. (2011). Agent-based Dynamic Support for Learning from Collaborative Brainstorming in Scientific Inquiry, International Journal of Computer Supported Collaborative Learning 6(3), pp 371-396.

  6. Howley, I., Mayfield, E., Rosé, C. P. (2011). Missing Something? Authority in Collaborative Learning, in Proceedings of Computer Supported Collaborative Learning

  7. Mayfield, E. & Rosé, C. P. (2011). Recognizing Authority in Dialogue with an Integer Linear Programming Constrained Model, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

  8. Howley, I., Adamson, D., Dyke, G., Mayfiled, E., Beuth, J., & Rosé, C. P. (in press). Group Composition and Intelligent Dialogue Tutors for Impacting Students’ Self-Efficacy, in Proceedings of Intelligent Tutoring Systems

  9. Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, submitted to the International Journal of Computer Supported Collaborative Learning 3(3), pp237-271.

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