Enhancing Scientific Reasoning and Discussion with Conversational Agents

This paper investigates the use of conversational agents to scaffold online collaborative learning discussions through an approach called academically productive talk (APT). In contrast to past work on dynamic support for collaborative learning, which has involved using agents to elevate the conceptual depth of collaborative discussion by leading students in groups through directed lines of reasoning, this APT-based approach lets students follow their own lines of reasoning and promotes productive practices such as explanation of reasoning and refinement of ideas. Two forms of support are contrasted, namely, Revoicing support and Feedback support. The study provides evidence that Revoicing support resulted in significantly more intensive reasoning exchange between students in the chat and significantly more learning during the chat than when that form of support was absent. Another form of support, namely, Feedback support increased expression of reasoning while marginally decreasing the intensity of the interaction between students and did not affect learning.

[1]  Kurt VanLehn,et al.  Interactive Conceptual Tutoring in Atlas-Andes , 2002 .

[2]  Pierre Dillenbourg,et al.  Over-scripting CSCL: The risks of blending collaborative learning with instructional design , 2002 .

[3]  K. Koedinger,et al.  Using Intelligent Tutor Technology to Implement Adaptive Support for Student Collaboration , 2010 .

[4]  Carolyn Penstein Rosé,et al.  Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning , 2008, Int. J. Comput. Support. Collab. Learn..

[5]  Pierre Lison,et al.  Multi-Policy Dialogue Management , 2011, SIGDIAL Conference.

[6]  Christof Wecker,et al.  Fading scripts in computer-supported collaborative learning: the role of distributed monitoring , 2007, CSCL.

[7]  Carolyn Penstein Rosé,et al.  Architecture for Building Conversational Agents that Support Collaborative Learning , 2011, IEEE Transactions on Learning Technologies.

[8]  Samuel Fernando,et al.  A Semantic Similarity Approach to Paraphrase Detection , 2008 .

[9]  Carolyn Penstein Rosé,et al.  Supporting students working together on math with social dialogue , 2007, SLaTE.

[10]  Michael Shayer,et al.  An Exploration of Long-Term Far-Transfer Effects Following an Extended Intervention Program in the High School Science Curriculum , 1993 .

[11]  M. Berkowitz,et al.  Measuring the developmental features of moral discussion. , 1983 .

[12]  Antonija Mitrovic,et al.  Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams , 2007, Int. J. Comput. Support. Collab. Learn..

[13]  Päivi Häkkinen,et al.  Specifying computer-supported collaboration scripts , 2007, Int. J. Comput. Support. Collab. Learn..

[14]  F. Fischer,et al.  Collaboration Scripts – A Conceptual Analysis , 2006 .

[15]  M. Azmitia,et al.  Friendship, transactive dialogues, and the development of scientific reasoning , 1993 .

[16]  Carolyn Penstein Rosé,et al.  Exploring the Effectiveness of Social Capabilities and Goal Alignment in Computer Supported Collaborative Learning , 2010, Intelligent Tutoring Systems.

[17]  Pierre Dillenbourg,et al.  The mechanics of CSCL macro scripts , 2008, Int. J. Comput. Support. Collab. Learn..

[18]  Karsten Stegmann,et al.  Scripting argumentative knowledge construction: Effects on individual and collaborative learning , 2007 .

[19]  Carolyn Penstein Rosé,et al.  Tutorial Dialogue as Adaptive Collaborative Learning Support , 2007, AIED.

[20]  S. Michaels,et al.  Deliberative Discourse Idealized and Realized: Accountable Talk in the Classroom and in Civic Life , 2008 .

[21]  Carolyn Penstein Rosé,et al.  Socially Capable Conversational Tutors Can Be Effective in Collaborative Learning Situations , 2010, Intelligent Tutoring Systems.