Enhancing Scientific Reasoning and Discussion with Conversational Agents

摘要

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.