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Supporting classroom orchestration with real-time feedback: A role for teacher dashboards and real-time agents

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Abstract

This paper investigates the role of the physical classroom environment, coupled with a technology environment that includes real-time agents and data analytics, to support the orchestration of complex collaborative inquiry designs in a high school physics classroom. This design-based research contributes to the wider domain of scripting and orchestration (e.g., Dillenbourg 2012; Dimitriadis 2012; Fischer et al. 2013). Guided by a theoretical perspective of learning in knowledge communities (Slotta et al., 2018), we partnered with a physics teacher to co-design curricular activities and assessments that engaged students in collectively solving, tagging and evaluating physics problems, creating a knowledge base of student-contributed examples, and using those examples as a resource in collaborative inquiry challenges. To support the teacher in orchestrating such a complex curricular design, we developed a tablet application that allowed the teacher see the state of the class in real-time, control the flow of activities and helped him know when and where he was needed within the flow of class activities. The tablet leveraged a set of specially designed real-time software agents to process student interactions in real time, allowing dynamic orchestration of student groups, material allocation, and teacher notifications. The paper begins with a review of recent literature on scripting and orchestration, drawing connection to the theoretical perspective of knowledge communities. We then describe our theoretical model, the design-based method, and details of our curriculum and technology environment. The paper concludes with a summary of how the teacher tablet and the real-time software agents helped support the teacher’s real-time facilitation and orchestration.

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Tissenbaum, M., Slotta, J. Supporting classroom orchestration with real-time feedback: A role for teacher dashboards and real-time agents. Intern. J. Comput.-Support. Collab. Learn 14, 325–351 (2019). https://doi.org/10.1007/s11412-019-09306-1

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