EAGER: Collaborative Mathematics Learning with Robots

PI: Harry Cheng
University of California-Davis

This PI team asks (i) how to use the affordances of modular robotics for distributing work among individuals in a student groups to promote both better engagement and better learning among those learning algebra and (ii) what can be learned more generally from this effort about orchestrating and supporting learning when learners have available to them technology that supports distributed group work. One PI, a mechanical engineer, brings a novel modular robotics platform to this collaboration, one that has many affordances for supporting algebra learning, is quite engaging and has been found to draw in students who otherwise might not become excited about math learning, and that has affordances for supporting distributed cognition and hence suggests some new ways of promoting and supporting collaborative learning. The second PI, a learning scientist and mathematics education researcher, brings expertise in math learning and a nuanced understanding of the ways distributed cognition can be leveraged to promote collaborative math learning. It is possible that there are aspects of collaborative learning that are uniquely afforded by the robotics materials of the first PI and the kinds of project-solving challenges that can be built up around them. And there may be new kinds of collaborative behaviors that arise from use of such manipulatives that suggests new avenues for promoting learning. This EAGER project supports first steps in creating synergy between these two approaches and in making these intriguing ideas about promoting collaboration and math learning concrete. The technological innovation in this project is in learning how to use manipulatives with affordances for supporting distributed cognition to promote collaboration and how to effectively integrate use of such manipulates with use of software designed for collaborative math problem solving. The research is directed at shedding light on how to take advantage of manipulatives that can support distributed cognition and hence collaborative learning, and discovering some of the new kinds of collaborative behaviors that might arise from activities with such manipulatives and how to take advantage of those (or alleviate them) in promoting learning.

The educational goals being addressed are both broader participation goals and learning goals, the idea being that “play” with engaging robots in the context of learning algebra and other disciplines will draw students in to the subject area at the same time it better promotes their learning. Algebra, in particular, is a gateway to more advanced STEM, and drawing more of the population into STEM careers requires drawing more of them into algebra learning and helping more of the population learn algebra well. The potential broader impacts are particularly strong with respect to gender and diversity. This project represents work in its early states on an untested but potentially transformative idea and is likely to catalyze rapid and innovative advances in the use of modular robotics and other computational manipulatives to broaden participation in STEM disciplines and promote deeper learning in those same disciplines.

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