CAREER: Intelligent Representations: How to Blend Physical and Virtual Representations by Adapting to the Individual Student's Needs in Real Time

This CAREER project examines how best to integrate physical models into adaptive educational technologies. The project will focus on a domain where models play a crucial role in instruction: undergraduate chemistry. A series of experiments at 2-year and 4-year colleges will test whether physical or virtual models are most effective for particular concepts, in which order to present them, and how to help students make connections among them. Further, the team will develop a technology that can assess how students manipulate physical models.

Results will be consolidated in a comprehensive theory of how physical and virtual models affect student learning. The results will help instructors select the best model for their students. The team will build on the results to develop an educational technology that adaptively selects physical and virtual models that is most helpful to the individual student given his/her learning progress.

This research will yield a new type of educational technologies that blend physical and virtual models and that can adapt to individual students' bodily interactions. Such technologies can make STEM concepts more accessible to students with diverse backgrounds. Further, by involving students and instructors from 2-year colleges who often lack access to technology innovations, the project will broaden participation and enhance socioeconomic equality in STEM.


Martina Rau


Completed on September 30, 2023

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Martina Rau