PIs: Martina Rau
University of Wisconsin-Madison
Adaptive educational technologies can much improve students’ learning in science, technology, engineering, and mathematics (STEM) domains. A particular strength of these technologies is that they can provide interactive models that visualize complex concepts. Educational technologies typically use virtual models that students manipulate via mouse or keyboard. Yet, physical models that students manipulate with their hands can be more intuitive because they relate abstract concepts to students? bodily experiences in the real world. 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.
Students’ difficulties in thinking in terms of visual representations jeopardize their learning in science, technology, engineering, and math (STEM) domains such as chemistry. Physical (tangible) and virtual representation modes have complementary benefits for students’ learning. Yet, there is no comprehensive theory of how representation modes complement one another when students learn abstract content. The goal of this CAREER project is to develop such theory. Because effective combinations of physical and virtual representations are concept-, action-, and student-specific, they are likely too complex for instructors to achieve without support. Educational technologies can offer such support, but they cannot interface with physical representations. Hence, another goal of this CAREER project is to translate theory about representation modes into adaptive educational technologies that intelligently blend physical and virtual representations. A series of experiments in 2- and 4-year college chemistry will investigate how physical and virtual representations complement one another, how best to sequence them, and how best to help students make connections among them. User studies with instructors and students will investigate how educational technology design can address educational needs and systemic constraints in real learning contexts and how to support instructors in combining representation modes effectively. Further project activities will expand the intelligent blending framework to STEM domains in K-12 contexts. The project will contribute new theory about how physical and virtual representations complement one another. It will yield practical recommendations for the design of educational technologies that adapt to individual students’ body-based interactions. A concrete deliverable will be an adaptive educational technology for 2- and 4-year college chemistry, available for free and disseminated to diverse populations. By including 2-year-colleges and by making this research applicable to STEM education in K-16 contexts, this project may broaden participation and enhance socioeconomic equality in many STEM domains.