UW-Madison’s Martina Rau recently received a prestigious early CAREER grant from the National Science Foundation (NSF) to explore how adaptive educational technologies can improve students' learning in science, technology, engineering and mathematics (STEM).
Rau leads the Learning Representations and Technology Lab on campus, is an assistant professor with the School of Education’s No. 1-ranked Department of Educational Psychology, and holds an affiliate appointment in the Department of Computer Sciences.
According to the NSF website, the Faculty Early Career Development (CAREER) Program is one of the foundation’s most distinguished awards backing early career faculty “who have the potential to serve as academic role models in research and education, and to lead advances in the mission of their department or organization. Activities pursued by early-career faculty should build a firm foundation for a lifetime of leadership in integrating education and research.”
Rau’s CAREER project is titled, “Intelligent Representations: How to Blend Physical and Virtual Representations by Adapting to the Individual Student's Needs in Real Time." The NSF award, for $598,399, will support Rau’s work on this topic involving students and instructors at both four- and two-year colleges over the next five years.
Rau explains that adaptive educational technologies, such as online tutoring systems, are designed to individualize instruction. For example, adaptive educational technologies can give error-specific feedback to help students overcome misunderstandings. They can select appropriate problems for a student to work on based on the student's estimated knowledge level.
Netflix, for example, customizes movie suggestions based on what it "thinks" you would like, explains Rau.
“Similarly, an educational technology can customize learning content to the student, based on what it ‘thinks’ the student knows,” she says.
Specifically, Rau’s research will examine how physical models can be integrated with virtual models into these adaptive educational technologies. A physical model is a tangible object students can manipulate.
“Common examples are ball-and-stick models of molecules in chemistry, electric circuits in physics, or even Play-Doh models of earth layers,” Rau says.
Physical models can be advantageous because they use students' intuition as they relate abstract concepts to their bodily experiences in the real world.
As part of this project, Rau and her team will develop a video-camera application that can give students feedback on physical models they constructed. It will be able to use the information on how students interact with the physical model to estimate what the student knows.
"One of the major advantages of educational technologies is that they can adapt to the individual student's needs in real time,” says Rau. “Currently, this is possible only when students interact with a system via a mouse or keyboard. Expanding these adaptive capabilities to things students do with their hands has the potential to revolutionize educational technologies."
The results of this research should help the team develop an educational technology that adaptively selects physical and virtual models that are most helpful to an individual student given his or her learning progress.
Such technologies could make STEM concepts more accessible to students with diverse backgrounds. And because the research project involves students and instructors from 2-year colleges –- where there can be a lack of access to innovative technologies -- the project may broaden participation and enhance socioeconomic equality in STEM.
“It’s my hope that more tangible and intuitive interactions may help disadvantaged students better visualize complex science concepts,” says Rau. “Therefore, I think this research can bring more students into the STEM pipeline and help close achievement gaps in STEM.”