A cross-disciplinary team of researchers from Tufts University has been awarded a $2.4 million National Science Foundation (NSF) Growing Convergence Research (GCR) grant to explore ambiguity, uncertainty, and confusion (AU&C) in STEM education. Conventional wisdom has framed AU&C as obstacles in educational settings, often leading to stress and learning challenges for students. These experiences can, however, have positive impacts on learning, potentially fostering deeper understanding and problem-solving skills. The Tufts team will investigate how students and professionals can reframe and better engage with these cognitive states, thereby motivating students to engage with challenges in STEM environments.
The interdisciplinary project team is: Holly Taylor and Ayanna Thomas (Psychology); Shuchin Aeron, Mark Hempstead, Eric Miller, and Sameer Sonkusale (Electrical and Computer Engineering); Abani Patra (Computer Science and Mathematics); David Hammer (Education and Physics and Astronomy); Milo Koretsky (Education and Chemical and Biological Engineering); and Michael Hughes (Computer Science). The initiative reflects a deep integration of disciplines, including learning science, cognitive science, mathematics, data science, and engineering, making Tufts well suited to this project.
The study will adopt a multimodal approach in which researchers collect a rich variety of data in both controlled lab settings and in regular Tufts STEM classes, from students who consent to participate. The data will be both qualitative (including video and audio of students as they engage in learning as well as their written work) and quantitative (including scores on tests, surveys, and physiological measurements of bodily and affective responses, such as stress). The project team will develop machine learning and natural language processing tools to analyze this data and to model AU&C dynamics at multiple levels, including individual, group, and classroom.
“With our long history in the study of teaching pedagogy and excellence in STEM education, data science, AI, and cognitive science, Tufts is distinctively positioned to develop new knowledge about how to enable students to manage uncertainty and complexity in learning science and engineering,” said Kyongbum Lee, dean of Tufts University School of Engineering and Karol Family Professor in the Department of Chemical and Biological Engineering.
“This new project highlights the expertise among and collaboration of our fantastic faculty and researchers across the learning sciences and across the schools of Arts and Sciences and Engineering,” added Bárbara Brizuela, dean ad interim of the School of Arts and Sciences, dean of the Graduate School of Arts and Sciences, and professor in the Department of Education. These efforts draw upon numerous departments and schools, including the Tufts Institute for Research on Learning and Instruction, the Tufts Center for Engineering Education and Outreach, the Center for Applied Brain and Cognitive Sciences, and the Tufts Institute for Artificial Intelligence.
The goal of the NSF-funded GCR research project is to create new classroom instruction methods to approach AU&C as learning motivators rather than obstacles. Beyond educational revolution, the results could have broad implications for workforce development, worker retraining, and even fields like emergency response, where professionals frequently face uncertainty in high-stakes situations. Moreover, the creation of a hardware and software testbed as part of the project will enable other researchers to apply these methods in diverse educational and professional settings. In other words, this project does not only address STEM education improvements, but also provides a basis for further research applications in the area.
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