This empirical project attempts to provide a transformative and novel answer to a foundational issue for science, technology, engineering and mathematics (STEM) learning: How can we measure the development of complex STEM thinking?
Young people today need to develop complex thinking in STEM in order to participate in the social, economic, and cultural life of a globalized world. Yet we cannot create curricula, programs, or activities to teach complex STEM thinking unless we can measure and show whether students have developed it.
In this project, we propose to develop epistemic network analysis (ENA) as a toolkit for measuring complex STEM thinking. Our goal is to develop tools and techniques that will be applicable to any form of complex STEM thinking, and thus our research and dissemination plan is explicitly designed to extend ENA to other STEM domains.
This project is based on the psychological theory of epistemic frames, which suggests that complex thinking in any field requires more than simply knowing basic facts and having basic skills. Epistemic frame theory suggests that complex STEM thinking requires a shared base of knowledge and skills, but also an understanding of the values that guide the use of those skills and of how to make decisions and justify actions to solve problems. More important, complex thinking does not just mean having a set of knowledge, skills, values, and ways of making decisions. It means understanding how these different elements of problem solving are connected: which values to consider before taking a certain action, what knowledge to gather before making a particular kind of decision, and so on. This means the development of STEM thinking can be quantified by a model of that network of connections.
We plan to work closely with groups of early adopters who will use the ENA toolkit and generate suggestions for improvements and other changes, leading to an iterative research and development cycle. The creation of a community of researchers, curriculum and technology designers, practitioners, and policymakers as users of the tool, will, in turn, generate new datasets for us to examine and further refine the conditions under which ENA can be used to measure complex STEM thinking in additional domains.
This project has the potential to advance work in the learning sciences, network analysis, psychometrics, and data visualization, and to develop new connections among them. ENA is more than merely a technical advance in the science of measurement and assessment. It lays the foundation for analyzing creativity and innovation in STEM problem-solving, and thus has the potential to influence conceptions of STEM learning, STEM assessment, and STEM education broadly.
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