ECR Projects

Explore past and current fundamental STEM education research projects across the three research areas that NSF's EDU Core Research (ECR) program funds, as well as across ECR funding types. Other search filters draw from both NSF's data and the ECR Hub's hand coding of award abstracts.

Ninth-grade biology students create cell models using clay.

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STEM Learning and Learning Environments STEM Learning and Learning Environments  

Expanding Applications of Network Analysis to STEM Education Research

Effective Years: 2022-2025

To better support college students in learning about science, it is important to understand how they make connections among the concepts and ideas as they are learning. Network Analysis (NA) has potential to enable new ways of analyzing student learning data to reveal such connections. This project seeks to serve the national interest by developing a researcher’s skills to apply the statistical techniques of NA to real-time data about students’ science learning. The principal investigator (PI) of the project will participate in professional development to hone her skills in Network Analysis and apply them to analyze data collected while college students learn about the immune system. The PI and other members of the project team will subsequently share what they learn about the use of this approach via workshops and publications, which will enable other science education researchers to apply the techniques to real-time data to make new discoveries about students’ knowledge and learning processes.

This capacity-building project at University of Illinois at Urbana-Champaign will develop the PI’s analytical skills in Network Analysis while exploring how this approach can be applied to better understand undergraduate students’ science learning. Statistical techniques of NA can be applied to student knowledge structures, but this has rarely been done with real-time data from individuals. These novel approaches to analyzing students’ knowledge structures have potential to advance knowledge regarding, for example, which concepts are most central and how tightly linked individual pieces of knowledge are within a knowledge network. The research findings are expected to contribute fundamental understandings to both Information Processing Theory and an emerging theories of multi-text multimedia comprehension. The PI will learn about Network Analysis and apply it to two extant think-aloud/eye tracking datasets collected from undergraduate biology students. To accomplish these objectives, the PI will take a course in NA and receive coaching from mentors and advisors with expertise in the subject. Subsequently, the project team will design and implement two workshops on novel applications of Network Analysis to real-time data. These applications are expected to be valuable to a wide range of researchers in STEM education and related disciplines such as medical education, cognitive psychology, and human-computer interaction. The project is supported by NSF's EHR Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.