College of Education and Human Development

Erin Peters-Burton, PhD

Donna R. and David E. Sterling Endowed Professor in Science Education

PhD, Education, George Mason University

Key Interests
Science Education | Self-Regulated Learning | Computational Thinking | STEM School Models | Epistemic Network Analysis | Nature of Science Knowledge

Research Focus

I am an educational researcher currently pursuing research projects in the nexus of the nature of science, student learning, science teacher pedagogical content knowledge, and educational psychology. I have served as a Principal Investigator for 5 NSF-funded projects studying critical components of exemplary inclusive STEM schools, as well as creating an electronic notebook that fosters high school science students’ computational thinking during data analysis with self-regulated learning. I am co-editor and contributing author to the STEM Road Map Curriculum Series published by National Science Teachers’ Association Press. I am currently working with high school science teachers to develop interventions for improving students’ self-regulation of learning for science and engineering practices, particularly computational thinking.

Current Projects

■ Fostering Student Computational Thinking (CT) with Self-Regulated Learning (SRL): the Science Practices Innovation Notebook (SPIN) is an electronic tool for lesson sharing and analysis built collaboratively by high school teachers. It will promote new transdisciplinary approaches to computational STEM teaching/learning that integrate CT and SRL into Earth Science, Biology, Chemistry, and Physics. The project also provides instruction and training for teachers.

■ Developing a Model of STEM-Focused Elementary Schools: this grant will translate research on inclusive STEM high schools into critical components that can be applied to elementary schools. The components will be verified by studying five STEM-focused elementary schools. The logic model produced can then aid in the development of future STEM-focused elementary schools.

■ Epistemic Network Analysis for Science Learning: Epistemic Network Analysis (ENA) is an analysis technique derived from Social Network Analysis and a card sorting method. ENA makes maps of the strength of the connections between ideas, the clustering of ideas, and central ideas. We use ENA to view how learners are connecting ideas and the density of these ideas.

Select Publications

Lynch, S. J., et al. (2018). Understanding inclusive STEM high schools as opportunity structures for underrepresented students: critical components. Journal of Research in Science Teaching, 55(5), 712-748.

Peters-Burton, E. E., & Botov, I. S. (2017). Self-regulated learning microanalysis as a tool to inform professional development delivery in real-time. Metacognition and Learning, 12(1), 45-78.

Peters, E., & Kitsantas, A. (2010). The effect of nature of science metacognitive prompts on science students’ content and nature of science knowledge, metacognition, and self‐regulatory efficacy. School Science and Mathematics, 110(8), 382-396.


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