Anita Schuchardt headshot
Office Address

515 Delaware St. SE
Minneapolis, MN 55455
United States



Associate Professor
Biology Teaching and Learning

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Research statement

I am committed to developing and researching the effect of curricula that engage students in learning science through authentic scientific practice, such as generating and testing models of scientific phenomenon. Having students engage in model-based practices to build scientific knowledge has the potential to provide many affordances including; building interest and motivation, fostering understanding of the nature of science, and deepening students’ understanding of scientific concepts and their interconnections. However, there is a need for both development of curricula that foster learning of science content through modeling and research that characterizes the effects of such curricula. I am particularly interested in the intersection of science with other disciplines such as mathematical modeling or computational modeling. I employ both quantitative and qualitative methods to assess the impact of these interventions in diverse contexts, with a focus on developing explanatory mechanistic models of model-based learning in science.

Selected publications

Schuchardt, A., & Schunn, C. D. (2016). Modeling scientific processes with mathematics equations enhances student qualitative conceptual understanding and quantitative problem solving. Science Education, 100, 290-320.

Schuchardt, A., Tekkumru-Kisa, M., Reynolds, B., Schunn, C.D., Stein, M. How much teacher professional development is needed with educative curriculum materials? It depends on the intended learning outcome. (Submitted).

Schuchardt, A. & Schunn, C.D. Mechanism connected mathematics in science education: Changing students’ approaches to quantitative problem solving. (Submitted).

Malone, K. & Schuchardt, A. Longitudinal study of the effects of modeling instruction on scientific reasoning. (Submitted).

Malone, K.L., Schuchardt, A., & Schunn, C.D. Improving conceptual understanding and representation skills through Excel-based modeling. (Submitted).

Schuchardt, A., D'Agati, V., Costantini, F., & Pachnis, V. (1994). Defects in the kidney and enteric nervous system of mice lacking the tyrosine kinase receptor ret. Nature, 367, 380.

Schuchardt, A., D'Agati, V., Pachnis, V., & Costantini, F. (1996). Renal agenesis and hypodysplasia in ret-k- mice result from defects in ureteric bud development. Development, 122, 1919.

Dodd, J., & Schuchardt, A. (1995). Axon guidance: A compelling case for repelling growth cones. Cell, 81, 1.

Education and background

Education & Background

University of Pittsburgh, 2016, Ph.D. Learning Sciences and Policy
Columbia University, 1994, Ph.D. Human Genetics and Development
Cornell University, 1989, B.A. Biological Sciences


2017 Outstanding Doctoral Research Award - National Association for Research in Science Teaching

Carnegie Science Award for Excellence, High School Educator, 2009

Curriculum Vitae

Teaching Statement 

Show a student a solution and you help them for a day, teach a student to think and you feed them for a lifetime. My teaching reflects my research, emphasizing learning of science and pedagogy through enculturation in the practice of science. I have developed a yearlong model-based biology curriculum that asks students to develop core science content concepts through the scientific practice of collecting and analyzing data and discussing conclusions. The model-based biology curriculum has been shown to improve student learning of content and scientific reasoning scores, particularly for students in the lowest quartile of scientific reasoning and it has been adopted in many classrooms nationally. I have also developed stand-alone units where students participate in the scientific practices of data analysis and mathematical and computational modeling to learn genetics and evolution. After completion of the genetics unit, students are better able to make both qualitative and quantitative predictions and switch back and forth between their mathematical models and biological representations in ways that are likely to facilitate problem solving.