I am a learning scientist whose work explores computational literacy, with special focus on how young people learn about scientific computing, its power, and its limitations. Most recently, I have explored how two varieties of scientific computing in particular, visual data analysis tools and agent-based simulation, can be responsibly introduced as epistemic tools within the precollegiate curriculum. Because my research focuses on the ways in which these tools allow youth to explore large-scale systems with significant social impacts (e.g. climate, health patterns, nutrition, pollution), I pay special attention to the ways in which students' personal experiences with these systems can be represented and explored through computing and digital narrative. Learn more about my work here.
I direct the Computing, Reasoning, and Expression (CoRE) Lab. My work has been featured in journals including Educational Researcher, Journal of the Learning Sciences, Science Education, Instructional Science, and the Journal of Science Teacher Education. In 2014 my research was selected for a National Science Foundation Early CAREER Award, and in 2020 I was awarded the AERA Division C Jan Hawkins Award for Early Career Contributions to Humanistic Research and Scholarship in Learning Technologies. In 2022 I served as Co-Chair (with Nicholas Horton) of the planning committee for the National Academies of Science, Engineering, and Mathematics Workshop on the Foundations of K-12 Data Science Education.
Lee, V., Wilkerson, M. H., & Lanouette, K. (2021). A call for a humanistic stance toward K-12 data science education. Educational Researcher, 50(9), 664-672. doi: 10.3102/0013189X211048810
Wilkerson, M. H., Shareff, R. L., & Laina, V. (2022). Learning from “interpretations of innovation” in the codesign of digital tools. In M-C. Shanahan, B. Kim, M. A. Takeuchi, K. Koh, A. P. Preciado-Babb, & P. Sengupta (Eds.), The Learning Sciences in Conversation: Theories, Methodologies, and Boundary Spaces. Routledge.
Wilkerson, M. H., Lanouette, K., & Shareff, R. L. (2021). Exploring variability during data preparation: A way to connect data, chance, and context when working with complex public datasets. Mathematical Thinking and Learning. doi: 10.1080/10986065.2021.1922838
Wilkerson, M. H. & Polman, J. L. (2020). Situating data science: Exploring how relationships to data shape learning [Special Issue]. Journal of the Learning Sciences, 29(1), 1-10. doi: 10.1080/10508406.2019.1705664
Erickson, T., Wilkerson, M. H., Finzer, W., & Reichsman, F. (2019). Data moves. Technology Innovations in Statistics Education, 12(1).
Wilkerson, M. H. & Laina, V.* (2018). Middle school students’ reasoning about data and context through storytelling with repurposed local data. ZDM Mathematics Education, 50(7), 1223-1235 doi: 10.1007/s11858-018-0974-9
Wilkerson, M. H. (2017). Teachers, students, and after-school professionals as designers of digital tools for learning. In C. DiSalvo, B. DiSalvo, J. Yip, & E. Bonsignore (Eds.), Participatory Design for Learning. Taylor & Francis. pp. 127-140.
Wilkerson-Jerde, M. H., Gravel, B. E., & Macrander, C. A. (2015). Exploring shifts in middle school learners’ modeling activity while generating drawings, animations, and simulations of molecular diffusion. Journal of Science Education and Technology, 24(2-3), 204-251. doi: 10.1007/s10956-014-9497-5. [PDF][Springer]
Interests and Professional Affiliations
Simulation Learning Environments
Technology and Schools
Classroom Learning Environments