2023 ASEE Annual Conference & Exposition

This NSF Research Experience for Teachers (RET) “Research Experience for Teachers in Big Data and Data Science” (award number: 1801513) engaged four middle/high school science teachers in summer 2022 with research related to big data and data science, with follow-up school year implementation of related curriculum. These teachers developed curriculum related to their summer research experience in big data and data science that spanned a range of student ages and topics: middle school science, 9th grade biology, 9th grade health, and 11th grade chemistry. Despite the wide range of student ages, curricular content, and instructional goals, all teachers found rich and varied curriculum applications that fit within their existing curriculum constraints.

With support from project personnel with experience in pre-college science education, for considering possibilities for follow-up classroom implementation related to their summer research, we settled on the Next Generation Science Standards (National Research Council, 2012) practice of “computational thinking” as potentially the most promising connection between their summer research and school year instruction. Teachers then explored a taxonomy of computational thinking in mathematics and science (Weintrop et. al, 2016), eventually settling on a core set of four computational thinking skills (Sheldon, 2017) most likely to be productive for their teaching focus; algorithmic thinking, decomposition, abstraction, and pattern recognition.

This paper reports on the variety of connections teachers developed with the practice of computational thinking, from clustering as an active practice for simulating early generation of the periodic table in a chemistry class, to sampling/resampling populations in outdoor aquatic environments, to programming in middle school science, to adapting explainable AI procedures for generating and analyzing student-generated data in a health education class. Teacher reports of their own learning about research in data science, and how they were able to adapt that learning for the benefit of their middle/high school students, will capture the flexibility and value that this experience provided.

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National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Committee on a Conceptual Framework for New K-12 Science Education Standards. Board on Science Education.

Sheldon, E. (2017, March 30). Computational thinking across the curriculum: Four of the skills used to solve computer science problems can be applied in other classes as well. Edutopia.

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25: 127-147.