Board 313: Implementing Computational Thinking Strategies across the Middle/High Science Curriculum

Thomas Tretter; Olfa Nasraoui; Kyle Dylan Spurlock; Breanna Graven

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: NSF Grantees Poster Session
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 10
DOI: 10.18260/1-2--42871
Permanent URL: https://peer.asee.org/42871
Download Count: 130

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Paper Authors

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Thomas Tretter University of Louisville orcid.org/0000-0003-3144-0431

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Thomas Tretter is professor of science education, Director of the Center for Research in Mathematics and Science Teacher Development, and Director of the Gheens Science Hall & Rauch Planetarium at the University of Louisville. His scholarship includes collaborative efforts with science and engineering faculty targeting retention of engineering students and K-12 teacher connections with engineering.

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Olfa Nasraoui University of Louisville

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Olfa Nasraoui is Professor of Computer Engineering and Computer Science, Endowed Chair of e-commerce, and the founding director of the Knowledge Discovery and Web Mining Lab at the University of Louisville. She received her Ph.D. in Computer Engineering a

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Kyle Dylan Spurlock

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Breanna Graven University of Louisville

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PhD candidate in curriculum and instruction with a focus on informal science education. Current graduate research assistant for the First-Year Engineering department studying ways to improve student retention and persistence to graduation.

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Abstract

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.

-------------------------- 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.

Citation
Format

Tretter, T., & Nasraoui, O., & Spurlock, K. D., & Graven, B. (2023, June), Board 313: Implementing Computational Thinking Strategies across the Middle/High Science Curriculum Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42871

TY - CPAPER
AB - 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.

--------------------------
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., &amp;amp; Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25: 127-147.

AU - Thomas Tretter
AU - Olfa Nasraoui
AU - Kyle Dylan Spurlock
AU - Breanna Graven
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Board 313: Implementing Computational Thinking Strategies across the Middle/High Science Curriculum
UR - https://peer.asee.org/42871
DO - 10.18260/1-2--42871
ER -