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Coding Science Internship Units: CS for Science Classrooms

Ari Krakowski
Eric Greenwald

The Coding Science Internships project aims to create and research simulated internship curriculum units that confront barriers to broader participation in computer science and position coding as a tool for doing science. The curricular resources engage middle school students in code-to-learn experiences in which they deepen their science understanding while they develop computational thinking and core programming practices. By integrating coding within core science courses and providing teachers with structured, educative curriculum materials, the Internships are designed to expand participation in coding beyond self-selected populations and build teacher capacity for integrating computer science concepts and practices in science courses.


The two Coding Science Internships that we are developing and researching are designed to provide a motivating, student-centered learning experience in which students work collaboratively to address meaningful real-world problems. The first unit, framed around coral ecosystem restoration, emphasizes coding concepts related to sequencing, loops, conditionals, and modeling. As an example, student intern teams use evidence and real-world data to program a scientific simulation to represent a coral reef ecosystem under threat. The purpose of the simulation is to communicate to stakeholders how various threats affect the health of a coral reef and how those threats may be mitigated. Students also gain first-hand experience with sequences, loops, and conditionals as they write code to program underwater robots to remove threats in variable conditions. The second unit, framed around air quality, focuses on coding concepts related to data analysis and visualization, abstraction, and modularity to engage students in coding data visualizations using real EPA air quality data. A core goal is to enable students to experience some of the increasingly prevalent ways that computer science is integrated into the work of scientists.

NSF Awards: 1657002

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Published Date
May 2020
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