Teaching with Code: Globular Cluster Distance Lab

TL;DR

This paper presents an educational approach using real astronomical data and Python programming in Jupyter Notebooks to teach high school students about star cluster distance measurement, integrating computational thinking into astronomy education.

Contribution

It introduces a hands-on, open-source educational lab that combines astronomy and computing, specifically designed for high school classrooms to enhance computational and scientific skills.

Findings

Students successfully analyze astronomical images to determine star cluster distances.

The lab enhances understanding of astronomical algorithms and Python programming.

Materials are openly available for classroom implementation.

Abstract

Modern astronomy increasingly depends on computational thinking. Although some astronomy courses for undergraduates use computing, high school astronomy courses often have little computing. Created as a part of a research experience for teachers in astronomy and another in computer science, this project leverages robotic telescope images and astronomical algorithms to determine the distance to a star cluster using variable stellar photometry. Students investigate Python and Jupyter Notebook to analyze astronomical images to calculate the interstellar distance to a star cluster across the Milky Way. Students will learn how to write Python code that runs in a Jupyter Notebook such that the brightness of stars in an astronomical image can be determined. The real astronomical image data will be directly manipulated and analyzed by code the students create. Student project files and teacher solution files are provided. Code is open source, and materials are available for classroom use.