Bridging Research and the Classroom: Leveraging Multi-Wavelength Data for SED Plots of Young Stellar Objects Using Google Colab (AAS 245 WM)

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Modern astronomy research requires computation and data science tools, which are not traditionally part of the Astronomy 101 course. This computational essay describes what a spectral energy distribution (SED) is and uses code to construct one. Although existing computational thinking tools like spreadsheets can be leveraged to allow computational thinking to construct science knowledge, the needs of a student or teacher astronomical researcher will likely go beyond introductory tools. The goal of the SED computational essay presented is not only to inform about SEDs and their use but also to be a part of a toolset meant to allow students and teachers to interrogate other stellar photometric datasets at scale. This notebook was designed by alumni of the NASA/IPAC Teacher Archive Research Project (NITARP). The code can be found on GitHub.

Using Data Science in High School Astronomy @ ASP 2024

~ newton

Astronomy datasets can be hard to use for high school astronomy classes. Data science education pedagogy can be leveraged to create astronomy activities in which students interrogate data, create visuals, and use statistical thinking to construct astronomy knowledge. This session describes how the NASA/IPAC Infrared Science Archive (IRSA) can provide a web-based interface for students to use basic data science techniques in astronomy to build data literacy while learning astronomical concepts. The activities shared will be available for anyone but were designed to be used in astro 101 classes in high school or early college.

WeTeach_CS Summit 2024

~ newton

Modern science teaching can benefit from combining computer science and data science. Students can construct science knowledge using data science techniques through writing and programming code. This session will show some Google Colab/Jupyter Notebook Python activities using authentic datasets designed for high school science courses. Learn how to access, reduce, visualize, and interpret some scientific datasets using best practices in basic data science. Some example activities will be explored using web-based tools tested in a classroom environment with students. Ideas about finding and accessing scientific datasets will be explored. All code is available as open source, and all lessons are shared as Creative Commons material.

Be sure to check out my list of activities that incorporate computational thinking, data science, and coding.

Learning Hubble-Lemaître’s Law Using SDSS Data and Computational Thinking

~ newton
Plot of relative distance versus redshift of galaxy population

American Association of Physics Teachers/American Astronomical Society Winter Meeting 2024

Modern astronomical science is increasingly driven by data science and computational thinking. It is possible to have astronomy students construct astronomy knowledge while employing computational thinking and data science pedagogies by using partially-reduced datasets like those from the Sloan Digital Sky Survey (SDSS) in conjunction with Python and Google Colab notebooks. Here, we explore a highly scaffolded activity for students to build a Hubble-Lemaître diagram using data from the Baryon Oscillation Spectroscopic Survey (BOSS) from SDSS. Educators with access to plates from the BOSS mission can tie the activity directly to data associated with the plate. Students access the data directly from the database and use Python and Google Colab notebooks to reduce, visualize, and interpret data in a highly scaffolded format. Students are asked to interpret plots and place data in an astrophysical context. This activity is part of ongoing research into the impacts of using computational thinking pedagogies with physics and astronomy students. This activity has been used in a high school astronomy course. The activity and all associated programming code are freely available as Creative Commons content.