Bridging Research and the Classroom: Leveraging Multi-Wavelength Data for SED Plots of Young Stellar Objects Using Google Colab
This page is based on work first presented at the American Astronomical Society Winter Meeting in January 2025 as an iPoster.
James Newland1, April Andreas2, Justin Hickey3, Elizabeth Ramseyer4, David Strasburger5
1Texas Advanced Computing Center, University of Texas at Austin, Houston, TX, 2McLennan Community College, Waco, TX, 3NITARP (Episcopal High School, TX), Houston, TX, 4NITARP, Skokie, IL, 5Lawrence Academy / NITARP, Somerville, MA.
Most activities in Astronomy 101 courses do not focus on using multiple data sets across various wavelengths from ground and space-based missions. Still, real-world astronomers rarely use just one source for their research. To address this issue, participants in the NASA/IPAC Archive Teacher Research Program (NITARP) developed a computational essay template to produce spectral energy distribution (SED) plots for potential young stellar objects (YSOs). With a focus on ready-to-use functionality, the group designed the template using Google Colab. This hosted Jupyter Notebook service does not require installation, login credentials, or admin privileges. While advanced users can modify the code and collaborate remotely and asynchronously, students can access powerful astronomy tools with little preparation time or experience. Students can use the template to help classify YSO candidates while leveraging data science and computational thinking skills. Using a computational essay format exposes students to SQL and Python, which can access and process data from across multiple servers while providing a narrative that can provide context and interpretation from researchers. Students can use the notebook to explore SED creation and YSO classification, allowing them to do potentially novel research with only a small introduction. The computational essay can also be used by teachers who want to create SED plots programmatically rather than using a spreadsheet tool. This work is part of the BIg NITARP Alumni Project, which aims to produce classroom tools using NITARP experiences and resources.
Why YSOs for High School Astronomy?
Young Stellar Objects (YSOs) and Spectral Energy Distributions (SEDs) are excellent topics for high school astronomy (Rebull, 2024) because they offer a perfect mix of challenge and discovery. YSOs are fascinating because they represent stars in the earliest stages of their lives, providing a window into how stars form and evolve. This makes them inherently engaging—like cosmic mysteries waiting to be solved.
Working with SEDs introduces students to a key tool used by professional astronomers. It allows students to explore how light behaves across different wavelengths and how that information helps classify objects in space. This process teaches essential concepts like the electromagnetic spectrum, energy conversion, and data visualization.
Even better, these topics connect students directly to actual astronomical research. They learn to analyze authentic data, make scientific interpretations, and possibly even contribute to discoveries. Plus, the activity feels like a genuine investigation—it’s not just learning about astronomy; it’s doing astronomy.
Using Python to handle the SED production aligns with actual astronomy practice (Normal et al., 2019). Jupyter notebook-style astronomy Python programming has become common in practice and belongs in the classroom (Lundgren & Trainor, 2023).
From Excel to Google Colab
Excel is Not Our Friend Here
Most high school students use Excel as the standard tool to plot their first SED. Unfortunately, because of the characteristics of astronomical data sets, all information must be adjusted, normalized, and compared.
Add in manual lookup tables, and even without the linear regression analysis, students are ready to give up before they’ve even started. Plus, you’ve lost only an hour or more of classroom time to get a graph far from… stellar.
Make it Happen with Google Colab
Even without prior programming experience, students and teachers can easily follow a computational essay to learn about YSOs and SEDs. It walks them through each calculation step-by-step, eliminating any sense of “magic” and allowing them to generate straightforward, high-quality plots. As they gain confidence, students can modify and analyze data independently, all while preserving the original file’s integrity.
Computational Essays for Learning
Computational essays (Odden, 2023) are particularly valuable for students because they provide a step-by-step learning experience. They can see how each piece of code contributes to the overall analysis, experiment with the code to explore “what if” scenarios, and gain deeper insights by directly interacting with the material. A computational essay is both an educational tool and a hands-on learning environment.
Introduce students to the basics of programming in Python without overwhelming them with the details:
Use formatted equations to show basic derivations and how to write functions to simplify the calculations.
Student agency is supported while encouraging exploration using viable alternative targets demonstrating different YSO properties.
Computational Thinking and Data Science
- Computational thinking states a problem in a form that is best solved using a computer.
- Data science uses computing and statistics to interrogate data to answer questions.
Using computational thinking in science teaching (Lee et al., 2020) involves the symbiotic building of knowledge in computing and science. Data science pedagogy asks students to interrogate data through computing, perform statistical analysis, and construct domain-specific knowledge (Israel-Fishelson et al., 2024).
Extensions and Future Work
Beyond SEDs, astronomers use a variety of diagrams to help identify YSOs. Based on feedback from this project, we’re excited to develop a more comprehensive set of Google CoLab tools tailored for high school classrooms. Additionally, we aim to expand the tool’s capabilities beyond the initial IC 417 region. This will involve a more advanced process of retrieving datasets in real-time, opening up new possibilities for exploration and analysis.
This work was conducted as part of the NASA/IPAC Teacher Archive Research Program (NITARP), which receives funding from the NASA ADAP program. We acknowledge the additional teachers and students who worked on our many teams and contributed their time and energy to NITARP experiences in large and small ways. Thanks to Dr. Luisa Rebull from the NASA/IPAC team for helping with this project. Also, the work of Adam LaMee (2021) to bring Google Colab to science teaching was a catalyst for this kind of work.
References
Bargagliotti, A., Franklin, C., Arnold, P., Johnson, S., Perez, L., Spangler, D. A., & Gould, R. (n.d.). Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) A Framework for Statistics and Data Science Education Writing Committee The Pre-K-12 Guidelines for Assessment and Instruction. Retrieved July 4, 2024, from https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf
Israel-Fishelson, R., Moon, P., Tabak, R., & Weintrop, D. (2024). Understanding the Data in K-12 Data Science. Harvard Data Science Review, 6(2). https://doi.org/10.1162/99608f92.4f3ac3da
LaMee, A. (University of C. F. (2021). Teaching Science Content with Jupyter at Scale, Elementary Through University. American Association of Physics Teachers Virtual Winter Meeting 2021, 88–88. https://doi.org/10.48448/46br-xe05
Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational Thinking from a Disciplinary Perspective: Integrating Computational Thinking in K-12 Science, Technology, Engineering, and Mathematics Education. Journal of Science Education and Technology, 29(1), 1–8. https://doi.org/10.1007/S10956-019-09803-W
Lundgren, B., & Trainor, R. (2023). ESCIP: A collaboration for developing and sharing educational Jupyter Notebooks. American Astronomical Society Meeting Abstracts, 241, 246.04. https://ui.adsabs.harvard.edu/abs/2023AAS…24124604L/abstract
Norman, D., Cruz, K., Desai, V., Lundgren, B., Bellm, E., Economou, F., Smith, A., Bauer, A., Nord, B., Schafer, C., Narayan, G., Li, T., Tollerud, E., Sipőcz, B., Stevance, H., Pickering, T., Sinha, M., Harrington, J., Kartaltepe, J., … Dong, C. (2019). The Growing Importance of a Tech Savvy Astronomy and Astrophysics Workforce. Bulletin of the AAS, 51(7). https://baas.aas.org/pub/2020n7i018
Odden, T. O. B., Silvia, D. W., & Malthe-Sørenssen, A. (2023). Using computational essays to foster disciplinary epistemic agency in undergraduate science. Journal of Research in Science Teaching, 60(5), 937–977. https://doi.org/10.1002/tea.21821
Rebull, L. M. (2024). Astronomy data in the classroom. Physics Today, 77(2), 44–50. https://doi.org/10.1063/pt.vlhh.iudp
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(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5