https://tinyurl.com/CASTPivotData
Eric Friberg and Jimmy Newland share how data science can help drive science learning through analyzing real-world phenomena.
STEM Educator and Researcher.
https://tinyurl.com/CASTPivotData
Eric Friberg and Jimmy Newland share how data science can help drive science learning through analyzing real-world phenomena.
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.
Quantum Engineering and You (QuERY) Program
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.
Read more about my computational thinking research or check out my presentation and all the links here.
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.