Improving Student Computational Thinking Outcome Expectancy through Integrating Model-building in a Secondary Physics Course

The role of computation in physics is increasing in both a professional and educational context. High school physics pedagogy should incorporate computational thinking, data science, and computer science skills to bolster learning across these domains. The study described here shows that when students build physically meaningful models using computer programming in a physics course, their outcome expectancy is positively impacted. Outcome expectancy can predict students’ future academic and professional choices. The STEMcoding platform asks students to construct models using Euler-Cromer step-wise modeling of physical phenomena, such as those described by the laws of motion. The study presented had students use STEMcoding during the kinematics portion of Advanced Placement Physics 1 classes at a large, urban high school in southeast Texas. A moderate positive impact on outcome expectancy was found across various groups in the study population. Results show how computational essays can provide scaffolded learning while allowing students to use code to create interactive models, allowing knowledge construction in a domain-specific manner.

Data Science Pedagogy can Transform how Astronomy Students Construct Knowledge.

If you are attending the Data Science Education K12 Conference in San Antonio, talk to me about data science education in astronomy classes during the individual showcases. Be sure to visit the website for my poster!

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. This presentation is in the iPoster format, as seen at this link.

https://tinyurl.com/CASTPivotData

Eric Friberg and Jimmy Newland share how data science can help drive science learning through analyzing real-world phenomena.

Visit the resources page for lots of content, including slides from the training.

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

Click here to see the slides from this presentation.

Read more about using computational thinking and data science in learning Kepler’s 3rd Law.

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.