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Learning With the Stars: A Stretch Into Astronomical Data Analysis

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Pennsylvania Convention Center, Terrace Ballroom Lobby, Table 14

Participate and share: Poster
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Presenters

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Science Teacher
Oley Valley High School
@adjaninetriphosphate
​Janine Bonham is a seasoned classroom and online educator, who specializes in curriculum development in the areas of earth science, astronomy, biology and marine science. She aims to further her students' skill sets by participating in citizen science projects and community service-based learning opportunities that make learning science exciting and relevant. Her experience includes teaching science in Hawai'i (12 years) and Pennsylvania (3 years). She is also creator of the YouTube channel called Learn the Sky (51K subs) where she creates digital content about constellations and stargazing.
Photo
Oley Valley High School
Photo
Oley Valley High School

Session description

As a collaborative student/teacher team working with the NASA/IPAC Teacher Archive Research Program (NITARP), we'll present our work and experiences with authentic astronomical research. You'll learn about opportunities for live-action data research and how to universally raise the rigor of your classrooms.

Purpose & objective

The purpose of this presentation is to share an opportunity for authentic teacher development and student participation in live scientific data analysis with mentor scientists facilitated through NASA. As a result of this presentation, participants will engage with students as they explain the strategies and skills developed while working with NASA scientists. Participants will gather authentic experience anecdotes from a high school educator growing her data analysis skills and learning alongside her students.

The presentation will include visual demonstration of the data sources, modeled analysis and interaction with simulated data sets. Student team members will be available to present on the floor and discuss their experiences and learnings with attendees.

Connections and links to the application process for the next NITARP cohort will be made available to attendees.

Success will be measured by interaction with the weblink and QR code for the team’s presentation.

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Outline

Summary of the fIRes project including explanation of the relevance of research into M class dwarf stars, and QR code connection to NITARP application resources will be shared in the visual poster presentation.

Procedural video-based explanation of data analysis processes, both pre-recorded and live from student-operated devices will provide attendees with an opportunity to interact with the data and the student scientists who conducted the analysis.

Ms. Bonham, the project’s lead teacher will be on hand to discuss the instructional strategies, practical logistics and process of pitching, facilitating and promoting this experience.

QR code and weblinks to NITARP and a student-created website about the experience as well as the team’s scientific poster will be shared for smartphone snap and on handout cards.

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Supporting research

https://nitarp.ipac.caltech.edu/
Gorjian, V. et al 2008, ApJ, 679, 1040, "The Mid-Infrared Properties of X-Ray Sources"
Melissa K. Kjelvik and Elizabeth H. Schultheis, Kjelvik, M. K., Getting messy with authentic data: Exploring the potential of using data from scientific research to support Student Data Literacy. CBE-Life Sciences Education. Retrieved September 29, 2022, from https://www.lifescied.org/doi/full/10.1187/cbe.18-02-0023

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Session specifications

Topic:
Project-, problem- & challenge-based learning
Grade level:
9-12
Skill level:
Beginner
Audience:
Curriculum/district specialists, Principals/head teachers, Teachers
Attendee devices:
Devices useful
Attendee device specification:
Smartphone: iOS
Laptop: Mac
Tablet: iOS
Participant accounts, software and other materials:
QR reader
Subject area:
Science, STEM/STEAM
ISTE Standards:
For Students:
Innovative Designer
  • Students develop, test and refine prototypes as part of a cyclical design process.
Computational Thinker
  • Students collect data or identify relevant data sets, use digital tools to analyze them, and represent data in various ways to facilitate problem-solving and decision-making.
  • Students break problems into component parts, extract key information, and develop descriptive models to understand complex systems or facilitate problem-solving.