2026 ASEE Annual Conference & Exposition

Incorporating Physical Tools, Simulator, and Machine Learning for Teaching Parallax Distance-Based Measurements in High School Astronomy

Presented at Manufacturing Division (MFG) Technical Session 1: Curriculum Development in Manufacturing Education I

Understanding how astronomers measure stellar distances is critical for high school students’ comprehension of space, yet traditional lessons often rely on simplified tools that fail to reflect current research practices. This study introduces a hybrid instructional sequence combining a student-designed physical parallax lab, a custom pixel-based simulation, and a teacher-guided machine learning extension. Across three sections of a high school astronomy course (N ≈ 90), student learning was evaluated with pre-, mid-, and post-tests as well as confidence surveys. Results demonstrated significant improvement: mean test scores increased from 4.5 on the pre-test to 15.1 on the post-test, and self-reported confidence rose from 2.7 to 5.1 on a 7-point scale. These findings indicate that integrating physical measurement, simulation, and computational modeling not only deepens conceptual understanding of parallax but also enhances students’ self-efficacy with both traditional and modern scientific tools. By bridging geometric reasoning with authentic digital workflows, this lesson provides a replicable framework for introducing advanced data-driven methods into secondary astronomy education.

Authors

Ryan Parham College Station High School, College Station ISD [biography]

Ryan Parham is a high school science teacher at College Station High School in College Station, Texas, where he teaches physics and astronomy. He has ten years of experience teaching secondary science, including courses in physics, astronomy, earth and space science, and chemistry. He has been with College Station ISD for six years. He holds a Bachelor of Science degree from Texas A&M University. His instructional interests include hands-on learning, simulation, and data-driven approaches to improving student understanding of complex scientific concepts.
Dr. Sheng-Jen Hsieh Texas A&M University [biography]

Dr. Sheng-Jen ("Tony") Hsieh is a Professor in the Department of Engineering Technology and Industrial Distribution and a member of the Graduate Faculty at Texas A&M University, College Station, TX. His research interests include automation, robotics, cyber-manufacturing and Industry 4.0; optical/infrared imaging and instrumentation; micro/nano manufacturing; and design of technology for engineering education. He is also the Director of the Rockwell Automation Laboratory at Texas A&M University, a state-of-the-art facility for education and research in the areas of automation, robotics, and Industry 4.0 systems. He was named Honorary International Chair Professor for National Taipei University of Technology in Taipei, Taiwan, for 2015-21. Dr. Hsieh received his Ph.D. in Industrial Engineering from Texas Tech University, Lubbock, TX.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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