Applying Design Thinking to Teach Physics and Mathematics: A Case Study on Building a Parachute and Analyzing Its Properties
Bibliographic record
Abstract
Eleven high school students participated in a one-week STEM summer camp focused on designing and building parachutes to deliver fragile objects safely. Using the Engineering Design Process (EDP) as a framework, students explored how canopy size affects performance. They applied physics concepts such as terminal velocity, forces, and acceleration, alongside mathematical skills like diagram interpretation. The program incorporated innovative technologies, including 3D design and printing tools and the BBC micro:bit microcontroller. Students followed the EDP steps—designing, building, testing, and refining prototypes—while also discussing the nature of science and distinguishing it from engineering practices. The camp successfully met its objectives: students enhanced their understanding of physics concepts, grasped key aspects of the nature of science, and demonstrated the ability to follow the EDP. They designed and built two parachutes, collected and analyzed data from test falls, and drew meaningful conclusions. This study highlights the potential of integrating engineering, physics, mathematics, and the nature of science into STEM education. The findings suggest that guided use of the EDP and modern technologies can improve students' scientific knowledge and problem-solving skills, fostering a deeper engagement with STEM concepts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".