Using Python to analyse experimental data in high school physics: a study based on external aggregate assessment exams

Background and purpose: This research addresses the need for improved analytical skills in high school physics students, particularly in the context of interpreting experimental data - a critical skill assessed in final, external summative examinations. Traditional teaching methods often lack interactive, hands-on data analysis tools, limiting students’ understanding of error analysis, graph interpretation, and other experimental techniques. Integrating Python programming into the curriculum offers a modern approach to bridge this gap, equipping students with practical, analytical skills that enhance both their classroom learning and performance in high-stakes assessments. Aims: This study specifically focused on enhancing students' ability to analyse experimental data through Python programming. The goal was to improve their understanding of error margins, linear relationships, and data visualization techniques. Study design or methodology: The participants were 12th-grade physics students (ages 17-18), with a sample size of 12. The research took place in a high school classroom setting, where students engaged with experimental data and Python programming for data analysis. Data collection involved both pre- and post-intervention assessments, alongside qualitative observations and a student survey on their experience. Custom Python scripts and datasets were provided as resources to guide analysis exercises, particularly focusing on error analysis and graphing. Findings: Key findings indicate that Python-based data analysis significantly improved students' comprehension of experimental data concepts. Students demonstrated greater accuracy in calculating errors, plotting graphs, and interpreting data trends. Survey feedback showed increased confidence in handling data and understanding statistical concepts. Conclusions, originality, value and implications: This study contributes to the field by illustrating how integrating Python programming into physics education can enhance analytical skills, adding value to both classroom practice and digital learning resources. The approach offers a scalable model for other educators to implement programming in experimental data analysis, fostering students' readiness for data-intensive tasks in advanced studies and careers.

Keywords (free text)

A Level Physics, Python programming, data analysis, experimental skills, examinations, error analysis, graph interpretation

Permanent link to cite this item

https://hdl.handle.net/20.500.14069/949

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