26th Australian Institute of Physics Congress and the Australian and New Zealand Conference on Optics and Photonics

Tracking Computational Skills Across Undergraduate Physics Curriculum

Not scheduled

20m

Belinda Hutchinson Building (The University of Sydney )

Contributed Oral AIP | Physics Education (PEG)

Speaker

Teresa Dalle Nogare (University of Sydney)

Description

Computational skills are a core component of modern physics education, underpinning the use of computers for simulations, data analysis, and interpretation of complex physical systems.
As physics graduates are expected to work with complex data and computational models, undergraduate curricula must provide a logical progression of computational skills that enables students to move from basic tasks to more structured analysis.
We examined the learning outcomes and teaching materials of the computational components in three units of the University of Sydney's Bachelor of Science Physics curriculum across first-, second-, and third-year. Our analysis aims to identify gaps between the second and third years, a critical transition period for students who are expected to move towards greater independence, and to evaluate their preparedness for Honours research or employment.
The curriculum audit showed consistency between stated learning outcomes and the reviewed teaching material, with computational content used to support core topics. First-year activities focus on experimental data handling, while second-year computational component accounts for 38% of the unit and is delivered through practical labs in optics and signal processing. In third year, computational training combines numerical-analysis theory, approximately 10%, with practical labs, around 25%, focused on numerical analysis and statistical mechanics.
Using the SOLO taxonomy, we quantified computational depth by classifying each audited activity according to the level of computational knowledge required. The distribution of SOLO levels showed a shift from lower-level activities in the second year toward more structured computational reasoning in the third year. Third-year activities contained a greater proportion of relational outcomes, requiring students to integrate computational methods with physical concepts, interpret numerical outputs, and apply techniques to more complex problems. These findings suggest that computational training in the School of Physics is structured to support the development of increasing computational autonomy and preparation for research- or industry-relevant work.