Physics Education

A rock gathered on a French beach many years ago has proved to be far more useful to one teacher than perhaps could have been predicted. No fewer than 10 of these uses are detailed, from across the secondary school curriculum and age range. It is recommended that every physics teacher consider acquiring a similar rock as a teaching aid.

The nonlinear elastic properties of rubber cause a specific relationship between the pressure and volume in a rubber balloon. This is manifested by the fact that when the balloon is inflated, the pressure inside it increases sharply, and then gradually decreases as the volume increases. It is interesting to see what will happen to the pressure in a rubber balloon in a vacuum bell jar, when air is not pumped into the balloon, but is pumped out of the bell jar and the balloon expands. The study was conducted using an Arduino system for pressure measurement, the Tracker software for video analysis, and Google Sheets cloud service for mathematical calculations and plotting. This allows for the effective implementation of the STEM approach in education. This study is appropriate to use in the training of future masters-level physics teachers to form digital competence and competence in STEM education. In the future, they can use it as a learning project in the educational process during their professional activities.

Grading assessments is time-consuming and prone to human bias. Students may experience delays in receiving feedback that may not be tailored to their expectations or needs. Harnessing AI in education can be effective for grading undergraduate physics problems, enhancing the efficiency of undergraduate-level physics learning and teaching, and helping students understand concepts with the help of a constantly available tutor. This report devises a simple empirical procedure to investigate and quantify how well large language model (LLM) based AI chatbots can grade solutions to undergraduate physics problems in Classical Mechanics, Electromagnetic Theory and Quantum Mechanics, comparing humans against AI grading. The following LLMs were tested: Gemini 1.5 Pro, GPT-4, GPT-4o and Claude 3.5 Sonnet. The results show AI grading is prone to mathematical errors and hallucinations, which render it less effective than human grading, but when given a mark scheme, there is substantial improvement in grading quality, which becomes closer to the level of human performance—promising for future AI implementation. Evidence indicates that the grading ability of LLM is correlated with its problem-solving ability. Through unsupervised clustering, it is shown that Classical Mechanics problems may be graded differently from other topics. The method developed can be applied to investigate AI grading performance in other STEM fields.

This paper presents a project exploring the intersection of physics education and design, focusing on affordable, aesthetically pleasing tools for school laboratories. Initially aimed at providing practical resources for physics teachers, the project uncovered how 'disciplinary aesthetics' can enhance student engagement and deepen conceptual understanding, especially for those inclined towards the arts. In collaboration with the European Institute of Design (IED), product design students created tools for physics experiments, leading to improved comprehension of scientific principles. The tools were showcased at the 2023 European Maker Faire, drawing significant public interest. Feedback suggests that integrating aesthetics into science education fosters better engagement and learning. The tools are freely available under a Creative Commons license, with further research on the role of aesthetics in education encouraged. The project was selected for the ADI Design Index 2024, becoming a candidate for the 'Compasso d'Oro' prize, and received the Excellence in Design in Lazio Award from the ADI Lazio delegation.

We present an educational activity, aimed at the exploration of rotational motion through the use of a modified fidget spinner and a smartphone. We analyze the physics concepts that are involved in the proposed experimental activity, such as angular velocity and moment of inertia, showing that it is possible to transform a well-known popular toy into an opportunity to teach/learn physics concepts, by performing easy and valuable physics experiments in classroom at undergraduate and first-year university laboratory.

The friction force on the feet when walking is a static friction force. Despite the fact that the foot on the ground remains at rest, the friction force does work to propel the walker forward.

The average acceleration of a car or a sprint runner can be estimated from the time taken to travel a given distance. An improved estimate can be calculated if the speed at that time is known.

The COVID-19 pandemic forced introductory lab courses to shift to an online format. This implementation involved a shift in emphasis in learning goals towards transferable lab skills and involved a range of activities, including PhET simulations, video data collection, analysis of data sets, and open-ended free response conceptual questions. In this study, we examined student perceptions of aspects of the online lab activities and learning outcomes. We find that synchronous attendance is more likely to produce positive learning outcomes and that activities associated with data analysis are perceived to be more difficult. We discuss structural flaws with the learning management systems that can exacerbate student perceptions.

OpenAI's ChatGPT, a formidable large language model (LLM) based on a generative pre-trained transformer architecture, has a remarkable, and rather unsettling, ability to solve conceptually challenging physics problems. One of the more impressive demonstrations of ChatGPT's capabilities in this regard is its expert-physicist-level performance on the questions that make up the Force Concept Inventory [CG West, https://arxiv.org/abs/2303.01067]. Motivated by the deep implications of these advances in LLM technologies for student learning, attainment, and assessment, we asked a class of undergraduate physics students (N = 119) to use ChatGPT to solve problems, and write Python code, related to the eigenfunctions of the Hamiltonian of the 1D quantum harmonic oscillator—a staple of introductory quantum mechanics courses. We discuss the significant pitfalls we encountered when incorporating ChatGPT into coursework in this manner, and make recommendations as to how LLMs might be better embedded into the undergraduate physics curriculum.

The short answer section of the final attainment test for the Advanced Physics I course at the University of Newcastle, Australia was investigated for performance bias based on gender. No overall gender bias was discovered, however there was a small to medium bias for the thermal physics topic. No other topics showed any significant difference by gender. A categorization schema for short answer exam questions was developed and revealed a correlation that if questions contain a majority of categories from the schema then no bias will be observed. Bias observed in the Thermal physics topic may be due to a combination of word density and low visual language.

The aim of this work is to design a user-friendly, illustrative, but also sufficiently accurate experimental setup for the measurement and data analysis, specifically for the determination of the thermal conductivity coefficient of a metal sample. The comparative method was chosen for the measurements, the reference sample was made of aluminium and the measured samples were made of steel and bronze. The measurement data evaluation is performed using LabVIEW software. The results correspond to the tabulated values for these materials, the deviations are less than 1.5%. In addition, a thermal camera can be used in the experiment, which makes the measurement even more illustrative. This method can be used, for example, in teaching in physics laboratories in high schools and technical universities.

It is well known that the work done by a torque, τ, during a small angular displacement $d\theta$ is given by $\tau d\theta$. The same relation applies if a ball is rolling down an incline, even if the torque arises from a static friction force. However, it is usually assumed that the work done by a static friction force is zero.