119 papers
Physics explores the fundamental rules governing our universe, from the tiniest subatomic particles to the vastness of distant galaxies. This category focuses on educational physics, bridging the gap between complex theoretical concepts and clear, understandable explanations for students and enthusiasts alike. It covers how we teach, learn, and visualize the laws of nature in everyday contexts.
Gist.Science monitors arXiv daily to process every new preprint in this specific field. We transform these raw scientific manuscripts into accessible plain-language overviews alongside detailed technical summaries, ensuring that cutting-edge educational research reaches a wider audience without losing its rigor. Below are the latest papers in physics education research and related studies that have recently appeared on arXiv.
This paper presents an interpretable machine learning model enhanced with domain-aligned inductive biases to automatically identify and locate students' acts of mechanistic reasoning in STEM team conversations, demonstrating improved generalization and offering practical guidance for both education researchers and ML designers.
This paper demonstrates how the Gravity Spy project leverages Zooniverse volunteers to identify new LIGO glitch classes, revealing their connection to detector states and highlighting the challenges they pose for machine-learning classification.
This paper argues for the establishment of a dedicated profession called the "Research Guide" to formally train practitioners who support authentic scientific inquiry outside of PhD programs by moving beyond the traditional apprenticeship model to address the unique pedagogical, methodological, and developmental needs of diverse learners.
This study demonstrates that integrating Artificial Neural Networks into undergraduate physics laboratories as a complementary tool for a compound pendulum experiment significantly enhances the precision and consistency of determining gravitational acceleration compared to traditional analytical methods, without replacing the fundamental experimental approach.
This paper explores the "Astro-Animation" initiative, which collaborates between artists and NASA scientists to create scientifically rigorous yet whimsical animations for outreach, evaluates its success in mitigating science anxiety among underrepresented teenagers through interactive workshops, and analyzes the project through the distinct perspectives of a scientist, an animator, and an animation student.
This paper describes an inquiry-based physics activity for third-year undergraduate students that uses a humorous question about the Geneva water jet to teach fluid dynamics, critical reasoning, and Fermi-type estimation through the application of Bernoulli's principle and power analysis.
This study investigates students' reasoning about the CNOT gate through think-aloud interviews, identifying three key cognitive resources—procedural application, qualitative target effects, and the belief that the control qubit remains unchanged—and analyzing how these resources, while foundational, can sometimes lead to misconceptions regarding entanglement and superposition.
This tutorial addresses the bottlenecks in transitioning flat-optics concepts to qualified hardware by proposing a stage-gate workflow, technical checks, and an artifact-based skills map to streamline the development process and clarify workforce training requirements for meta-optics and diffractive optics.
This paper presents an introductory set of lectures on quantum mechanics for macroscopic audiences, primarily based on Gasiorowicz's classic textbook and supplemented by works from Phillips and Dirac, while focusing on one-dimensional systems to minimize mathematical complexity.
This paper presents a low-cost, open-source IoT platform utilizing an Arduino and LED array to enable physics students to conduct autonomous, closed-loop experiments that train and compare machine learning algorithms, thereby bridging the gap between theoretical ML concepts and practical experimental skills.