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 a gamified board game intervention for undergraduate radiocommunications courses that, through three years of implementation and mixed-method evaluation, successfully enhances student engagement, conceptual understanding, and motivation by transforming traditional lecture-based learning into an interactive, collaborative, and competitive experience.
This paper presents a series of exercises and discussions designed to introduce the concept of ultralight dark matter to undergraduate students by leveraging their existing knowledge of undergraduate physics to explore contemporary cosmological research.
This paper presents an accessible undergraduate-level laboratory experiment that demonstrates how varying the buoyancy Reynolds number influences internal wave generation and breaking, revealing three distinct turbulence regimes through linear stratification, forced topography, and advanced diagnostic techniques.
By analyzing data from over 10,000 students across diverse disciplines, this study develops a predictive model identifying that specific combinations of lecture time, group worksheets, clicker questions, and student questions reliably predict conceptual learning gains, revealing that exceptional outcomes are achieved either through a balanced mix of these activities or by dedicating 30% or more of class time to group worksheets, while noting that active learning strategies without group worksheets yield results comparable to traditional lectures.
Based on a decade of institutional data, this study reveals that physics suffers from the lowest enrollment, highest attrition, and lowest female participation among STEM disciplines, attributing these inequitable outcomes to cultural stereotypes, uninspiring introductory courses, and a lack of intentional retention efforts.
This study demonstrates that the validity of LLMs as judges in physics assessments is primarily determined by the criterion-referenceability of the task format—achieving high accuracy and discriminative validity for structured questions and plots with explicit grading criteria, while failing to reliably discriminate quality in open-ended essays regardless of model capability or prompt engineering.
This paper presents a fully reproducible demonstration of an AI-assisted scientific workflow on canonical physics and mathematics benchmarks, illustrating how contemporary AI can effectively serve as a trustworthy copilot for derivation, implementation, and validation when guided by human oversight and rigorous verification.
This paper presents a low-cost, robust, and compact experimental setup using dual-channel cross-correlation sampling with standard electronic components to accurately measure the Boltzmann constant and elementary charge via thermal and shot noise, making it an ideal educational tool for university and high school laboratories.
This paper proposes a geometric framework that reframes the Bernstein-Vazirani algorithm as a classical linear computation performed in a rotated basis, thereby distinguishing it from genuinely quantum, entanglement-generating circuits and offering a new pedagogical perspective on quantum advantage and the Gottesman-Knill theorem.
This study analyzes interviews with six upper-division physics majors to reveal that while they view generative AI as a useful learning resource, they remain skeptical of its accuracy and primarily conceptualize their own creativity within the "mini-c" and "little-c" frameworks of the Four C Model.