Physics

We explore the idea of integrating machine learning (ML) with high performance computing (HPC)-driven simulations to address challenges in using simulations to teach computational science and engineering courses. We demonstrate that a ML surrogate, designed using artificial neural networks, yields predictions in excellent agreement with explicit simulation, but at far less time and computing costs. We develop a web application on nanoHUB that supports both HPC-driven simulation and the ML surrogate methods to produce simulation outputs. This tool is used for both in-classroom instruction and for solving homework problems associated with two courses covering topics in the broad areas of computational materials science, modeling and simulation, and engineering applications of HPC-enabled simulations. The evaluation of the tool via in-classroom student feedback and surveys shows that the ML-enhanced tool provides a dynamic and responsive simulation environment that enhances student learning. The improvement in the interactivity with the simulation framework in terms of real-time engagement and anytime access enables students to develop intuition for the physical system behavior through rapid visualization of variations in output quantities with changes in inputs.

Teaching cosmology at the undergraduate or high school level requires simplifications and analogies, and inevitably brings the teacher into contact with at least one of the pedagogical interpretations of the expanding universe. The by far most popular interpretation holds that galaxies in an expanding universe are stationary, while space itself expands and thus causes the growing distances that characterize cosmic expansion. The alternative relativistic explosion interpretation regards cosmic expansion as a pattern of (relativistic) galaxy motion. The aim of this article is to discuss the two competing interpretations from the perspective of potential student preconceptions, taking into account both beneficial anchoring conceptions and potentially harmful preconceptions that can lead to misconceptions.

We present a framework for investigating effective dynamics of SU(3) color charge. Two- and three-body effective interaction terms inspired by the Heisenberg spin model are considered. In particular, a toy model for a three-source "baryon" is constructed and investigated analytically and numerically for various choices of interactions. VPython is used to visualize the nontrivial color charge dynamics. The treatment should be accessible to undergraduate students who have taken a first course in quantum mechanics, and suggestions for independent student projects are proposed.

Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes,FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.

Introductory electricity and magnetism lab manual was designed to use with virtual Physics II class. The lab manual consists of experiments on electrostatics, electric potential and energy, current and resistance, DC circuits, electromagnetism, and AC circuits. Virtual experiments were based on simulations. Open educational resources (OER) were used for all experiments. Virtual experiments were designed to simulate in-person physical lab experiments. Special emphasis was given to computational data analysis with excel. Formatted excel sheets per each lab were given to students and step by step calculation in excel were explained during the synchronous class. Learning management system (LMS) was used to fully web enhance the lab class. Virtual labs were delivered by using live video conference technology and recorded lab sessions were added to LMS. Lab class were tested with both virtual delivery methods (synchronous and asynchronous). Student learning outcomes (understand, apply, analyze and evaluate) were studied with detailed lab reports and end of the semester lab-based written exam which confirmed the virtual lab class was as effective as the in-person physical lab class.

Recognizing the underlying relationship between e-learning practice and the institutional environments hosted in, the Chinese educational practice on branching high school students into science, technology, engineering, and mathematics (STEM) and non-STEM academic major groups before being admitted into universities or colleges is examined. By extending the well-established Technology Acceptance Model (TAM) with computer self-efficacy, this study aims to examine the difference in perceptions and behaviours on e-learning adoption from the STEM and non-STEM students. The results revealed that STEM score of computer self-efficacy, perceived ease of use and behavioural intention to use e-learning are all greater than non-STEM.

Traditionally, scholars in physics education research pay attention to students solving well-structured learning activities, which provide restricted room for collaboration and idea-generation due to their close-ended nature. In order to encourage the socialization of information among group members, we utilized a real-world problem where students were asked to generate a well-structured physics task, and investigated how student groups collaborated to create physics problems for younger students at an introductory physics course at a university in northern Chile. Data collection consists of audio recording the group discussions while they were collaborating to develop their physics problems as well as the solutions they created to their problems. Through interviews, we accessed students' perceptions on the task and its challenges. Results suggest that generating problems is an opportunity for students to propose ideas and make decisions regarding the goals of the problem, concepts and procedures, contextual details and magnitudes and units to introduce in their generated problems. In addition, we found evidence that groups tested the validity of their creations by engaging in strategies often observed with algebra-based physics problems, such as mathematical procedures and qualitative descriptions of the physics embedded in the problem, yet groups invested more time with algebra-based strategies compared to more qualitative descriptions. Students valued the open-ended nature of the task and recognized its benefits in utilizing physics ideas into context, which in turn enabled collaboration in a way not experienced with traditional algebra-based problems. These findings support the use of generative activities as a pathway for students to engage in real-world physics problems that allow for a range and variety of collective processes and ideas.

Despite limiting access to applicants from underrepresented racial and ethnic groups, the practice of using hard or soft GRE cut-off scores in physics graduate program admissions is still a popular method for reducing the pool of applicants. The present study considers whether the undergraduate institutions of applicants have any influence on the admissions process by modelling a physics GRE cut-off score with application data from admissions offices of five universities. Two distinct approaches based on inferential and predictive modelling are conducted. While there is some disagreement regarding the relative importance between features, the two approaches largely agree that including institutional information significantly aids the analysis. Both models identify cases where the institutional effects are comparable to factors of known importance such as gender and undergraduate GPA. As the results are stable across many cut-off scores, we advocate against the practice of employing physics GRE cut-off scores in admissions.

We report on the study of student difficulties regarding heat engine in the context of Stirling cycle within upper-division undergraduate thermal physics course. An in-class test about a Stirling engine with a regenerator was taken by three classes, and the students were asked to perform one of the most basic activities---calculate the efficiency of the heat engine. Our data suggest that quite a few students have not developed a robust conceptual understanding of basic engineering knowledge of the heat engine, including the function of the regenerator and the influence of piston movements on the heat and work involved in the engine. Most notably, although the science error ratios of the three classes were similar ( ∼ 10\%), the engineering error ratios of the three classes were high (above 50\%), and the class that was given a simple tutorial of engineering knowledge of heat engine exhibited significantly smaller engineering error ratio by about 20\% than the other two classes. In addition, both the written answers and post-test interviews show that most of the students can only associate Carnot's theorem with Carnot cycle, but not with other reversible cycles working between two heat reservoirs, probably because no enough cycles except Carnot cycle were covered in the traditional Thermodynamics textbook. Our results suggest that both scientific and engineering knowledge are important and should be included in instructional approaches, especially in the Thermodynamics course taught in the countries and regions with a tradition of not paying much attention to experimental education or engineering training.

Conceptual inventory surveys are routinely used in education research to identify student learning needs and assess instructional practices. Students might not fully engage with these instruments because of the low stakes attached to them. This paper explores tests that can be used to estimate the percentage of students in a population who might not have taken such surveys seriously. These three seriousness tests are the pattern recognition test, the easy questions test, and the uncommon answers test. These three tests are applied to sets of students who were assessed either by the Force Concept Inventory, the Conceptual Survey of Electricity and Magnetism, or the Brief Electricity and Magnetism Assessment. The results of our investigation are compared to computer simulated populations of random answers.