Physics education research (PER) has shown substantial growth in recent years, yet comprehensive reviews mapping the field’s overall trajectory remain scarce. The leading journal, Physical Review Physics Education Research (PRPER), has been a cornerstone of the discipline since its launch in 2005. Marking its 20th anniversary, this study presents a comprehensive bibliometric analysis of PRPER publications from July 2005 to June 2025. Using data extracted from the Web of Science and Scopus, this study examines key indicators, including publication/citation trends, contributor demographics, prolific author characteristics, and collaborations, alongside the field’s intellectual structures and their evolution. The results reveal sustained growth in publication volume and a concentration of authorship within U.S.-based institutions. The analysis finds that PRPER publications group well into seven major research clusters: Understanding and Supporting Student Learning; Identity and Equity; Conceptual Inventory; Physics Laboratory; Physics Problem Solving; Attitude and Epistemology; and Quantum Physics Education. Notably, Quantum Physics Education emerges in this analysis as one of the seven distinct major research clusters identified through bibliographic coupling. Timeline analysis reveals a shift in thematic emphasis from foundational work on conceptual assessment toward increasing focus on identity, equity, and quantum education. This bibliometric overview not only traces the intellectual trajectory of PRPER but also offers insights into the broader evolution of PER, highlighting existing gaps, such as the need to integrate equity research into quantum education, and suggesting future directions for the field.

Computation has become an integral part of physics research. However, little is known about how students learn to productively use computation as a tool beyond the introductory level, especially as they transition into physics research. In this study, we apply the theory of physics computational literacy and the novice-expert framework to describe the development of expertise in computational physics, as students transition from novice to journeyman computational physicists. We base this description on a thematic analysis of interviews with 13 computational physics master’s students with extensive experience using computation. We first describe the most important elements driving the development of computational physics expertise, identifying two distinct transitions of competence during their studies, driven by experience with large computational projects and professional research. We then present an overview of the various skills students attain on this path toward the journeyman level of computational physics expertise. Based on these results, we argue for the need to assist students in collaborative coding and in the learning of new tools, as well as for the importance of large, scaffolded, computational projects in helping students develop the advanced skills needed for computational research.

For several years, we have been investigating student conceptual understanding of radioactivity, particularly regarding half-life. Although it is considered to be random when an individual nucleus undergoes the sudden process of radioactive decay, the duration of time within which half of a radioactive sample decays (i.e., the half-life) is a predictable quantity. Many students generally struggle with the idea that random behavior of individual agents can give rise to predictable patterns in the collective; specific to radioactivity, many students have said both in interviews and surveys that, if you are looking at an individual nucleus, half of the nucleus will have decayed after one half-life. Our findings have indicated, however, that this idea (of individual nuclei decaying in a predictably continuous manner) is often not a robust and intact mental structure; rather, in other contexts, the same students correctly discuss radioactive decay as being instantaneous and unpredictable. We developed a survey to measure this context dependency and administered it both to high school students in Austria and to college students at the University of Alaska Fairbanks. Using both cluster analysis and alluvial diagrams to organize our data, we find a great variety in answering patterns across the survey prompts, indicating that student ideas have not yet crystallized. Our paper thus provides experimental evidence in support of a “knowledge in pieces” model of student cognition about radioactivity as opposed to a “misconceptions” model.

The rapid growth of quantum information science and technology (QIST) in the 21st century has created both excitement and uncertainty about the field’s trajectory. This qualitative study presents perspectives from leading quantum researchers, who are educators, on fundamental questions frequently posed by students, the public, and the media regarding QIST. Through in-depth interviews, we explored several issues related to QIST including the following key areas: the current state of quantum computing in the noisy intermediate-scale quantum (NISQ) era and timelines for fault-tolerant quantum computers, including a possible timeline for quantum advantage on Shor’s factoring algorithm, the feasibility of personal quantum computers in our pockets, and promising qubit architectures for future development. Our findings reveal diverse yet convergent perspectives on these critical issues. While experts agree that the current machines with physical qubits that are being built in the NISQ era should be called quantum computers, most estimated that it will take a decade to build a small fault-tolerant quantum computer, and several decades to achieve scalable systems capable of running Shor’s factoring algorithm with quantum advantage and some stated that they would find it exciting if some law of physics prevents achieving those ultimate goals. Regarding carrying a quantum computer in the pocket, experts viewed quantum computers as specialized tools that will remain in central locations such as data centers and can be accessed remotely for applications for which they are particularly effective compared to classical computers. Quantum researchers suggested that multiple platforms show promise, including neutral atoms, superconducting circuits, semiconducting qubits, and photonic systems, with no clear winner emerging. These insights provide valuable guidance for educators, policymakers, and the broader community in establishing realistic expectations for developments in this exciting field. Our findings can provide valuable information for educators to clarify student doubts about these important yet confusing issues related to quantum technologies at a time we are celebrating the International Year of Quantum Science and Technology.

Upper-level, undergraduate quantum mechanics (QM) is widely considered a difficult subject with many varied approaches to teaching it and considerable variation in content coverage. For example, two common approaches to undergraduate QM instruction are a spins-first approach, which focuses on the postulates of QM in spin systems before discussing wave functions, and a wave functions-first approach, which focuses on the Schrödinger equation and its solutions for continuous functions in various potentials before discussing spin. These different approaches, along with the content variability in the textbooks used by instructors, may mean students learn different things in QM classes across the United States (U.S.). In this paper, we offer a characterization of QM courses based on survey responses from instructors at institutions across the U.S. With the responses of 76 instructors teaching QM courses (or sequences), we present results detailing their teaching methodologies, use of pedagogical resources, and coverage of QM topics. We find that the plurality of instructors in our sample are using traditional lecture, but many instructors are using interactive lecture or another nontraditional method. Additionally, instructors are using a wide variety of pedagogical tools (e.g., clicker questions). Many instructors in our sample reported teaching single-semester (or single-quarter) QM courses; these instructors report similar content coverage to instructors teaching first-semester (or first-quarter) QM courses, though their responses showed greater variability. We additionally report a comparison of content coverage between instructors using wave functions-first vs spins-first approaches, finding a large degree of overlap with differences in coverage for a few specific topics. These findings can help inform both future research and instructional efforts in QM education.

Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students’ mechanistic sensemaking, working from a coding scheme that is aligned with previous work in physics education research (PER) and that is amenable to recently developed ML classification strategies using language encoders. We describe pilot tests of the tool, in three versions with different language encoders, to analyze sensemaking evident in college students’ written responses to brief conceptual questions. The results show, first, that the tool’s measurements of sensemaking can achieve useful agreement with a human coder, and, second, that encoder design choices entail a tradeoff between accuracy and computational expense. We discuss the promise and limitations of this approach, providing examples of how this measurement scheme may serve PER in the future. We conclude with reflections on the use of ML to support PER research, offering cautious optimism for strategies of codesign between PER and ML.

In the rapidly evolving interdisciplinary field of quantum information science and technology, a major obstacle is the need to understand advanced mathematics to solve complex problems. Current findings in educational research suggest that incorporating visualizations into problem-solving settings can have beneficial effects on students’ performance and cognitive load compared to relying solely on symbolic problem-solving content. Visualizations like the (dimensional) circle notation enable us to represent not only single-qubit but also more complex multiqubit states, entanglement, and quantum algorithms. In this pilot study, we aim to take an initial step toward identifying the contexts in which students benefit from the presentation of visualizations of single- and multiqubit systems in addition to mathematical formalism. For this purpose, we propose a set of test items and a comprehensive methodology to assess students’ performance and cognitive load when solving problems. This is a pilot investigation with a large breadth of questions intended to generate hypotheses and guide larger-scale, more focused studies in the future. Specifically, we compare two approaches: using the mathematical-symbolic Dirac notation alone and using it in combination with the (dimensional) circle notation. In surveys in one-, two- and three-qubit systems, we gather qualitative data from five, five and two think-aloud interviews, identifying problems that students encounter and their problem-solving strategies. In addition, we analyze quantitative data (performance and cognitive load) from 23, 27, and 17 participants in surveys on one-, two-, and three-qubit systems recruited mainly from our quantum computing lectures. We find that most of the test items are appropriate for a heterogeneous target group, as they can differentiate between participants in terms of performance and time taken. In general, the A-B crossover structure of the study is suitable for investigating the benefits of visualization for problem solving with regard to length and feasibility. Future studies should, however, be narrower in scope, given the observable dependence on student characteristics and context, with particular interest lying in the further investigation of the Hadamard gate, the cnot gate, and entanglement in multiqubit systems.

In a data-driven world, the ability to critically assess empirical data is a core aspect of scientific literacy and a major goal of physics education. Yet, students often fall back on heuristics that conflict with statistical reasoning. A common example is the “the more measurements, the better the quality of the data” (MMB) heuristic, which naively equates quantity with quality, ignoring variance. According to conceptual change theory, such heuristics or misconceptions coexist with formal scientific knowledge and are automatically triggered during reasoning, resulting in conflicts and requiring active suppression. Inhibitory control, an executive function that overrides automatic responses, may be the central key to resolving these conflicts; however, its role in procedural domains, such as data literacy, is understudied. This study examines whether and how inhibitory control facilitates students to overcome the MMB heuristic in different representational formats (tabular vs pictorial). Using a modified Stroop paradigm, undergraduate physics students ($N=30$) evaluated data quality presented in tabular (numerical tables) and pictorial (dot plots) formats across the congruent, incongruent, and neutral stimulus types. Results showed significant accuracy reductions in incongruent trials compared to both neutral and congruent trials across both formats, indicating significant Stroop effects. For the response times, we found a significant Stroop effect (longer response time in the incongruent than congruent trials) in the pictorial format, while a similar but non-significant trend in the tabular format. These findings first indicated the involvement of inhibitory control when the heuristic activation occurred in both formats. However, the pictorial format may evoke stronger or more persistent heuristic conflict, or the potential higher cognitive processing demands in the tabular format may mask the subtle RT-based Stroop effects. These findings extend the conceptual change research by identifying inhibitory control as crucial for resolving heuristic interference in procedural reasoning, and they stress the need to consider how representational formats modulate both heuristic activation and cognitive processing demands when designing instruction in physics.

Students in a spins-first quantum mechanics course often have difficulties transitioning from spin systems to continuous systems like the infinite square well. Our study investigates how students understand discrete and continuous quantities in quantum mechanics and the connections the students make between them. We interviewed six students who were concurrently enrolled in a junior-level spins-first quantum mechanics course with a computational lab. During the interview, students organized a set of 20 cards containing various quantum mechanics concepts and representations, including snippets of python code. Using a phenomenographic approach, we generated four themes: understandings of discrete, understandings of continuous, discreteness of representations, and discrete approximations of integrals. Participants often related discrete and continuous quantities in the context of code, suggesting that computational activities can support students in transitioning from discrete systems to continuous ones.

As quantum technologies increasingly impact society and the workforce, there is a growing need to integrate quantum physics (QP) education into all levels of the curriculum, including secondary education. However, despite increasing interest, there is little consensus on which topics should be covered and to what depth. This study addresses this issue by exploring experts’ perspectives on the future of QP education at the secondary school level. Using an adapted Delphi method, we conducted three rounds of surveys with a diverse panel of quantum physics and physics education experts. The study aimed to identify the most appropriate QP concepts for inclusion in secondary school curricula and the instructional level at which they should be taught. The findings show strong expert support for including foundational QP topics, such as quantum interference, superposition, and quantum measurement, taught at a conceptual rather than a mathematical level. More advanced topics related to quantum technologies were widely deemed unsuitable for the secondary school. These results are consistent with existing national curricula and provide valuable insights into current perspectives on QP education, reinforcing the importance of core quantum physics concepts in secondary education. These insights lay the groundwork for further research into the future direction of quantum education and support the integration of quantum technologies into secondary school curricula.

This paper presents a project-based learning (PjBL)/science, technology, engineering, and mathematics (STEM)-based instructional model for eigth-grade school students that integrates reverse engineering exercises and explores energy use and its transformations. The instructional model was implemented in a public school in Brazil with students aged 13–14. The results suggest that this integration has the potential to promote meaningful learning and improve scientific literacy, as defined by PISA. Furthermore, the findings highlight how simple and adaptable the integration of PjBL/STEM-based and reverse engineering (RE) exercises can be in various school contexts.

This paper explores the potential of large language models to accurately extract and translate equations from typed student responses into a standard format. This is a useful task as standardized equations can be graded reliably using a computer algebra system or a satisfiability modulo theories solver. Therefore physics instructors interested in automated grading would not need to rely on the mathematical reasoning capabilities of language models. We used two novel frameworks to improve the translations. The first is consensus where a pair of models verify the correctness of the translations. The second is a neurosymbolic LLM-modulo approach were models receive feedback from an automated reasoning tool. We performed experiments using responses to the Australian Physics Olympiad exam. We report on results, finding that no open-source model was able to translate the student responses at the desired level of accuracy. Future work could involve breaking the task into smaller components before parsing to improve performance or generalizing the experiments to translate hand-written responses.

Recognizing the complex environmental challenges that humanity faces, there is a growing need to develop critical thinking skills specifically oriented toward understanding and addressing climate change. To facilitate the assessment of these skills, a critical thinking in climate change (CTCC) test was developed. In the first step, 16 semistructured interviews were conducted with high school and university students. Drawing on insights from these interviews alongside previous research, a first version of the CTCC test comprising 41 items was administered to a sample of $\mathit{N}=289$ high school and university students from Austria, Belgium, and Germany. The test development process and subsequent evaluation were performed using Rasch modeling to determine test functioning. Through an iterative process that included three rounds of testing involving a total number of $\mathit{N}=809$ students and analyses, the test items were revised. The final version of the CTCC test consists of 24 items. Results from Rasch analysis suggest that the test has high item reliability (0.96) and person reliability (0.80), making it a useful tool for assessing critical thinking skills among high school graduates and first-semester university students.

This study systematically explores the thematic evolution of articles in The Physics Teacher and Physics Education journals during a critical period in modern history, from the Cold War era to the prepandemic world (1966–2019). To contextualize the analysis, we first present an overview of historical events and developments that have characterized physics education in the United States and Europe since the 1950s. Using a natural-language-processing-based inductive topic modeling approach, we then identify recurring themes that have shaped physics education literature, including content-based topics, teaching methodologies, laboratory practices, curriculum development, and the influence of physics education research (PER). Our findings reveal both overarching trends and distinct thematic preferences between the journals. Physics Education has historically emphasized curriculum structures, social aspects of education, and interdisciplinary connections, whereas The Physics Teacher has focused more on pedagogical strategies, demonstrations, and practical teaching tools. In the last three decades, both journals have increasingly incorporated discussions on technology, computation, and PER-driven instructional practices. By tracing these shifts over five decades, this study provides a broader perspective on how physics education has responded to changing educational priorities, technological advancements, and research developments.

This work reports results from the first national study of the impact of student facilitation of informal physics outreach programs on their physics identity, sense of belonging, and essential career skill development. Drawing on results from studies at a single institution with a well-established physics outreach program, a national survey was developed and distributed through the Society of Physics Students’ network to more than 5000 individuals. A total of 704 responses were received. Responses to closed-form questions on the survey were analyzed descriptively and using multiple regression analysis to evaluate the relationship between student participation in informal physics outreach programs and the constructs of interest. Results show that students’ engagement in outreach was linked to increased confidence in communicating their physics knowledge, as well as to the development of key career skills. Additionally, facilitating informal physics outreach programs was a modest but significant predictor of a stronger growth mindset and a greater sense of belonging within the field, which are known to contribute to student persistence in their major.

Improving students’ views of experimental physics is often an important goal of undergraduate physics laboratory courses. However, traditional lab courses typically include highly guided activities that often do not require or encourage students to engage in the authentic process of experimental physics. Alternatively, open-ended activities in lab courses can provide students with a more authentic learning experience. Here, we investigate the correlation of open-ended activities in lab courses with students’ views of experimental physics, using an expanded database of the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS) survey responses from over 30,000 students at over 100 institutions. We show that the inclusion of some open-ended activities is associated with more expertlike postinstruction responses relative to the courses that include only traditional guided activities, and the effect is larger for students with low preinstruction scores. We also find that for courses with at least 1 week spent on open-ended activities, the number of weeks (above zero) spent on open-ended activities is not associated with a pre- to post-instruction gain in the E-CLASS scores. This result suggests that possibly including even a small amount of time on open-ended activities can improve students’ views of experimental physics, which lowers the barrier for instructors to implement such components. These results also suggest that additional research should be conducted to better understand and categorize lab environments beyond simple definitions of inquiry, so as to understand the possible causation mechanisms of labs on students’ views of experimental physics.

Grounded in constructionist learning theory, Maker education facilitates knowledge building through the construction of personally meaningful physical artifacts. Maker education has shown potential to promote learning, engagement, identity development, and interest in science, technology, engineering, and mathematics (STEM), with most evidence coming from informal and after-school spaces or elective classes. A review of research on Making in formal STEM education identified positive effects, most often in knowledge of electric circuits. Research on Maker teacher preparation, however, is limited. This 5-year study, situated in a university physics class for preservice and in-service STEM teachers and future teacher educators, explored the effects of adding a Maker component to the course. There was no statistically significant difference between the Maker and non-Maker groups on a common conceptual learning assessment. Qualitative analysis, however, provided evidence that the Maker project enabled activation of conceptual knowledge, enhanced student engagement, legitimated students’ funds of knowledge, and revealed affordances of Maker learning to future teachers.

Understanding how learners interpret visual representations of invisible scientific phenomena is crucial for teaching and designing effective instructional materials. This study investigated which visualizations of infrared radiation, radiation absorption, and molecules are perceived as most intuitively comprehensible by students and adults. Grounded in dual process theories of cognition and cognitive load theory, we hypothesized that intuitive visualizations increase cognitive fluency, thereby reducing extraneous cognitive load. A total of $n=377$ students aged 14–15 and $n=100$ adults participated in an online survey, selecting the visualization they found most convincing for each of the scientific concepts. The visualizations were developed based on prior physics education research, and participants’ preferences were analyzed using Bayesian model comparison. Across all topics, certain visualizations—especially those developed in prior domain-specific research—were strongly preferred, suggesting that intuitive comprehensibility can be systematically supported through thoughtful visual design. Differences between student and adult preferences highlighted the role of prior knowledge and visual familiarity, as adults were clearer in their choices. The findings contribute to a systematic investigation of visual representations used in science education and science communication and offer practical guidance for developing intuitive visualizations in instructional contexts.

Prior research has demonstrated that students’ performance on physics test items can be accurately predicted using machine learning algorithms based on their gaze behavior. These gaze data are typically recorded during item completion and capture students’ visual attention to both the verbal item stem and accompanying visual representations, such as graphs. This predictive capability highlights the potential of machine learning as a tool for the development of AI-based adaptive learning systems, such as intelligent textbooks, for improving personalized learning. With this study, we aim to replicate the findings on the predictive power of gaze data recorded during test completion in the field of kinematics and extend them by examining whether gaze data collected during a preceding learning phase can similarly predict performance on the test items. The study involved 88 high school students who first engaged with instructional material on kinematics and then completed test items from the test of understanding graphs in kinematics, a test aimed at assessing students’ conceptual understanding of kinematic graphs. Eye-tracking data were recorded both while students engaged with the learning materials and while they worked on the test items presented after each slide. To analyze these data, we used a support vector machine to predict whether students would answer the test items correctly. We compared prediction accuracies based on gaze data recorded during the learning phase with those based on gaze data recorded during the test phase. The findings indicate that eye-tracking data gathered during the completion of test items were more accurate in predicting students’ performance than data collected during the learning phases. Notably, paying close attention to graphs was one of the key behaviors that distinguished students who answered correctly from those who did not. Students’ gaze behavior on the graph was also most predictive of students’ performance. These findings highlight the potential of gaze-based adaptive systems and the importance of including graphical representations in personalized learning environments. They also suggest that future research should explore how intelligent textbooks can combine visual representations, such as graphs, with short conceptual questions to optimize real-time adaptive support.

Social network analysis has become a common methodology within physics education research to quantify the impacts that student interactions and classroom structure have on student outcomes. These relationships are typically quantified using a degree centrality metric based on the number and range of connections a student has, such that a higher degree centrality would indicate a more central position in the network. However, when looking at these interdependent metrics as independent parameters, they do not consistently show any statistically significant relationship with motivational outcomes like self-efficacy or sense of belonging. In this study, we instead characterize students’ network positionality using various “network actor roles” and measure the strength of their connections through network paths, within the context of social capital. In this framing, we are less interested in the impact and importance of individuals than we are in the overall social integration of most students in the class. Using data from an interactive calculus-based physics 1 class, we find that students who are network isolates (no incoming or outgoing connections) or weakly connected report a lower sense of belonging compared with other students, but, consistent with prior research, the number of connections was not correlated with sense of belonging. Similarly, students who were strongly connected to at least one other person reported a higher sense of belonging compared to those who were weakly connected. This indicates the potential for graph theoretical methods to enhance our understanding of student interactions in physics classrooms.