Physics demonstrations are celebrated for enlivening lectures and anchoring understanding, yet remarkably little is known about how the choice of demonstration materials, namely, familiar household objects versus specialized laboratory equipment, may shape students’ affective evaluations. A total of 288 secondary and college students observed two resonance demonstrations—one using wine glasses as everyday focal objects and one using tuning forks as laboratory focal objects—and completed a survey requesting an overall preference (household/lab/no preference) and Likert ratings on seven perception items for each demonstration. Demonstration order was counterbalanced across class sessions to guard against sequence effects. Overall, students far more often preferred the wine-glass demonstration (58.3%) over the tuning-fork demonstration (14.9%), with 26.7% reporting no strong preference; this preference pattern was consistent across both secondary and college students and did not differ significantly between male and female students. Preference distributions were also independent of presentation order. Paired comparisons showed that the household demonstration was rated significantly higher in interestingness, novelty, relevance to daily life, and its ability to stimulate interest in science. Importantly, clarity of the phenomenon and self-reported support for understanding resonance did not differ significantly between the two demonstrations, supporting the interpretation that the preference for the household condition is not attributable to the phenomenon being easier to observe or the concept easier to grasp. The results are theoretically grounded in cognitive load and situated cognition frameworks to explain how material-based contextualization may enhance student engagement without increasing perceived cognitive burden. These findings suggest that a seemingly simple instructional design choice, i.e., using a recognizable household object as the focal material in a physics demonstration, can meaningfully enhance student engagement and perceived relevance without sacrificing conceptual clarity.

Empirical research on whether offering history of science electives at the university level enhances students’ understanding of the nature of science (NOS) remains scarce. This study aims to investigate the impact of enrolling in a history of science course on undergraduates’ understanding of the nature of science. In this study, the history of physics elective employed three distinct instructional methods: conventional lecture-based teaching, lecture-based teaching combined with explicit instruction and individual reflection, and lecture-based teaching combined with explicit instruction and group reflection. Teaching effectiveness was assessed by administering the SUSSI questionnaire to 160 participants before and after the course, supplemented by cognitive network analysis of students’ reflective reports on the nature of science within the authentic context of the COVID-19 pandemic. A randomly selected control group of non-STEM majors who did not enroll in the course was included for comparison. Results demonstrated that all three instructional models significantly improved students’ NOS conceptions across multiple dimensions, with the explicit instruction and group reflection approach showing optimal comprehensive effectiveness. However, none of the instructional models produced a statistically significant improvement in students’ understanding of the scientific laws and theories dimension.

Mathematical descriptions are the focus of quantum physics education research, as quantum physics is inherently described by its mathematical structures. However, there is a lack of research regarding the role of mathematical descriptions for understanding fundamental principles of quantum physics in teacher preparation and training. In order to investigate how preservice teachers and in-service teachers assess and understand the mathematical formalism in quantum physics, an explorative interview study was conducted. Therefore, we developed a teaching-learning sequence employing a reduced Dirac formalism in the context of single-photon polarization serving as an exemplary two-state system. In that respect, the focus was on two mathematical representations. The ket notation was used as an algebraic representation in connection with the Bloch sphere as a graphical representation of quantum states. On that basis, semistructured and material-based interviews with $N=15$ preservice teachers and $N=15$ in-service teachers were conducted. The data suggest that the reduced Dirac formalism is well understood and accepted by both preservice teachers and in-service teachers. However, some in-service teachers raise critical concerns about the abstract nature of the mathematical formalism, pointing to a perceived lack of experimental or phenomenological connection. The ket notation is valued not only for its communicative clarity and its role as a language but also for its structural role. By comparison, the Bloch sphere is met with more cautious acceptance—yet its use in visualizing experiments and measurement processes is positively assessed.

Many organizations and researchers within the physics community have highlighted the need to improve undergraduate programs in areas such as pedagogy and the representation of marginalized students. Although many physics programs have actively attempted to address these challenges, the impact of these efforts has yet to be reflected broadly in the physics community. Our previous work highlighted standard cultural practices around the approach of physics faculty to educational change. We found that physics faculty often initiate educational change in an individualistic way that is rushed and ad hoc, while ignoring the use of formal evidence. To address this shortcoming, disciplinary organizations have developed initiatives like the Departmental Action Leadership Institute (DALI). In particular, DALI was developed to support physics faculty in becoming better change agents and learning effective leadership and collaboration practices to pursue educational change in their local programs. In our investigations of this institute, we developed case studies of five DALI physics programs. Each case study program followed the DALI recommendations and built teams following the Departmental Action Team (DAT) model to pursue local change. In this project, we studied and documented the emerging idioculture within these five DATs. We used a multiple case study methodology and collected data from multiple sources. We found that the emerging idioculture was different from the departments’ dominant cultures in ways that support collaborative approaches to educational change. We also found that these DATs take a deliberate and strategic approach to the change effort by taking time to consider the context and feasibility of the change effort. Finally, we found that these DATs center formal evidence and students’ voices in their decision-making processes. We present the results and discuss how the emerging idioculture in these DATs is situated within dominant departmental cultures of educational change.

Developing expertlike problem-solving skills is a central goal of undergraduate physics education. In this study, we investigate the impact of teaching explicit problem-solving frameworks, combined with deliberate practice, on students’ problem-solving approaches. Using multidimensional scaling to analyze students’ decision-making patterns, we compare the similarity of students taught with these methods to physics experts and to students taught with traditional repeated practice. Our results show that students who received structured frameworks and targeted feedback through deliberate practice exhibited problem-solving behaviors significantly more aligned with those of experts. These findings suggest that pedagogies emphasizing explicit strategy instruction and feedback are more effective than rote repetition for fostering expertise. We recommend the integration of these approaches into physics curricula to better support the development of skilled and adaptive problem solvers.

Energy is a central idea in physics, but students often learn energy as a set of loosely connected terms, equations, and conservation statements rather than as an integrated explanatory framework. Prior physics education research (PER) has shown that students have persistent difficulty identifying appropriate systems, tracing energy changes across systems and forms, and applying energy conservation in unfamiliar contexts. Drawing on the knowledge integration perspective and on research on systems-based energy reasoning, this study developed a conceptual framework for a ninth-grade energy unit in which the work and energy relation served as the central organizing relation linking energy change, energy conservation, and contextualized problem solving. We implemented a conceptual-framework-based instructional intervention and compared it with regular textbook-sequence instruction in a quasi-experimental study involving two intact ninth-grade classes in China. Both groups studied the same curriculum content for the same amount of instructional time and were taught by the same teacher. Analysis of covariance showed that, after controlling for pretest performance, the intervention group significantly outperformed the control group on the overall post-test score and on the three conceptual categories of energy change, energy conservation, and the work and energy relation. Statistical mediation analyses further showed that the intervention group’s post-test advantage was associated with students’ performance on the work and energy relation. In the post-test mediation model, this indirect association was larger for atypical context performance than for typical context performance. These findings provide classroom-based evidence that reorganizing energy instruction around a core conceptual relation can support students’ integrated understanding and application of energy ideas. The study contributes to PER by extending conceptual-framework-based instruction to a broader secondary energy unit and by clarifying how a work-centered organization may support reasoning beyond familiar surface features.

The emergence of generative artificial intelligence (GenAI) marks a potential inflection point in the way academic information is accessed, raising fundamental questions about the evolving role of search in student learning. This study examines this shift by analyzing longitudinal trends in physics-related search and page-view activity, using declines in traditional search behavior as a quantitative proxy for changes in independent information-seeking practices. We analyze Google Trends data for core concepts in classical mechanics, electromagnetism, and optics across three academic years (2022–2025) in more than 20 countries and complement this analysis with Wikipedia page-view data across seven major languages to establish platform independence. The results reveal a substantial, systematic, and persistent global decline in search and page-view activity across most examined physics topics. The magnitude of this decline is domain-dependent, with mechanics-related content showing sharper and more consistent reductions than content related to electromagnetism and optics. Pronounced geographic and linguistic heterogeneity is observed: while English-speaking regions show relative stability or only moderate declines, non-English-speaking regions exhibit substantially larger reductions in traditional, search-based information-seeking activity. Despite the overall decrease in volume, the seasonal structure characteristic of academic activity remains robust. Taken together, these findings indicate a redistribution of physics-related information-seeking behavior in academic contexts where generative tools are increasingly available.

In this paper, the focus has been on students’ application of special relativity theory after learning it. In order to determine students’ understanding and conceptualization of the theory of special relativity, paradoxes, which are contradictory problems, have been used in the study. How students solved paradoxes related to the relativity of simultaneity, length contraction, and time dilation has been investigated, and an answer has been sought to the question “What is the state of undergraduate students who have learnt special relativity theory in applying the theory to paradoxes?” The students who participated in the study had previously attended Physics I, General Physics 2, and General Physics 3 courses in which classical physics topics (force and motion, electricity and magnetism, and optics and waves) were taught. The data were analyzed using a phenomenographic approach to identify the categories emerging from students’ responses. Most students experience difficulty in understanding the nature of special relativity and applying the theory to other situations. For this reason, they did not use the concept of the reference frame in solving the paradoxes. From the students’ responses, it is understood that they mostly think within a single reference frame and experience difficulty in understanding the contradictions that arise when viewed from different reference frames.

The growth of the quantum information science and engineering (QISE) industry has increased interest in how undergraduate programs prepare students for careers in this field. Prior research emphasizes the value of experiential learning as preparation for the quantum industry but lacks specificity regarding the experimental skills needed for positions available to bachelor’s degree graduates. In this study, we investigate the experimental skills associated with bachelor’s-level quantum industry positions through 44 semistructured interviews with quantum industry professionals. Guided by the American Association of Physics Teachers recommendations for the undergraduate physics laboratory curriculum, we characterize the experimental skills associated with positions described as requiring bachelor’s-level preparation and thematically synthesize them into four categories: instrumentation, computation and data analysis, experimental and project design, and communication and collaboration. We further examine how these skills cluster across role types and articulate them as learning goals to provide guidance for educators interested in aligning undergraduate instruction with the needs of students wanting to pursue a career in the quantum industry. Our findings suggest the need to emphasize the discussion of hardware in QISE theory courses, expand experimental training through instructional laboratories, and intentionally integrate professional skills in undergraduate QISE education.

The theoretical framework knowledge in pieces (KiP), proposed by Andrea diSessa, holds that intuitive knowledge in physics is organized through small cognitive structures called p-prims (phenomenological primitives). One of the heuristic principles on which the KiP framework is built—the principle of redescription—suggests that the descriptive framework of p-prims can be fine-tuned. However, the theoretical development of p-prim descriptions has remained largely limited to informal linguistic expressions drawn from students’ explanations, without a clearly defined syntactic or semantic structure. This lack of systematicity has led to variations in the naming and conceptualization of p-prims, making their consistent identification and analytical use difficult. In response to this limitation, this paper proposes a formal schematization of p-prims based on Fillmore’s Frame Semantics, supported by tools such as FrameNet. Our proposal aims to make explicit the core and peripheral components of each p-prim and to clarify the conditions under which they are evoked. By providing p-prims with a well-defined linguistic structure, the KiP framework is strengthened, opening new possibilities for the automated analysis of students’ reasoning using artificial intelligence (AI), as well as for the design of visual representations to support the development of instructional materials. Theoretical, methodological, and technological implications of this proposal for future research in physics education research (PER) are also discussed.

This paper presents the first iteration of development and classroom testing of Stars and Elements, a web-based learning resource for Norwegian upper secondary physics students focused on how heavy elements are produced in stars and neutron star mergers. Combining the model of educational reconstruction (MER) with the pedagogical link-making framework and design-based research (DBR), we identified core ideas to be communicated, reviewed the physics curriculum and research on student understanding and motivation, and formulated design hypotheses that guided development of a 90-min learning resource with animations, videos, interactive tasks, peer discussion, and teacher consolidation. Six classes ($N$ = 120) participated; data comprised students’ written responses to three open tasks, brief evaluations, and classroom observations. Most students could describe central concepts and processes concerning red giant stars and neutron star mergers as sites for heavy element production, notably the slow and rapid neutron capture processes, and many benefited from animations and varied task formats. Some displayed uncertain grasp of concepts such as fusion and beta decay. Nature of science (NoS) aspects concerning how physics knowledge is developed were found to be insufficiently represented. From these findings, we propose design principles for research-based learning resources on similar topics, including emphasizing explicit link-making across scales and prior knowledge, scaffolding for challenging nuclear concepts, multimodal visualizations, and explicit NoS focus. We argue that combining MER, link-making, and DBR is a promising approach for developing learning resources that reflect the frontiers of science while having a solid grounding in the classroom.

Learning progressions (LPs) are widely used to describe the long-term development of students’ understanding of core scientific ideas. However, as increasing attention has been paid to the context-dependent nature of student learning, the limitations of traditional LP models in capturing the complexity and dynamics of cognitive development have become more apparent. This study integrates the partial credit model (PCM) and model analysis to systematically investigate students’ cognitive developmental characteristics within the Force and Motion Learning Progression (FM-LP). Using Force Concept Inventory data from 1619 Chinese students, we repeated the study by Fulmer et al. on U.S. students to examine the applicability of the established FM-LP across Chinese student populations. The results indicate that the established FM-LP does not exhibit satisfactory model fit in the Chinese sample, providing further evidence that science learning is highly context- and population-dependent. To characterize students’ intermediate cognitive states, this study introduces a model-state combination analysis framework. The findings show that most students do not stably occupy a single LP level; instead, they display thinking patterns spanning multiple levels, with clear differences observed between middle school and high school students. Further analyses based on PCM reveal that, as students’ ability increases, their model states shift from strongly mixed states to less mixed states and eventually toward the relatively consistent use of the scientific conception. The model-state-based approach offers a new perspective on capturing cognitive complexity in learning progressions and provides important implications for progression-oriented assessment.

We report results from a mixed-methods survey study ($N=81$) examining how undergraduate students in introductory physics courses use, trust, and prefer to integrate artificial intelligence (AI) tools into their learning. Among respondents, 91% (95% confidence interval (CI): [83%, 96%]) reported using AI for physics coursework, yet only 41% (95% CI: [30%, 52%]) trusted AI-generated physics explanations—a 50-percentage-point trust-utility gap consistent with domain-calibrated skepticism rather than uncritical adoption. Thematic analysis of open-ended responses ($n=67$; Cohen’s $\kappa =0.78$) identified eight themes; the most distinctly physics-specific finding was that 40% of qualitative respondents spontaneously articulated whereAI fails in physics—visual-spatial reasoning, circuit analysis, and abstract physical reasoning—aligning with physics education research on student difficulties with multirepresentational tasks. This skepticism is also consistent with Kortemeyer’s benchmarking results, which show that AI performance is weakest on precisely the multirepresentational task types that students identified as failure-prone, suggesting that student trust calibration may track underlying AI competence boundaries in physics. A majority (65%) preferred optional over mandatory AI integration. Because high-performing students were overrepresented by 13.9 percentage points, the estimated preference for optional integration (and/or resistance to mandatory) may be inflated in this sample. These findings highlight a critical unresolved priority—the verification gap between self-reported and actual verification behavior—and inform recommendations for AI-integrated physics pedagogy.

The ability of ChatGPT, especially its latest versions, attracts significant attention from educators and researchers in fields such as physics. Several studies on this topic explore the performance of ChatGPT in answering physics questions across different versions of ChatGPT. In this study, we evaluate the development of ChatGPT in answering physics questions using the Mechanical Waves Conceptual Survey (MWCS) to examine whether its capabilities improve across different versions. A total of 75 responses per item are generated using ChatGPT versions 4, 4.o, and 5.2, resulting in 4950 responses for comprehensive analysis. Based on the comparison between the three versions of ChatGPT, we find that some difficult types of questions that are answered incorrectly by previous versions, as reported in earlier studies, can now be answered correctly, although there are still minor mistakes, especially when examining the reasoning. A generalized linear mixed model (GLMM) analysis, together with post hoc comparisons, indicates that ChatGPT 5.2 significantly outperforms both ChatGPT 4 and 4.o, suggesting that the capabilities of the latest version included in this study significantly improve. These findings highlight a future direction for this type of research, as our study shows that the latest version of ChatGPT can correctly answer more types of questions in MWCS.

Physics students’ use of ChatGPT or other generative AI tools is a topic broadly discussed in physics faculties around the globe, with discussion often outpacing evidence. What remains unclear is how physics students truly engage with these tools and by what perspectives their choices are shaped. This study develops a data-driven typology of how students perceive the tool’s usefulness and its risks. Through a qualitative content analysis of 1189 survey responses, followed by latent class analysis (LCA), we identified two distinct user profiles. The majority (70%) are “Pragmatic Users” who, while aware of the tool’s inaccuracies, use it for scaffolding tasks like conceptual clarification and finding starting points for problem-set questions. The minority (30%) are “Skeptical Non-Users,” who avoid the tool over concerns about overreliance and its potential to hinder independent problem-solving. These profiles show students making a calculated trade-off between usefulness and risk, suggesting one-size-fits-all policies on AI are inadequate. Thus, rather than promoting uniform adoption, a differentiated pedagogy could create space for students to make informed, self-determined choices—supporting reflective AI use for those who opt in, while equally validating and enabling the learning practices of those who choose not to use generative AI.

The present study describes the development and field testing of the nature of heat inventory (NOHI) that examines high school and undergraduate students’ understanding of heat transfer processes in the terms of kinetic molecular theory. NOHI comprises 10 items. Based on preliminary study and the history of science, the 30 distractors of NOHI were designed to reflect the following three categories of possible misconceptions regarding heat: (a) materialistic views, (b) hot atoms, and (c) expanding atoms. Field testing, carried out on 81 students for science teaching, showed the materialistic misconceptions of heat still to be the most popular. NOHI has been proven to be a valid and reliable tool that could be used to determine students’ thinking regarding classical heat processes.

This paper is the second in a two-part series that investigates students’ reasoning on astronomical phenomena. In this work, we examined how (if at all) students rely on spatial scale assumptions when reasoning about Moon phases. To that end, we conducted $N=25$ semistructured interviews with last-year high school students. A personalized, physical scale model of the Sun-Earth-Moon system was constructed for every participant individually and placed on the interview table. Students were encouraged to clarify their thoughts by interacting with the scale model or by making a drawing. The scale estimates underpinning the scale model on the table were assessed through an interactive online survey, which was administered several days to weeks prior to the interviews. Only students with severely underestimated values for the relevant sizes and distances were selected for an interview. This selection procedure was motivated by both pragmatic and empirical considerations. We observed a wide variety of explanations for Moon phases, some of which were not found in the existing literature. Several students used their (inaccurate) scale ideas to reason about Moon phases. However, occasionally an interviewed student expressed arguments inconsistent with the scale model on the table. In these cases, scale assumptions seemed to be not taken into account when reasoning about Moon phases. The implications of these findings are discussed.

This paper is the first in a two-part series that investigates students’ reasoning on astronomical phenomena. We interviewed $N=25$ last-year high school students to uncover their reasoning on three observable phenomena, two of which will be discussed in this paper. The first phenomenon was the apparent sizes of the Moon and the Sun (as seen from Earth) and potential causes for their variation. The second phenomenon regarded the event of a solar eclipse and the difference between total and annular eclipses. The interviews were held in light of the students’ understanding of spatial scales in the Earth-Moon-Sun system. Based on their answers on a prior online survey assessing students’ estimates of these scales, a personal to-scale model of the Earth-Moon-Sun system was provided for every student. The students selected for interviews were those who showed a substantial underestimation of these scales. Students were repetitively encouraged to illustrate their reasoning on both phenomena by using the scale model or by providing a drawing. For both discussed astronomical phenomena, we encountered several alternative explanations that—as far as we know—were not previously documented in the research literature. Many of these relied on an inaccurate comprehension of the involved scales. Although for some students these explanations were in line with the scale model on the interview table representing their substantial underestimations, others did not seem to take spatial scales into account when making suggestions on the causes for these astronomical phenomena. The implications of these findings are discussed.

Students interpret grades as signals of their strengths, and grades inform students’ decisions about majors and courses of study. Differences in grades that are not related to learning can impact this judgment and have real-world impact on course-taking and careers. Existing work has examined how an overemphasis on high-stakes exams can create equity gaps where female students and Black, Hispanic, and Native American students earn lower grades than male students and Asian and white students, respectively. Yet, minimal work has examined how the weighting of individual midterm exam scores can also contribute to equity gaps. In this study, we examine how three midterm exam score aggregation methods for final grades affect equity gaps. We collected midterm exam data from approximately 6000 students in an introductory physics course over 6 years at a large, research-intensive university. Using this dataset, we applied common midterm exam score aggregation methods to determine their impact on aggregated midterm exam grades: dropping the lowest midterm exam score, replacing the lowest midterm exam score with the final exam score if higher, and counting the highest midterm exam score more in the final grade calculation than the lowest midterm exam score. We find that dropping the lowest midterm exam score resulted in the largest increase in final grades, with students with lower grades benefiting the most. However, we find no evidence that these alternative midterm exam aggregation methods close equity gaps. Instead, we find some evidence that such practices could increase equity gaps between male and female students, though the effect is very small in relation to the overall grade increase. Implementing the alternative midterm exam aggregation methods examined here may be useful for instructors wanting to raise course grades or give lower-scoring students a boost. However, they do not appear to be effective in reducing equity gaps.

Accurate diagnosis of student misconceptions is crucial for effective science instruction. Traditional diagnostic tools, however, often rely on unreliable self-reported confidence levels. This study developed and validated a two-dimensional diagnostic model that integrates objective eye tracking data with test responses to more robustly identify misconceptions in Newtonian mechanics. The study involved three phases: developing a four-tier diagnostic test, conducting an eye tracking experiment to analyze visual attention patterns (fixation duration, revisits, scan paths) among high school students, and constructing the final model by correlating eye movements with confidence judgments. Consistent with previous research, results showed that students with misconceptions had shorter fixation durations on correct options and displayed inefficient scan paths. Conversely, those with scientific conceptions showed focused attention. As a novel contribution of this study, a “reversed U-shaped” pattern was observed between confidence and revisits, indicating that revisits can serve as an objective measure of cognitive uncertainty. Based on these findings, we propose a two-dimensional “eye tracking-test” model. This model offers a more precise method for diagnosing student understanding, providing a foundation for targeted instructional interventions and fostering a dynamic “teach-learn-assess” feedback loop for personalized physics education.