Peer discussion (PD) is widely used as an active-learning strategy in physics education, yet most prior research has focused on conceptual multiple-choice questions. Much less is known about how peer discussion functions when applied to mathematically structured problems typical of calculus-based mechanics courses. To address this gap, we compared the effects of PD and instructor explanation (IE) during short in-class exercise sessions in a calculus-based introductory mechanics course ($N=87$). Using a cluster-randomized crossover design with two course sections, instruction alternated between PD and IE across weekly exercise topics. After each lesson, students completed a 16-item post-test; 12 of these items were also administered on the final exam. Across the 16 post-test items, we found no statistically significant evidence that PD outperformed IE on any single item. In unadjusted item-level analyses, four items that required greater abstraction and multistep mathematical reasoning showed a trend favoring IE, although none of these differences remained statistically significant after Holm correction for multiple comparisons. On the final exam, no statistically significant differences were observed between conditions. These results suggest that, within the instructional conditions of this study, PD did not produce consistent performance gains relative to IE, and any item-level differences may depend on task characteristics such as mathematical abstraction, novelty, and multistep reasoning demands.

Performance in introductory courses, particularly physics, is often crucial for student success in science, technology, engineering, and mathematics majors and can impact an individual’s tendency to persist in their chosen field. To enhance students’ individual learning experiences, faculty at many universities have worked to develop open-access, self-study materials to help build conceptual understanding and problem-solving skills. Faculty at Texas A&M University have contributed to these efforts, creating more than 200 online video resources and a broad bank of prior exams available to students. This work explores and measures the impact that these resources can have on student course outcomes in an introductory, calculus-based electricity and magnetism course. Data were collected from three fall semesters, 2021–2023, including classroom performance, a conceptual assessment, and relevant university-level data to contextualize student background and preclass abilities. We present results from regression analyses examining the relative importance of multiple resources offered concurrently to students. We also discuss how the use of supplemental material affects course performance, alongside other potential predictors such as demographics and prior preparation. Similar to prior studies, we found that relevant prior preparation in mathematics was the strongest predictor of student performance, with prior physics knowledge being a weaker but still statistically significant predictor. Students’ utilization of supplemental prior exams was the second-highest predictor of student performance. First-generation students were observed to have a slightly lower average performance on exams. However, interaction terms in the regression models suggested that first-generation students using supplemental prior exams were able to close this gap. Through anonymous surveys, students reported favorable impressions of the materials, with over 80% of students sharing that they had a noticeable contribution to their learning outside of the classroom and 98% stating they would recommend them to their peers.

This qualitative study explores how undergraduate women define what it means to be a physics person alongside how they describe their own physics identity. Our team analyzed 120 survey responses (97%, or 116 of the 120 respondents, identified as women or non-binary) to understand what factors are associated with an individual’s physics identity. Those surveyed were attendees of Conferences for Undergraduate Women in Physics, who attended 88 unique institutions in 30 states. Our participants’ conceptions of what it means to be a physics person fell into two themes: (1) people who are skilled academically and in research and (2) those who have an interest in physics ranging from an enjoyable interest to a life-devoting passion for the subject. When asked to explain their own physics identities, students responded through a personal and social lens more frequently than through an academic lens. Our analysis revealed three tensions in the development of undergraduate women’s physics identity: (1) the tension between identifying entirely as a physics person and having other identities that are important to who they are, (2) the tension between the need for belonging in the physics community and their view of themselves as a physics person, and (3) the tension between their expectations of a physics person and their beliefs about their own abilities or interest.

The Disciplinary Learning Companion (DLC) is an online reflection tool designed to enhance students’ engagement in metacognitive activities during physics problem solving. It achieves this through providing reflective discipline- and topic-specific prompts on the solving process. This study focuses on evaluation strategies and aims to understand whether, how, and when students use evaluation strategies during physics problem solving, whether the DLC’s reflective prompts trigger the use of evaluation strategies and the extent to which this use is transferred to future problem solving. A total of twelve first-year bioscience engineering students each participated in four think-aloud interviews over a four weeks period. In these interviews, the students first solved a physics problem and then reflected on it supported by the DLC. This study focuses on students’ use of evaluation activities as part of their metacognitive process. To assess students’ current metacognitive levels, we examined the evaluation strategies they used spontaneously. In addition, we evaluated the impact of the DLC by studying the evaluation strategies students used after being prompted and those transferred to subsequent problem-solving attempts. The analysis revealed that students spontaneously applied evaluation strategies to numerical problems but rarely to symbolic problems. The DLC’s reflective prompts successfully triggered the use of evaluation strategies, though infrequently. Similarly, there were few instances of transferring evaluation strategies from one think-aloud interview to another.

Generative artificial intelligence (GenAI) has shown growing potential in supporting teacher education, yet little is known about how preservice teachers collaborate with AI during instructional design (ID). This study examined the effects of DeepSeek, a Chinese large language model, on preservice physics teachers’ ID performance and cognitive processes. A quasiexperimental design was conducted with 220 participants completing a 45-min ID on Newton’s third law, either with or without DeepSeek assistance. Data from IDs, interaction logs, and interviews were analyzed through statistical tests, epistemic network analysis (ENA), and thematic coding. Results indicated that the DeepSeek-assisted group outperformed the control group in analytical and structural aspects of ID, such as teaching content analysis and learner analysis. ENA revealed that high-performing participants exhibited proactive “problem representation–AI interaction–monitoring” patterns, while low-performing participants relied on reactive prompting. Interviews further confirmed that AI was helpful in contextual introductions and experiment design but also revealed issues such as outdated curriculum references and overly absolute expressions that lacked sensitivity to teaching contexts. The findings suggest that GenAI can serve as a cognitive scaffold when used strategically and highlight the need to integrate AI literacy and metacognitive training into teacher education.

Student experiences in physics beyond the classroom and their role in supporting student development have been the subject of increasing research attention in recent years. Results, typically from small studies at single institutions, have illustrated that facilitating informal physics experiences for nonscientists can enhance student disciplinary identity, learning, sense of belonging, and career skills. However, it is essential to examine whether these impacts are the sole provenance of institutions with well-developed outreach programs or if they may be shared by institutions anywhere. This work reports on the analysis and findings of responses to three open-ended questions presented to students who indicated they had engaged in facilitating outreach programs as part of a national survey distributed through the Society of Physics Students network in spring 2023. Employing a network analysis with Girvan-Newman clusters revealed six core themes of student experiences: community participation, resilience, transformation, audience dialogue, disciplinary development, and disciplinary connectedness. The first four of these clusters were observed to be highly interconnected, providing evidence that the impacts and experiences within them are interrelated with other clusters, particularly interactions with the audience, which is a central feature of informal physics programs. In particular, student experiences highlighted that facilitating informal physics programs enhanced their resilience and belonging, grew their physics identity, provided opportunities to develop essential career skills, and cultivated a growth mindset.

Models are commonly used in astronomy education to help students understand and discuss difficult or abstract concepts. From a physics education research perspective, metaphors and analogies are often used to support reasoning about abstract phenomena. This study investigates how students draw on classroom experiments as physical analogies (a multimodal instance of conceptual metaphor) when reasoning about exoplanetary atmospheres. We conducted two lessons: one about cloud formation on Earth and exoplanets and another about lightning on Earth and exoplanets, both including a hands-on experiment related to the topic. The students were then split into peer-led focus groups and tasked with discussing the experiment and its relation to weather in planetary atmospheres. We audio-recorded these group discussions and extracted relevant excerpts. We analyzed these excerpts sequentially using thematic analysis, network analysis, and conceptual blend analysis. We identified four input spaces used by students (relevant physics, the experiment, Earth conditions, and exoplanet conditions) and three ways in which students use analogies (mapping between domains, identifying limitations, and extending the analogy). Across these analyses, we found that students integrate these input spaces to construct blend spaces that mediate their reasoning about processes and concepts in Earth and exoplanetary weather phenomena. We also discuss implications for future research and teaching.

Students’ problem-solving processes have attracted a wide range of attention. From an information processing perspective, we distinguish three types of physics problems: information-explicit, information-redundant, and information-implicit. This study explores students’ visual behaviors when they were doing problem solving with these three types of problems. The participant sample included 90 high school students. Eye-tracking technology was used to record their eye movements while they solved each type of problem. The results showed that although students’ accuracy did not differ significantly across the three problem types, their eye-movement behaviors exhibited differences. Students displayed longer total fixation times on information-redundant problems, particularly in the text and option areas, suggesting increased cognitive effort caused by extraneous information. In contrast, information-implicit problems elicited the highest number of revisits to the text area, indicating students’ repeated attempts to extract obscured information. Moreover, students devoted more fixation time and more revisits to the correct option in information-explicit problems, whereas information-redundant and information-implicit problems resulted in longer fixation and more revisits to incorrect options. These findings indicated that students’ visual attention patterns were influenced by the information structures of the physics problems, even though their performance outcomes did not significantly differ. The study highlights the importance of problem design in supporting students’ effective reasoning and information integration.

As quantum technologies gain importance across scientific and societal domains, understanding the foundational principles driving their development is increasingly crucial. While much work has been devoted to defining essential knowledge and curriculum content for quantum technologies at various educational levels, less attention has been paid to how the Second Quantum Revolution is shaping the nature of quantum computation in terms of its foundational epistemic aims and values. The paper presents a conceptual account of the nature of quantum computation through document analysis of foundational research papers. Using the lens of the Family Resemblance Approach to the nature of science, it uncovers the core epistemic aims and values characterizing the domain of quantum computation. By applying this analytical framework, the study identifies both predetermined epistemic aims and values (e.g., objectivity and accuracy) and emergent subcategories (e.g., materiality of information and efficiency) relevant to quantum computation. The document analysis contributes to the literature on the nature of science in general and to the characterization of quantum computation as a domain in particular. Furthermore, the analysis brings to light implicit epistemic aims and values that guide practitioners in a scientific domain to the foreground, which often remain unarticulated in educational contexts. The findings include 17 instances of epistemic aims and values of quantum computation generated from an analysis of significant research papers in the field. By systematically generating these epistemic aims and values, the study provides a nuanced and explicit approach to characterizing the nature of quantum computation, enabling educators to incorporate these themes into curriculum design, teaching, and learning. Some implications for quantum technologies education are discussed.

Generative artificial intelligence (AI) has shown promising potential in education, particularly in providing real-time, customized, student-facing feedback in large-enrollment courses. A crucial skill that mediates effective use of AI is the systematic structuring of natural language instructions to AI models, commonly referred to as prompt engineering. This study has two objectives: (i) to investigate the sophistication of student-generated prompts when seeking feedback from AI and (ii) to examine trends associated with students’ preferences and perceptions of features in AI feedback generated from self-crafted prompts vs prompts incorporating prompt-engineering techniques and principles of effective feedback. Results indicate that student-generated prompts typically reflect only a subset of foundational prompt-engineering techniques. We also identified 12 distinct features that students attribute the usefulness of AI-generated feedback, spanning four broader themes: (i) evaluation, (ii) content, (iii) presentation, and (iv) depth. Finally, students overwhelmingly prefer feedback generated from structured prompts, particularly those combining prompt-engineering techniques with principles of effective feedback. Implications of these results—such as integrating the principles of effective feedback in design and delivery of feedback through AI systems, and incorporating prompt engineering in introductory physics courses—are discussed.

Physicists have called for “a culture and climate that not only is harassment-free but includes people taking active steps to support those who are minoritized … learning spaces that … provide positive experiences where everyone is supported and valued” [A. V. Knaub et al., Changing culture and climate to prevent sexual harassment in the physics educational setting, Phys. Teach. 58, 352 (2020)]. In this paper, I provide guidance on how to design DEI interventions to support this goal. I address common critiques of DEI interventions and the research base supporting the use of interventions, and discuss physics-specific considerations around DEI interventions [especially concerns about (i) dealing with skeptical participants, and (ii) inadvertently making conditions worse for minoritized participants]. I lay out possible outcomes of DEI interventions, including: increasing participants’ awareness of bias; teaching participants to overcome bias; increasing motivation to reduce bias; increasing physics identity; providing recognition to physics students; and increasing the sense of belonging among physicists from minoritized groups. I explore how to justify an intervention to participants and the benefits of making the intervention voluntary or required, and provide possible intervention activities, matched with intended outcomes, as well as a list of physics-specific materials for teaching about stereotype threat, microaggressions, intersectionality, Whiteness, and more. I provide guidance on evaluating interventions, including tools that others have used to measure various outcomes, appropriate quantitative and qualitative approaches, and considerations of rigor. The paper is written in sections; each section begins with a summary and is written so that readers can jump to the section of most interest.

We investigate how faculty share aspects of their identities to foster connections with students, finding significant nuances in what, how and when they decide to share. From interviews with 19 faculty, we identify and develop four personas: Brooke, the Trust Builder, prioritizes creating an environment of trust by openly discussing their identity, aiming to foster student openness. Wray adopts a walled-off approach, separating personal and professional life due to past negative experiences or a belief in the importance of that division. Casey, the Cautious Sharer, expresses concerns about potential alienation or backlash and approaches personal sharing with caution. Finally, Nour, the Identity Navigator, shares personal experiences to assist others in navigating their own identities, acknowledging the challenges of the college years. Communication of shared or adjacent identities is a key step toward developing an empathetic understanding that can facilitate taking appropriate supporting actions, and the nuances of how these identities are communicated add insight into how faculty can work within their comfort zones to better support students.

A deep understanding of mathematical equations and their physical meaning is an important skill for physicists and therefore a central learning goal in university physics education. However, many students approach mathematical expressions in physics in a purely procedural manner, using them as calculation tools without critically evaluating their physical plausibility. Making sense of equations, that is, being able to interpret and evaluate equations within a physical context, is essential for the development of expertlike reasoning in physics. Frequently employed strategies that assist in the sensemaking process are dimensional, covariational, special, and limiting case analysis. Despite their importance, these strategies are rarely taught or practiced explicitly in university physics courses. The present intervention study systematically implemented sensemaking strategies within a university course for first-year physics students covering topics of electro- and magnetostatics as well as electrodynamics. Traditional exercises were enriched with tasks requiring students to critically evaluate the plausibility of self-derived equations. The tasks were integrated into recitation sessions and homework assignments. A total of $N=67$ physics students participated in the study. A quasiexperimental pre-post design with two groups was employed to evaluate the effectiveness of the intervention approach. Results show that students highly valued all four strategies equally and reported an increased perceived competence in their application after the intervention. Significant improvements in performance were observed for dimensional ($d=0.67$) as well as for special case analysis ($d=0.50$). Students’ performance in analyzing covariations only showed a tendency favoring the intervention group. No intervention-related impact on the performance of limiting case analysis was found, suggesting that this strategy may require more extensive practice. The findings underscore the importance and usefulness of explicitly teaching strategies to support students’ sensemaking of physics equations.

A growing number of introductory physics instructors are implementing active learning methods in their classrooms, and they are modifying these methods to fit their local instructional contexts. However, we lack a detailed framework for describing the range of what these instructor adaptations of active learning methods look like in practice. Existing studies apply structured protocols to classroom observations and report descriptive statistics (e.g., the fraction of class time spent on each activity), but this approach overlooks the complex nature of instruction. In this study, we apply network analysis techniques to classroom observations to define a typology of active learning that considers the temporal and interactional nature of instructional practices. We analyze video data from 30 instructors at 27 institutions who implemented one of the following named active learning methods in their introductory physics or astronomy courses: Investigative Science Learning Environment (ISLE), Peer Instruction, Tutorials, and Student-Centered Active Learning Environment with Upside-down Pedagogies (SCALE-UP). We first create one classroom observation network per instructor that captures temporal sequences of activities measured using the Classroom Observation Protocol for Undergraduate STEM (COPUS). We then calculate the cosine similarity between all pairs of observation networks, create a similarity network where each instructor’s classroom observation network is a node and the edges represent cosine similarity, and apply the Infomap clustering algorithm to the similarity network to identify types of active learning instruction. We find five types of instruction: clicker lecture, dialogic clicker lecture, dialogic lecture with short groupwork activities, short groupwork activities, and long groupwork activities. We find no significant relationship between these instruction types and the named active learning methods; instead, implementations of each of the four methods are spread across different instruction types. This result prompts a shift in the way we discuss and study active learning: the names of developed active learning methods may not actually reflect classroom instruction. We also find that student conceptual learning does not vary across the identified instruction types, suggesting that instructors may be flexible in the style in which they implement these methods without sacrificing effectiveness. We discuss the implications of these results for both research and the professional development of college physics instructors.

The majority of previous studies of physics courses at the upper undergraduate level have been content-related or focused on the format of instruction, while detailed investigation of student performance patterns across various conceptual and quantitative measures in this context has been neglected, relatively speaking, in comparison to the substantial work that has been carried out in introductory courses. In this study, we used a sample of students in a traditional upper-level electricity and magnetism course at a large U.S. university to examine performance patterns and understand the extent to which, and the ways in which, conceptual and quantitative performance were related to each other. To this end, we used various measures of prior preparation (including standardized math scores and background in physics and mathematics in high school and college), in-course quantitative performance (homework, quizzes, and exams), and conceptual performance (scores on the Conceptual Survey of Electricity and Magnetism). Overall, our analysis revealed that prior preparation was a significant predictor of success in this course and that there were strong correlations between students’ conceptual and quantitative performance. In particular, grades in the most recent upper-level physics course (upper-level mechanics) were the strongest predictor of upper-level electricity and magnetism course performance, even after controlling for mathematical preparation. Although average normalized conceptual gains were relatively large and independent of preinstruction conceptual scores, threshold effects suggested that students entering with the lowest conceptual understanding were unable to achieve high postinstruction conceptual scores or overall course grades. Our findings suggest that better prior preparation through early development of mathematical skills, as well as instructional strategies that support learners in bridging physics and mathematics effectively, may be crucial for success in upper-level electricity and magnetism.

Collaborative mathematical modeling of real-world problems requires learners not only to engage in multiple epistemic practices but also to remain aligned around shared physical models as assumptions and solution paths evolve. However, few empirical studies have traced how such coordination unfolds moment by moment during authentic work. This study examined the in vivo discourse of a group of preservice physics teachers solving an ill-structured mechanics problem by tracking their collaborative reasoning turn by turn. We analyzed the group’s work by coding their epistemic modeling phases and the operational physical models in use, alongside a time-resolved measure of discourse model coherence (DMC) that indexes alignment around shared model assumptions. By combining turn-level qualitative coding with time-resolved quantitative visualizations, we traced how the group’s shared problem space evolved over time and identified moments of convergence and divergence. The analysis revealed that the group progressed through six epistemic phases of modeling while iterating across multiple operational physics models of increasing complexity. Periods of heightened complexity were marked by slower progression and frequent fluctuations between epistemic phases. Cross-model comparisons functioned as a key evaluative resource, in which simpler models served as benchmarks for judging more complex ones. Instructor prompts played a significant role: moves that narrowed the modeling scope tended to stabilize coherence, whereas prompts that reopened assumptions often increased epistemic uncertainty and temporarily disrupted alignment that produced measurable drops in DMC. These findings provide a time-resolved account of collaborative modeling dynamics and offer methodological and instructional insights for supporting modeling-rich physics learning.

This study compares conceptual understanding, as well as the prevalence and consistency of student ideas regarding simple dc circuits, between two distinct groups: high school students after initial instruction ($N=1274$) and undergraduate students in physics-related degree programs before attending university lectures ($N=423$). To investigate student ideas, we employed the 2T-SEC test, a two-tier multiple-choice instrument. Results show a significantly higher conceptual understanding among undergraduates (Cohen’s $d=0.96$). However, the overall prevalence of specific student ideas appears to be similar across both groups: of the 13 student ideas examined, 5 occur equally often, 4 are more frequent among high school students, and 4 among undergraduates. Notably, certain ideas well documented in the literature, such as “sequential reasoning” and “current consumption,” are significantly more prevalent among undergraduates. Consistency analyses reveal generally low levels across both groups, with few significant group differences. Moreover, these findings suggest that everyday experiences and language may reinforce certain student ideas over time.

In this paper, we use the action, process, object, schema theoretical framework to validate a hypothetical learning trajectory of the 2D heat equation based on a preliminary genetic decomposition that stresses the conceptual understanding of its mathematical formulation. We design questions to probe specific mental constructions of the preliminary genetic decomposition. We interviewed eight second-year B.Sc. students enrolled in engineering, physics, or twin (mathematics and physics) majors. Our findings indicate that students engage with many predicted mental constructions. In particular, the coordination and encapsulation of two process conceptions of the Laplacian of temperature improve understanding, although doing so is challenging. Other parts of the genetic decomposition require refinements. These include mental constructions related to the temperature distribution function, heat flow, and temperature gradient.

We report on a department-wide initiative, implemented beginning in 2018, aimed at making computational methods a routine and integrated part of the undergraduate physics curriculum. Using survey data collected from undergraduate physics students across multiple years, we examined how students’ attitudes toward computation and their self-efficacy with computational skills change as they progress through the program. The survey includes attitudinal items and self-efficacy ratings for a range of computational tools and methods, as well as retrospective assessments of students’ initial skill levels. An exploratory factor analysis was conducted for both parts of the survey, and each part produced reliable, two-factor structures. Our analyses of student attitudes and self-efficacy throughout the program show that, at the program level, students generally report more positive views of computation and higher computational self-efficacy at later stages. However, these changes are not evenly distributed across the curriculum, with larger shifts concentrated in particular semesters. Together, these results show that our department-wide effort to integrate computational methods into the undergraduate curriculum supports growth in student attitudes and self-efficacy, but that this growth may be driven by specific experiences in the program rather than by progression alone. We also find that conclusions about growth in computational self-efficacy depend on how growth is measured. For some skills, growth based on present self-efficacy closely matches growth estimates that incorporate retrospective initial ratings, while for others, the two approaches differ substantially. Importantly, these differences are not uniform across skills but align with the underlying factor structure of the survey. Skills associated with specific computational tools or languages show relatively little response-shift effect, whereas skills associated with applied computational methods exhibit larger differences in growth estimates when retrospective measures are included. These findings suggest that retrospective pretest survey designs may be particularly important for assessing growth in methods-based computational skills. On the whole, this study highlights the importance of considering both curricular structure and measurement approach when evaluating our curriculum-wide initiative to integrate computation into undergraduate physics programs.

We present three versions of a pendulum lab activity to explore how theoretical commitments, motivations, and aspirations are reflected in curriculum design. In earlier work, Boudreaux and Elby [How curriculum developers’ cognitive theories influence curriculum development, Phys. Rev. Phys. Educ. Res. 16, 020144 (2020)] discussed how different theoretical perspectives led them to different designs of tutorials. Here, we discuss our finding that even starting from the same theoretical perspective and having similar goals for students can lead to differences in design. We give three interacting reasons why our labs diverge: We have different expectations of students at our respective institutions; we have different ancillary goals; and we draw different implications from our shared theoretical commitments. Our account demonstrates the complexity of the relationship between theory, goals, and curriculum design. In this way, it adds to prior arguments for the importance of designers’ articulating the reasoning for their choices as well as for the possible value of instructors’ responsive adaptations to curricula.