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.

Writing is a critical skill for modern science, enabling collaboration, scientific discourse, public outreach, and more. Accordingly, it is important to consider how physicists and astronomers are trained to write. This study aims to understand the landscape of science writing education, specifically in physics and astronomy, in higher education in the United States. An online survey probing various aspects of their writing training in both undergraduate and graduate school was administered to 515 participants who have obtained training in physics and/or astronomy, or related fields, at the level equal to or beyond upper-division undergraduate study. Humanities and writing requirement courses appear to have a key role in general writing education, while laboratory courses and feedback from mentors are the dominant modes of science writing education in undergraduate and graduate school, respectively. There is substantial variation in the quality of writing education in physics and astronomy, often dependent on the student’s institution and/or mentor. Some participants also report that their success in disciplinary writing was a result of a solid foundation from K-12 education and/or self-direction toward resources; such reliance on past experiences and student background may contribute to inequality in the field. Many participants also stated a clear desire for more structured writing training to be available in the field. We provide suggestions for how to implement such training to meet the needs of the community identified in the survey.

The rapid shift to remote instruction during the COVID-19 pandemic required widespread adoption of synchronous virtual modalities such as Zoom in higher education. This study directly compares student conceptual learning outcomes in two parallel sections of an introductory physics course—one taught face-to-face (F2F) and the other synchronously via Zoom—delivered concurrently by the same instructor. Conceptual understanding of Newtonian mechanics was measured using the Force and Motion Conceptual Evaluation, administered presemester and postsemester. Normalized gain analysis showed average improvements of 30.49% for the F2F group and 25.01% for the Zoom group, a difference that was not statistically significant. Effect size analysis revealed a large gain for the F2F cohort ($d=1.27$) and a moderate gain for the Zoom cohort ($d=0.72$), with only a small, nonsignificant difference in post-test means ($d=0.19$). Analysis of covariance confirmed that presemester scores were the strongest predictor of postsemester performance ($p<0.001$), while modality itself was not significant ($p=0.333$). A significant interaction ($p=0.010$) indicated that prior preparation translated more effectively into learning gains for F2F students. Overall, both instructional modalities fostered meaningful learning; however, face-to-face instruction produced more consistent outcomes, whereas Zoom-based instruction revealed greater variability tied to students’ initial levels of preparation.

As the need for a quantum-ready workforce grows, educators in quantum information science and engineering (QISE) face the challenge of aligning their programs and courses with industry needs. Through a series of interviews with program directors and faculty across 15 different institutions, we identified the considerations that educators are currently addressing as they develop their various courses and programs. Grounded in a curriculum framework, we conducted a strengths, weaknesses, opportunities, and threats (SWOT) analysis, which revealed shared challenges and opportunities about program goals, curriculum development, collaboration, program data collection and evaluation, and connections across stakeholders in the quantum ecosystem that educators should consider when developing their QISE efforts. Our findings highlight five overarching themes: (1) the strategic ways educators navigate institutional structures to support QISE initiatives, (2) the ongoing challenge of aligning QISE curricula with industry and institutional needs, (3) the importance of fostering interdisciplinary collaboration across departments and institutions in QISE, (4) the need for robust data collection and evaluation to inform QISE course and program development, and (5) the importance of strengthening industry-academia connections to prepare students for the quantum workforce. The details and interconnections in our findings illustrate the value of applying a structured approach to QISE course and program development with the goal of creating a more cohesive QISE education ecosystem.

This study investigates the role of learning dimensions in planning and trialing teaching-learning sequences (TLSs) on surface phenomena in liquids for high school students. Particularly, it aims to identify how different modeling approaches—macroscopic and mesoscopic—impact students’ learning in terms of different dimensions of learning such as conceptual understanding, intellectual growth, and scientific habits of mind. Some results derived from the application of thematic analysis (TA) to qualitative data collected from students’ interviews and feedback, and allowing for the identification of key learning dimensions, are presented. By systematically linking qualitative data to learning dimensions, TA provided a structured framework for evaluating the effectiveness of different instructional strategies. The study highlights the role of both cognitive and affective factors in science learning and underscores the importance of integrating diverse modeling approaches to foster a comprehensive understanding of complex scientific topics.

With the rise of quantum computing, interest has grown in using two-state quantum systems (qubits) at the secondary level to foster students’ conceptual understanding. Quantum measurement, in particular, is central to quantum theory and its accurate conceptualization by students is crucial for grasping fundamental quantum principles. However, instructional methods typically make use of different contexts (i.e., different two-state systems), significantly affecting students’ conceptual development in quantum physics. In this paper, we report findings from a cluster-randomized field trial involving 181 students taught through three inquiry-based, two-state approaches: the which-path-encoded single-photon, the polarization, and the double-well potential approach. All three approaches supported students’ conceptual development, yet students taught using photon polarization and the double-well potential significantly outperformed those participating in a course following the which-path-encoded single-photon approach. Our findings indicate that students participating in the which-path-encoded single-photon approach often retain mixed-thinking frameworks, whereas those taught with photon polarization or the double-well potential approaches were more likely to develop toward quantum thinking. Thus, our findings underpin how influential the choice of (experimental) context is on students’ conceptual development (also) in quantum physics.

Science textbooks often include text and diagrams to aid learner understanding. Yet traditional written instructions in textbooks often separate them, which can increase cognitive load and hinder effective learning. This research proposes and tests a novel presentational format for written instruction that incorporates spatial contiguity, numbered reading sequences, and color matching, aiming to enhance science learning. The effectiveness of the novel format was assessed through two randomized experimental studies. The first study involved 349 high school students in a popular science context, and the second included 187 students in a complex physics course. The results revealed statistically significant improvements in students’ learning outcomes and self-perceived understanding when exposed to the novel instructional format. The improvements were particularly notable in the study focused on complex scientific laws in physics. Furthermore, students reported lower levels of mental effort with the novel format compared to traditional formats, particularly in contexts involving more challenging materials. These results align with cognitive load theory in multimedia learning. The novel presentational format can significantly enhance student learning outcomes and understanding level. It offers a practical and more efficient alternative for instructional material design.

The Energy and Momentum Conceptual Survey (EMCS) is an instrument designed to assess students’ understanding of key physics concepts in introductory mechanics. In this study, we applied a combination of classical test theory (CTT), item response theory (IRT), and exploratory factor analysis (EFA) to a dataset of over 10 000 students collected across 5 semesters at a large U.S. university. CTT and IRT analyses identified several problematic items with weak discrimination, low reliability, or evidence of guessing. We then explored the factor structure of the full and reduced versions of the EMCS, comparing models with differing numbers of factors. While a five-factor model offered the best statistical fit, a four-factor model excluding three problematic items (Q16, Q22, and Q23) provided improved interpretability and conceptual coherence. In both models, energy-related items were separated into distinct clusters, suggesting multiple modes of student reasoning. Finally, we evaluated a theoretically grounded two-factor model reflecting the EMCS’s design intent. This structure, representing energy and momentum as distinct yet stable factors, proved robust across model variations. The study demonstrated the best practice of exploratory factor analysis. Our findings demonstrate best practices for EFA while also highlighting the importance of instructional context in shaping learning gains. The low course-level gains observed in this study suggest the need to examine teaching practices along a continuum from traditional to active. Future work linking EMCS outcomes with detailed instructional-context data across multiple institutions will help clarify how different enactments of active teaching influence student conceptual understanding in physics.

Although conceptual assessment tests are commonly administered at the beginning and end of a semester, this pre-post approach has inherent limitations. Specifically, education researchers and instructors have limited ability to observe the progression of students’ conceptual understanding throughout the course. Furthermore, instructors are limited in the usefulness of the feedback they can give to the students involved. To address these challenges, we propose an alternative approach that leverages computerized adaptive testing (CAT) and increasing the frequency of CAT-based assessments during the course, while reducing the test length per administration, thus keeping or decreasing the total number of test items administered throughout the course. The feasibility of this idea depends on how far the test length per administration can be reduced without compromising test accuracy or precision. Specifically, the overall test length is desired to be shorter than when the full assessment is administered as a pretest and a subsequent post-test. To achieve this goal, we developed a CAT algorithm called Chain-CAT. This algorithm sequentially links the results of each CAT administration using collateral information. We developed the Chain-CAT algorithm using the items of the Force Concept Inventory (FCI) and analyzed its efficiency through numerical simulations. We found that collateral information significantly improved the test efficiency, and the overall test length could be shorter than that of the pre-post method. Without constraints for item balancing and exposure control, simulation results indicated that the efficiency of Chain-CAT is comparable to that of the pre-post method even when each CAT administration includes only 5 items and the CAT is administered 9 times throughout the semester. However, when these constraints are imposed, we found that the efficiency of the Chain-CAT with a test length of five items is lower than that of the pre-post method. Based on this result and analysis of which items were administered, we suggest expanding the FCI item bank by creating additional items or incorporating items from other research-based assessments that have high discrimination parameters and exhibit sufficient variability in their difficulty parameters.