To address the issue of gender disparity in participation in the physics state matriculation exam, a novel school visit program was designed. The program was facilitated by undergraduate university students of physics and related courses who visited schools providing lower secondary education and delivered a single-session workshop including hands-on demonstrations of physics principles, presentations about famous female scientists, information on physics-related careers, and the importance of adopting a growth mindset. In this paper, we present a detailed description of the principles underlying the workshop’s design and its final outline. Participants’ responses to a pre-post questionnaire are analyzed to assess the immediate effects of the workshop, particularly as regards changes in their opinions about physics and students’ intention to study it for their state matriculation exam. Female and male responses are analyzed separately to establish differences in the intervention’s effect between the two genders. Results show an increase among female students with positive perceptions of their physics knowledge and familiarity with physics role models. Our attempt to boost confidence, enjoyment, and perceptions of the relevance of physics proved challenging but yielded some positive results. Strikingly, girls reported positive change in their ability to imagine themselves as physicists and their intention to study physics for their matriculation exam. The intervention was more successful among younger girls. Results obtained from boys were positive but to a lesser extent. The study demonstrates an encouraging and strong positive impact of a uniquely designed, single-session intervention, especially on adolescent girls, and contributes to research on the effectiveness of gender equality-oriented science outreach interventions.

[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name a few. To prepare students for such opportunities, it is important to give them a strong foundation in the basics of QISE, in which quantum computing plays a central role. In this study, we discuss the development, validation, and evaluation of a Quantum Interactive Learning Tutorial, on the basics and applications of quantum computing. These include an overview of key quantum mechanical concepts relevant to quantum computation (including ways a quantum computer is different from a classical computer), properties of single- and multiqubit systems, and the basics of single-qubit quantum gates. The tutorial uses guided inquiry-based teaching-learning sequences. Its development and validation involved conducting cognitive task analysis from both expert and student perspectives and using common student difficulties as a guide. For example, before engaging with the tutorial, after traditional lecture-based instruction, one reasoning primitive that was common in student responses is that a major difference between an $N$-bit classical and $N$-qubit quantum computer is that various things associated with a number $N$ for a classical computer should be replaced with the number $2^N$ for a quantum computer (e.g., $2^N$ qubits must be initialized and $2^N$ bits of information are obtained as the output of the computation on the quantum computer). This type of reasoning primitive also led many students to incorrectly think that there are only $N$ distinctly different states available when computation takes place on a classical computer. Research suggests that this type of reasoning primitive has its origins in students learning that quantum computers can provide exponential advantage for certain problems, e.g., Shor’s algorithm for factoring products of large prime numbers, and that the quantum state during the computation can be in a superposition of $2^N$ linearly independent states. The inquiry-based learning sequences in the tutorial provide scaffolding support to help students develop a functional understanding. The final version of the validated tutorial was implemented in two distinct courses offered by the physics department with slightly different student populations and broader course goals. Students’ understanding was evaluated after traditional lecture-based instruction on the requisite concepts and again after engaging with the tutorial. We analyze and discuss their improvement in performance on concepts covered in the tutorial.

Developing a deeper understanding of scientific concepts is one of the primary goals of science education. To improve students’ conceptual understanding, it is necessary to explore the major characteristics of their learning process. Informed by previous work on conceptual understanding, this study focuses on the concept of evaporation, exploring the level division of students’ conceptual understanding of evaporation and the development of corresponding test questions. The level-based conceptual understanding assessment was tested on 721 seventh-grade Chinese students before and after the evaporation lesson. The results of quantitative and qualitative analyses indicated that the students’ conceptual understanding of evaporation could be divided into three progression levels. Students with a low level of understanding only mechanically remembered the definition of evaporation and could identify evaporation phenomena in their daily lives based on memorized examples. Students with an intermediate level of understanding grasped not only the definition of evaporation but also the factors affecting the evaporation rate. Students with a high level of understanding understood the microscopic nature of evaporation and could explain how these three factors affect the evaporation rate from a microscopic perspective. This study also provides further evidence of the value of grasping the nature of concepts in conceptual understanding.

Physics graduate studies are substantial efforts on the part of individual students, departments, and institutions of higher education. Understanding the factors that lead to student success and attrition is crucial for improving these programs. One factor that has recently started to be investigated is the broadly defined students’ experiences related to support structures. The Aspects of Student Experience Scale (ASES), a Likert-style survey, was developed by researchers to do just that. In this study, we leverage the network approach for Likert-style surveys (NALS) methodology to provide a unique interpretation of responses to the ASES instrument for well-defined demographic groups. We confirm the validity of our findings by studying the stability of the NALS themes and investigating how they are expressed within demographic-based networks. We find that for all four themes in the original ASES study, certain thematic trends capturing students’ experiences vary across the demographic-based networks in meaningful ways. We also reveal that for some demographic groups, there is an interesting interplay between, and mixing of, the original themes. Finally, our study showcases how NALS can be applied to other Likert-style datasets.

Concepts and practices surrounding measurement uncertainty are vital knowledge for physicists and are often emphasized in undergraduate physics laboratory courses. We have previously developed a research-based assessment instrument—the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE)—to examine student proficiency with measurement uncertainty along a variety of axes, including sources of uncertainty, handling of uncertainty, and distributions and repeated measurements. We present here initial results from the assessment representing over 1500 students from 20 institutions. We analyze students’ performance pre- and postinstruction in lab courses and examine how instruction impacts students with different majors and gender. We find that students typically excel in certain areas, such as reporting the mean of a distribution as their result, while they struggle in other areas, such as comparing measurements with uncertainty and correctly propagating errors using formulas. Additionally, we find that the importance that an instructor places in certain areas of measurement uncertainty is uncorrelated with student performance in those areas.

Although there has been extensive research on students’ understanding of the particulate nature of matter (PNM), there is still a lack of research on contexts that can be used to teach this challenging topic. In a previous design-based research study, the authors developed a teaching-learning sequence (TLS) on the PNM in the context of crystal structures based on 40 student interviews using the method of probing acceptance. Data suggested that salt and snow crystals form an effective context for learning the concept of emergence and therefore gaining a better understanding of the PNM. To test whether the TLS also promotes students’ use of the PNM in a realistic classroom setting, a proof of principle study was conducted. In six eighth-grade classes in Vienna, students’ use of the PNM was assessed with a pretest before they were taught the TLS during four lessons. After the intervention, students were given a post-test. Open-ended questions were coded using evaluative qualitative content analysis so that quantitative analysis could be applied. T-tests comparing the means of students’ scores on both tests show significant improvements in students’ use of the PNM in the post-test. The context of crystal structures seems to be helpful to students, as most of them use the PNM when asked about crystal formation. In addition, in the post-test, students more often accepted the idea of empty space between particles and associated particle motion with temperature. However, when asked about phase changes, most students remained in a continuous conception of matter.

A major goal of physics education is to develop strong problem-solving skills for students. To become expert problem solvers, students must have opportunities to deliberately practice those skills. In this work, we adopt a previously described definition of problem solving that consists of a set of 29 decisions made by expert scientists. We quantified the amount of practice undergraduate physics students get at making each decision by coding the decisions required in assignments from introductory, intermediate, and advanced physics courses at a prestigious university. A research-focused capstone course was the only example that offered substantial practice at a large range of decisions. Problems assigned in the traditional coursework required only a few decisions and routinely reduced potential opportunities for students to make other decisions. In addition, we modified traditional physics coursework to offer more decision-making practice. We observed that this increased the number of decisions students actually made in solving the problems. This work suggests that to better prepare undergraduates for solving problems in the real world, we must offer more opportunities for students to make and act on problem-solving decisions.

One expected outcome of physics instruction is for students to be capable of relating physical concepts to multiple mathematical representations. In quantum mechanics (QM), students are asked to work across multiple symbolic notations, including some they have not previously encountered. To investigate student understanding of the relationships between expressions used in these various notations, a survey was developed and distributed to students at six different institutions. All of the courses studied were structured as “spins-first,” in which the course begins with spin-$1/2$ systems and Dirac notation before transitioning to include continuous systems and wave function notation. Network analysis techniques such as community detection methods were used to investigate conceptual connections between commonly used expressions in upper-division QM courses. Our findings suggest that, for spins-first students, Dirac bras and kets share a stronger identity with vectorlike concepts than are associated with quantum state or wave function concepts. This work represents a novel way of using well-developed network analysis techniques and suggests such techniques could be used for other purposes as well.

Teaching assistants have a very important role in physics education as they interact with students and guide them in different contexts. A multitude of research has focused on how to prepare teaching assistants to implement high-quality, research-based teaching techniques. Video clubs, i.e., working with a group of teachers to watch and discuss excerpts of videos recorded from their own teaching, is a novel approach to teaching assistant training that draws from teacher education. This study reports on the experiences of five physics teaching assistants as they participated in a semester long series of video clubs as a part of the implementation of a new introductory lab course. Data were collected through interviews ($N=5$). The interview data were analyzed through reflexive thematic analysis. Five themes for the experiences were constructed from the data. Participation in the video clubs enabled the teaching assistants to uptake teaching practices from each other and spurred a wish for more coplanning of teaching. There was tension in the experiences as some teaching assistants appreciated the positive feedback but others would have preferred more constructive feedback. As the teaching assistants’ experiences were positive, the use of video clubs in teaching assistant training warrants more research. This research could focus on, e.g., the type of feedback the teaching assistants receive in the video clubs.

Wave optics is a mandatory part of Croatian secondary school physics curriculum for students in the final year of secondary school (age 18–19). Many physics education research studies have shown that it is a difficult physics topic for both university and secondary school students. An inquiry-based teaching sequence on wave optics, designed for eight 45-min teaching periods, was developed by the authors. The sequence included four investigative students’ experiments on the topics of interference, diffraction, and polarization of light, as well as several teacher demonstrations. The experimental group included six classes of students from six different Croatian urban secondary schools, who underwent the teaching intervention with the new inquiry-based sequence on wave optics, whereas the control group consisted of six classes from the same schools, taught in a predominantly lecturing way. Both groups were post-tested with the same instrument, the Conceptual Survey on Wave Optics (CSWO), to evaluate the research hypothesis that the new sequence might improve students’ conceptual understanding better than the traditional teaching. The results of the experimental and control groups were analyzed and compared using the Rasch analysis. The results show that the experimental group outperformed the control group in four out of five conceptual areas probed by the CSWO, suggesting that the new inquiry-based teaching sequence may contribute to stronger development of secondary school students’ conceptual understanding of wave optics, especially concerning typical wave optics patterns, reasoning from experiments, and explaining basic wave optics phenomena. A questionnaire on attitudes toward the teaching intervention was administered to students and it was found that students generally liked the inquiry-based teaching intervention and expressed positive attitudes to interactive, experimental, and collaborative aspects of physics teaching. The results are very promising, but their generalization may be limited by the selection of the students, as well as by the short duration of the teaching intervention and the relatively small breadth of the covered topics.

Although researchers have extensively studied student conceptions of radioactivity, the conceptions held by preservice teachers on this subject are largely absent from the literature. We conducted a qualitative content analysis of problem-centered interviews with preservice teachers ($N=13$) to establish which conceptions are held by preservice teachers and to examine these conceptions’ structure in coordination classes. As has already been observed in students, some preservice teachers inadequately differentiate between radioactive matter and ionizing radiation and between fission and decay. We also observed that preservice teachers tend to describe the activation of materials due to ionizing radiation despite having previously denied an activation, thus showing that the conception of activation of materials can reemerge in particular framings. Within the interviews conducted, the concept of energy emerged as a central coordination class regarding radioactivity. This coordination class appeared across contexts and proved fruitful in explaining preservice teachers’ conceptions about radioactivity. We will use the results from this study to develop a teaching-learning laboratory for preservice teachers in which they can actively study high school students’ conceptions while reflecting on their own. In this way, these findings will contribute to improving the structure of nuclear physics courses at the university.

Previous research on student thinking about experimental measurement and uncertainty has primarily focused on students’ procedural reasoning: Given some data, what should students calculate or do next? This approach, however, cannot tell us what beliefs or conceptual understanding leads to students’ procedural decisions. To explore this relationship, we first need to understand the range of students’ beliefs and conceptual understanding of measurement. In this work, we explored students’ philosophical beliefs about the existence of a true value in experimental measurement. We distributed a survey to students from 12 universities in which we presented two viewpoints on the existence of a true definite position resulting from an experiment, asking participants to indicate which view they agreed with more and asking them to explain their choice. We found that participants, both students and experts, varied in their beliefs about the existence of a true definite position and discussed a range of concepts related to quantum mechanics and the experimental process to explain their answers, regardless of whether or not they agreed with the existence of a true value. From these results, we postulate that students who exhibit similar procedural reasoning may hold widely varying philosophical views about measurement. We recommend that future work investigates this potential relationship and whether and how instruction should attend to these philosophical views in addition to students’ procedural decisions.

As part of an effort to examine students’ mathematical sensemaking (MSM) in a spins-first quantum mechanics course during the transition from discrete (spin) to continuous (position) systems, students were asked to construct an eigenvalue equation for a one-dimensional position operator. A subset of responses took the general form of an eigenvalue equation written in Dirac notation. Symbolic blending, a combination of symbolic forms and conceptual blending, as well as a categorical framework for MSM, were used in the analysis. The data suggest two different symbolic forms for an eigenvalue equation that share a symbol template but have distinct conceptual schemata: A transformation that reproduces the original and to operate is to act. These symbolic forms, when blended with two sets of contextual knowledge, form the basis of three different interpretations of eigenvalue equations modeled here as conceptual blends. The analysis in this study serves as a novel example of, and preliminary evidence for, student engagement in sensemaking activities in the transition from discrete to continuous systems in a spins-first quantum mechanics course.

Instructor’s feedback plays a critical role in students’ development of conceptual understanding and reasoning skills. However, grading student written responses and providing personalized feedback can take a substantial amount of time, especially in large enrollment courses. In this study, we explore using GPT-3.5 to write feedback on students’ written responses to conceptual questions with prompt engineering and few-shot learning techniques. In stage I, we used a small portion ($n=20$) of the student responses on one conceptual question to iteratively train GPT to generate feedback. Four of the responses paired with human-written feedback were included in the prompt as examples for GPT. We tasked GPT to generate feedback for another 16 responses and refined the prompt through several iterations. In stage II, we gave four student researchers (one graduate and three undergraduate researchers) the 16 responses as well as two versions of feedback, one written by the authors and the other by GPT. Students were asked to rate the correctness and usefulness of each feedback and to indicate which one was generated by GPT. The results showed that students tended to rate the feedback by human and GPT equally on correctness, but they all rated the feedback by GPT as more useful. Additionally, the success rates of identifying GPT’s feedback were low, ranging from 0.1 to 0.6. In stage III, we tasked GPT to generate feedback for the rest of the students’ responses ($n=65$). The feedback messages were rated by four instructors based on the extent of modification needed if they were to give the feedback to students. All four instructors rated approximately 70% (ranging from 68% to 78%) of the feedback statements needing only minor or no modification. This study demonstrated the feasibility of using generative artificial intelligence (AI) as an assistant to generate feedback for student written responses with only a relatively small number of examples in the prompt. An AI assistant can be one of the solutions to substantially reduce time spent on grading student written responses.

This project aims to understand physics faculty responses to transitioning to online teaching during the COVID-19 pandemic. We surveyed 662 physics faculty from the United States following the Spring 2020 term; of these, 258 completed a follow-up survey after the Fall 2020 term. We used natural language processing to measure the sentiment scores of 364 Spring 2020 responses and another 134 Fall 2020 responses of physics faculty who completed an optional written prompt. Additionally, we determined the change in sentiment scores of the 100 individuals who responded to both surveys. These sentiment scores measured between $-1$ and 1 for completely negative and completely positive, respectively. Sentiment scores after Spring 2020 were slightly positive with a median value of 0.2347. The distribution of sentiment changes was approximately normally distributed with a mean centered near zero. Analysis suggests the average sentiment did not change from the initial to follow-up surveys. To identify major topics within the responses for both surveys, latent Dirichlet allocation analysis was applied to the data. The topic distribution for the initial survey is given as course modifications and technology, negative aspects of the transition—primarily with labs and cheating, exam and evaluation difficulties, and difficulties with student understanding. The topics were noticeably different in the follow-up survey with differences between Fall and Spring, cooperative learning strategies, strategies that worked in the remote space, and benefits of in-person labs.

Vector fields are a highly abstract physical concept that is often taught using visualizations. Although vector representations are particularly suitable for visualizing quantitative data, they are often confusing, especially when describing real fields such as magnetic and electric fields, as the vector arrows can overlap. The present study investigates vector understanding at the end of secondary education. In particular, the extent to which the geometry of the field can be derived from conventional unit vector representations and representations with centered unit vectors was examined. To support this understanding, two exercises were compared. The unirepresentational exercise argued within the conventional unit vector representation, while the multirepresentational exercise attempted to support the link between centered and conventional unit vectors. The results show that almost all test subjects solved the items for generating vector representations correctly, but significant difficulties were encountered in interpreting vector representations. Drawing and interpreting vector representations therefore appear to be different skills that should be practiced intensively and in an integrated way. Various problems could be identified when interpreting vector representations. For example, the number of vectors is often erroneously used to estimate the strength of the field, although more vectors per surface element actually only increase the resolution of the representation. Here, however, the results suggest that the longitudinal density and the transverse density of the drawn vectors are perceived differently by the learners. Furthermore, the learners recognized the field’s geometry much more readily from centered unit vectors than from conventional unit vectors. Errors occur especially when interpreting the geometry of conventional unit vector representations of rotational fields and fields containing both sources and sinks while the geometries of fields containing only sinks were interpreted quite well. The comparison between the two training exercises showed that a promising approach to deepen students’ understanding would be to use an exercise that contrasts conventional and centered unit vector representations and explains how to translate from one representation to the other, rather than describing the main elements of only a single representation. Finally, based on the results of the study, we propose a strategy for teaching vector representations in schools. Given the significantly improved readability of the representation with centered unit vectors, the results even raise the question of whether this type of representation could possibly replace the conventional representation in textbooks and learning materials in the future.

Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on variables available in the first few weeks of the class which could potentially allow for early interventions to help at-risk students. Multiple types of variables were used in the model: in-class variables (average homework and clicker quiz scores), institutional variables [college grade point average (GPA)], and noncognitive variables (self-efficacy). The substantial imbalance between the pass and fail rates of the course, with only about 10% of students failing, required modification to the machine learning algorithms. Decision threshold tuning and upsampling were successful in improving performance for at-risk students. Logistic regression combined with a decision threshold tuned to maximize balanced accuracy yielded the strongest classifier, with a DF accuracy of 83% and an ABC accuracy of 81%. Measures of variable importance involving changes in balanced accuracy identified homework grades, clicker grades, college GPA, and the fraction of college classes successfully completed as the most important variables in predicting success in introductory physics. Noncognitive variables added little predictive power to the models. Classification models with performance near the best-performing models using the full set of variables could be constructed with very few variables (homework average, clicker scores, and college GPA) using straightforward to implement algorithms, suggesting the application of these technologies may be fairly easy to include in many physics classes.

In physics education, a number of studies have developed assessments of teachers’ knowledge of student understanding (KSU) of specific physics concepts with modified versions of existing concept inventories, in which teachers were asked to predict the popular incorrect answers from students. The results provide useful but indirect information to make inferences about teachers’ knowledge of the misconceptions that students may be using in answering the questions. To improve the assessment of teachers’ KSU, a new instrument is developed using a three-tier item design. The items were adapted from 17 questions from the Force Concept Inventory on force and motion. Each item was designed in three tiers, with tier 1 asking for teachers’ own answers to the question to test their content knowledge, tier 2 asking for teachers’ predictions of popular students’ incorrect answers, and tier 3 asking for teachers’ explanations of students’ incorrect answers in an open-ended form. The three-tier design captures teachers’ content knowledge, predictions, and explanations in a single item to allow explicit measures of teachers’ own content knowledge and their KSU on students’ misconceptions. The instrument was validated with preservice physics teachers, who were master-level graduate students in a normal university in China. The assessment results also suggest that the preservice teachers’ KSU of force and motion was only moderately developed, and their content knowledge was uncorrelated with their KSU. In addition, a four-level progression scale of KSU was also developed, which categorized the preservice teachers into five proficiency groups.

Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from reasoning chain construction tasks. We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks.