We analyze the identity trajectory of a single case study, Cassidy, within the physics community. We focus our analysis on two settings in the physics community: an undergraduate research experience, and undergraduate coursework. We use video data from three interviews (spanning roughly fifteen months) to longitudinally analyze shifts in participation. We discuss Cassidy’s experience through two constructs: normative identities, Cassidy’s sense of the valued roles within physics, as well as personal identity, who Cassidy is within the physics community and the extent to which she aligns with normative identities. In attending to shifts in the alignment between personal and normative identities, we identify several entry points, or salient events that open up new opportunities for participation.

Introductory calculus-based mechanics (“Physics 1”) is an important gateway course for students desiring to pursue a science, technology, engineering, and mathematics (STEM) career. A major challenge with this course is the large spread in the students’ incoming physics preparation. This level of preparation is strongly predictive of a students’ performance because of the overlap between Physics 1 and high school physics courses. Because the level of students’ incoming preparation is largely determined by the quality of their high school physics courses, Physics 1 can amplify K–12 educational inequities and be a barrier for marginalized students wishing to pursue a STEM career. Here, we present a novel introductory course design to address this equity challenge. The design and implementation are based on the concept of deliberate practice as applied to learning real-world problem solving. Students explicitly practice research-identified decision-based skills required for problem solving in the context of solving real-world problems. The problems used in the course and their solutions have little resemblance to what students encounter in high school physics, thereby reducing the dependence of course performance on the high school physics preparation. Versions of this course were taught at a highly selective private and a lightly selective public university. The students who took the course learned the physics content knowledge they needed for future courses, particularly in engineering, and their problem-solving skills improved substantially. Furthermore, their course performance had much less correlation with their incoming physics preparation than was the case for the outcomes from the traditional Physics 1 courses at both institutions: in one case the correlation dropped from $r=0.62$ to 0.14, and on the other case the correlation dropped from $r=0.56$ to 0.26. These findings suggest this course design can be a more equitable version of the traditional Physics 1 course, and hence particularly beneficial for marginalized students.

Gender bias, reduced sense of belonging, and lower physics self-efficacy are among the challenges faced by women who choose to study physics. Prior studies focusing on this underrepresented group have examined the experiences and impacts of formal educational settings, leaving the impact of informal physics programs, frequently called outreach programs, as a relatively overlooked area. Existing research on the impact of informal physics programs indicates that student facilitators, who help run the programs, can experience positive impacts on their learning and sense of community beyond the formal setting of a classroom or laboratory. In this study we took a first step, narrowing our focus to explore the relation between facilitation of informal physics programs and female students’ physics identity, persistence, mindset, and worldview. We analyzed a subset of survey responses (32) and interviews (11) with undergraduate and graduate female students collected as part of our prior study. Our analysis of this subset, based on self-reported data, showed a statistically significant shift in confidence of choice of major after facilitating informal physics programs. Analysis of interviews indicated a positive relation between facilitation of informal programs and female student interest and motivation with regards to the field of physics, improved performance and competence beliefs, and the development of characteristics indicative of a growth mindset. A semantic network analysis showed statistically significant interdependencies between positive persistence and constructs including growth mindset, important early undergraduate experiences, gender stereotype threat, external recognition, and confidence.

Physics instruction is often unable to support students’ self-efficacy. The remote teaching brought on by the COVID-19 pandemic has also affected learning. We surveyed an introductory quantum mechanics course for three years during a transition into the spin first approach, adapting the student-centered prime-time learning model and using it through the remote teaching during the pandemic. Prime-time learning includes weekly meetings where students and instructors discuss in a small group, and the assessment is based on exercises, group work, and self-assessment. We show that this teaching method improved students’ self-efficacy. Students’ conceptual knowledge post teaching remained high throughout the teaching reform, as measured by an abbreviated Quantum Mechanics Concept Assessment test. We also find that the prime-time model is remarkably stable during remote teaching: in contrast to many other studies, we did not see a decline in conceptual learning outcomes or self-efficacy in remote teaching during the COVID-19 pandemic.

The Hertzsprung-Russell diagram (HRD) is a fundamental representation in stellar physics. It contains information about key properties of stars and allows inferences about stellar evolution. The use of the HRD is an important disciplinary activity in astrophysics. For example, it is particularly important to have a graphical understanding of the HRD in order to understand elementary astrophysical relationships (e.g., about the luminosity, temperature, radius, and mass of stars). However, several research papers indicate that students often have difficulty interpreting the HRD, apparently due to its visual complexity, and a number of learning difficulties have been described. Yet, there is still no evidence concerning how learners actually select and extract information from the HRD when completing tasks. In this study, we examined the gaze patterns and think-aloud protocols of 35 physics students as they performed 14 open-response tasks. Benchmarking against traditional $x\text{−}y$ diagrams shows that the HRD imposes a significantly higher cognitive load on students, particularly due to the representation of luminosity, magnitude, and spectral class. Students reported a variety of learning difficulties related to information selection and extraction, sometimes mechanistically copying procedures from typical $x\text{−}y$ diagrams. Eye-movement analysis confirmed these learning difficulties on a procedural level and show whether the students fixated on task-relevant parts of the HRD. Based on the study results, preliminary recommendations can be made in order to create engaging learning materials relating to the HRD.

The study of students’ use of representations is one of the main topics of physics education research and is guided by the overarching field of semiotics. In this paper we compare two semiotic frameworks, one coming from didactics of mathematics and one from physics education research; the theory of registers of semiotic representations and social semiotics, using the networking of theories methodology. A group of first year university students were audio and video recorded as they discussed concepts relating to thermal energy, a study that will be further explored in an upcoming paper. We find that analyzing the recorded data using two different semiotic perspectives provides a wider interpretation of students’ representational use, a descriptive approach to how students use the representations, and an approach to the cognitive aspects of the construction of knowledge. By comparing the theoretical constructs they employ, and how they are employed in the analysis process, we identify constructs that both frameworks have in common, but also where they differ. We have found that each semiotic theory provides a different perspective regarding students’ representational use. We also propose that comparing different theories may provide a space for complementing the constructs of each theory and providing a bigger picture to understand students’ representational use in physics and other science, technology, engineering, and mathematics education areas.

Student difficulties with making sense of graphs in physics have been thoroughly reported. In the study of one-dimensional waves, the issue is even trickier since the amplitude is a function of two variables (position and time). In this work, we investigate students’ reasoning and difficulties with interpreting the graphical representation of the propagation of a pulse in a string. A profile $y(x)$ of the pulse was provided and students were asked to estimate the velocities of several points at the profile. This forced them to consider the time dimension, by focusing their attention on the motion of these points. This turned out to be extremely challenging to the students, who manifested several conceptual challenges which were categorized and analyzed in the first phase of the study. Based on these findings, three levels of scaffolding support were provided, where each level gradually guided the students to draw the wave profile after some time has elapsed. The scaffolding turned out to be effective, since many students managed to identify the new positions of the points successfully. The study reveals how static representations of intrinsically dynamic phenomena can be challenging for students to grasp.

This study examined physics major retention to degree at two institutions with substantially different admissions selectivity. Two modes of leaving the physics major were examined: leaving college and changing to another major while staying in college. The risk of leaving college while still enrolled as a physics major was highest in the spring freshman semester. The changing major risk was substantially different between the two institutions. For the less selective institution, the students changed major at the highest rate in the fall sophomore semester. For the more selective institution, the risk of changing major was high through the first two years of college with highest risk in the fall freshman semester and the fall junior semester. Different features were important in predicting the two modes of leaving; these also differed between institutions. For the less selective institution, math readiness (being academically prepared to enroll in Calculus 1 in the fall freshman semester) was the most predictive feature for leaving the physics major while staying in college; high school GPA was the most important feature for predicting both leaving college and graduating with a physics degree. For the more selective institution, ACT composite scores were the only significant predictor of retention. The role of math readiness was dramatic at the less selective institution with 41% of students not math ready upon enrolling in college as physics majors; 59% of these students failed to enroll in the first required physics class.

Currently there are no assessment instruments available for upper-division thermal physics, though several introductory assessments are currently available. Notably missing from these introductory assessment are items targeting statistical mechanics. This leaves a gap in the content that can be assessed by upper-division thermal physics faculty. In this paper, we present a new, upper-division thermal physics assessment that explicitly addresses both classical thermodynamics and statistical mechanics: The Upper-level Statistical and Thermodynamics Evaluation for Physics (U-STEP). We piloted the U-STEP in multiple classes across various institutions during three semesters, and collected over 600 student responses in total. Here, we present multiple measures of validity and reliability for the U-STEP. We utilized classical test theory to determine item difficulties and discriminations, and differential item functioning to identify potential bias in assessment items that can manifest as performance gaps between different genders and races. The completed U-STEP will be the first upper-division thermal physics assessment available, and one of the first standardized physics assessments to explicitly address identification of possible item bias during the development process.

The Conceptual Survey of Electricity and Magnetism (CSEM) is a multiple-choice survey that contains a variety of electricity and magnetism concepts from Coulomb’s law to Faraday’s law at the level of introductory physics used to help inform instructors of student mastery of those concepts. Prior studies suggest that many concepts on the survey are challenging for introductory physics students and the average student scores after traditional instruction are low. The research presented here investigates the progression in student understanding on the CSEM. We compare the performance of students in introductory and advanced level physics courses to understand the evolution of student understanding of concepts covered in the CSEM after traditional lecture-based instruction. We find that on all CSEM questions on which less than 50% of the introductory physics students answered a question correctly after instruction, less than two-thirds of the upper-level undergraduate students provided the correct response after traditional instruction. We also analyzed the CSEM data from graduate students for benchmarking purposes. We discuss the CSEM questions that remain challenging and the common alternative conceptions among upper-level students. The findings presented here at least partly point to the fact that traditional instruction in upper-level courses which typically focuses primarily on quantitative problem solving and incentivizes use of algorithmic approaches is not effective for helping students develop a solid understanding of these concepts. However, it is important for helping students integrate conceptual and quantitative aspects of learning in order to build a robust knowledge structure of basic concepts in electricity and magnetism.

Likert-style surveys are a widely used research instrument to assess respondents’ preferences, beliefs, or experiences. In this paper, we propose and demonstrate how network analysis (NA) can be employed to model and evaluate the interconnectedness of items in Likert-style surveys. We explore the advantages of this approach by applying the methodology to the aspects of student experience scale datasets and compare the results to the principal component analysis. We successfully create a meaningful network based on survey item response similarity and use modular analysis of the network to identify larger themes built from the connections of particular aspects. The modular NA of the network of survey items identifies important themes that highlight differences in students’ overall experiences. Our network analysis for Likert-style surveys methodology is widely applicable and provides a new way to investigate phenomena assessed by Likert-style surveys.

Peers play an important role in an individual’s learning via collaboration. This study examined three semesters worth of student data collected through collaborative exams in a calculus-based introductory physics course. Participants were asked 142 physics problems over two rounds, and they answered the problems first individually (first round) and then with their groups (second round). Based on the group type determined by the number of correct answers, a majority of correct or incorrect answers, and a variety of answers in the individual round, we examined how group type affects group performance in the group round. A majority of correct and incorrect answers and the variety of answers in the individual round were important for group-round performance. The percentage of finding the correct answer in the group round was directly proportional to the number of students with correct responses in the individual round. Furthermore, an increase in the number of correct answers in the individual round increased the group performance when similar conditions were seen in the majority of correct and incorrect answers and the variety of answers in the previous round.

Societal stereotypes and biases about who belongs in physics and who can excel in it can impact the physics beliefs, including their self-efficacy, interest, and identity, e.g., of women in physics courses. Exploring these beliefs longitudinally and analyzing how different beliefs predict students’ physics identity are important for developing a better understanding of the experiences of women who are majoring in science, technology, engineering, and mathematics in their mandatory physics courses and making the learning environment equitable and inclusive. We analyzed beliefs of students longitudinally in introductory physics 1 and physics 2, which are mandatory for students on the bioscience track, and used structural equation modeling (SEM) and a physics identity framework to investigate whether the relation between gender and physics identity was mediated by self-efficacy, interest, and perceived recognition by others. Although women are not underrepresented in this two-semester physics course sequence for students on the bioscience track, societal stereotypes and biases internalized by women over their lifetime can impact their beliefs about physics when they enter these physics classes and gender gaps can persist if the physics learning environment is not equitable and inclusive. Our findings show a gender gap in beliefs disadvantaging women throughout the physics course sequence. Additionally, the SEM model of physics identity shows that physics perceived recognition by others plays a central role in predicting students’ physics identity throughout the two-semester course sequence.

The communities of practice (COP) framework is useful in understanding the effort to expand physics education into professional preparation. This framework prompts physics educators and physics education researchers to consider “what counts as doing physics” that we want to prepare students for and how we can model professional physics practice in our classrooms. We argue that this focus on community omits an important consideration of the student’s perceptions of the physics community, which informs how they navigate the community. We introduce the idea of a COP model to describe a student’s internal representation of the community’s goals and practices and their sense of membership within the community. The student develops their COP model in response to legitimate peripheral participation within the community and uses this model to extrapolate their experience of the local community to the global community. We describe how this construct shares similarities with other frameworks but retains distinct features that make it a helpful tool for analysis. We demonstrate the use of the COP model in the review of student interviews about the use of computational practices in the physics community. The COP model helps us interpret student responses in terms of their COP models’ alignment and misalignment with the physics community. We discuss implications for instruction and reflect on the utility of the COP-model construct.

High school science classrooms across the United States are answering calls to make computation a part of science learning. The problem is that there is little known about the barriers to learning that computation might bring to a science classroom or about how to help students overcome these challenges. This case study explores these challenges from the perspectives of students in a high school physics classroom with a newly revamped, computation-integrated curriculum. Focusing mainly on interviews to center the perspectives of students, we found that computation is a double-edged sword: It can make science learning more authentic for students who are familiar with it, but it can also generate frustration and an aversion towards physics for students who are not.

Our study investigates whether confidence correlates with consistency in reasoning, specifically about radioactive decay. In prior work, we developed and tested a survey designed to measure consistency of student reasoning about radioactive decay by comparing responses to three prompts that are isomorphic, meaning that, despite having different surface features, they can all be answered appropriately with the understanding that radioactive decay occurs at random. In this paper, we compare (i) student patterns on these isomorphic prompts with (ii) confidence ratings that students provided together with their responses. Our research question is “to what extent does student confidence correlate with consistency in reasoning about radioactive decay?” We have found that there is no significant correlation, suggesting that more confident students are not more likely to be consistent. One reason why this finding is relevant is that the misconceptions model attributes consistency to student ideas (as opposed to the pieces model, which describes student ideas as potentially being context dependent). Our findings suggest that it is premature to describe a student idea as a misconception, even if the student is confident in that idea.

At the start of the COVID-19 pandemic, the majority of secondary instruction in the United States transitioned to an online environment. In many parts of the country, online schooling continued for upwards of two years. Many experts have hypothesized an “academic slide”—a reduction in student learning—following this period of online instruction. We investigated the change in student preparation for introductory college physics in incoming Stanford University students between the fall term of 2019 to the fall term of 2021. We did this by looking at the performance on a validated physics diagnostic exam that all Stanford students intending to take a physics course took before enrolling in an introductory physics course. We found no statistically or educationally significant change in scores. Despite many anecdotal faculty reports, at least for this population, the level of student preparation in physics and related math appears to be unchanged.

Eye tracking enables the reconstruction of eye movements and thus the analysis of visual information selection and integration processes during problem solving. In this way, learner-specific difficulties can be identified and problem-solving process can be adapted accordingly. For such an adaptation, the prediction of response behavior plays a crucial role. To predict whether a problem is solved correctly or incorrectly, the segmentation of the visual stimulus into specific areas of interest (AOIs) is particularly crucial for the quality of a prediction based on eye-tracking data. In the study presented here, the gaze data of $N=115$ students were analyzed while solving the Test of Understanding Graphs in Kinematics (TUG-K), a validated test instrument whose items include graphs of position, velocity, and acceleration versus time. For selected items, response accuracy was predicted based on visual attention using multiple logistic regression analysis, examining the influence of AOI segmentation. The prediction quality could be significantly improved when the diagram was not considered as contiguous AOI, but when it was divided into solution-relevant and solution-irrelevant areas. To verify that the AOIs selected by the regression algorithm are indeed relevant to the solution process, an expert rating was performed, which showed moderate to good agreement between the AOIs rated by the experts as relevant to the correct solution and the AOIs selected by the algorithm. There are also pairs of items in the TUG-K that require the same mathematical solution procedure but differ in the physical context. This opened the possibility to investigate a new approach. Based on response accuracy and allocation of visual attention to one item, the response accuracy of the other item of the pair was predicted. It could be shown that the prediction quality based on visual attention was significantly higher than the prediction based on response accuracy. This demonstrates the added value of collecting process-based data versus product-based data for prediction and thus for learner-specific adaptation. The results of this study indicate, first, that only certain areas are crucial for a correct solution when extracting information from diagrams and, second, that the application of mathematical procedures plays a crucial role in interpreting graphs of different physics quantities. These findings thus provide insight into the visual strategies involved in interpreting kinematic diagrams and can also serve as a basis for eye tracking-based adaptation of problem-solving processes, in which adaptation can occur even before an incorrect answer is given.

Physics classes with computation integrated into the curriculum are a fitting setting for investigating computational thinking. In this paper, we present a framework for exploring this topic in introductory physics courses. The framework, which was developed by reviewing relevant literature and acquiring video data from high school classrooms, comprises 14 practices that students could engage in when working with Glowscript VPython activities. For every practice, we provide in-class video data to exemplify the practice. In doing this work, we hope to provide ways for teachers to assess their students’ development of computational thinking and give physics education researchers a foundation to study the topic in greater depth.