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.

While commercially available mixed-reality (MR) head-mounted devices are also increasingly used in education the impact of MR learning environments is mostly being evaluated with respect to learning outcomes and learning gains alongside a number of affective variables. Here we aim at a deeper understanding of the influence of MR on experimental problem-solving processes by means of eye-tracking analyses as an addition to self-reported and questionnaire-based test instruments. On the basis of a didactically and empirically sound designed MR learning environment for experimental problem solving with an electrical circuit we investigate how novices and experts differ in their success in problem solving and their visual attention for both a MR learning environment and a non-MR condition. Our results indicate that MR not only has a positive effect for both groups on success in problem solving, but that it is also accompanied by significant shifts of visual attention on the different parts of the experiment.

The 2020–2021 academic year was a unique time for many instructors who had to adapt their courses to be conducted remotely due to the COVID-19 pandemic. This was especially challenging for physics lab courses, which usually emphasize hands-on experiments. Although many courses have now returned to in-person teaching, the possibility remains of future disasters necessitating similar remote courses. It is important to understand how undergraduate students experienced remote physics lab courses during the pandemic, including what aspects of the courses contributed to positive student outcomes. To investigate this, we surveyed over 5000 students from 24 different institutions, asking how the students engaged with their physics lab courses during the 2020–2021 academic year. Here, we describe the frequency with which the students performed various class activities, aspects of the course environment, challenges the students faced, aspects of the courses the students found enjoyable, and some student outcomes. We further study the impact of the course activities and course environment on four of the outcomes (self-reported learning of lab skills, self-reported learning of concepts, course enjoyment, and development of a sense of community). We find that students who were provided clear expectations, had enough time for their coursework, frequently worked in groups, and frequently had access to guidance from their instructors were more likely to report positive outcomes. This work demonstrates the importance of certain aspects of lab courses for several desirable outcomes in remote lab courses during a pandemic, with findings that may transfer to in-person or remote lab courses in the future.

This paper investigates the interactions between context and professional development of physics instructors in a case study of two physics faculty. A phenomenological-case study approach was used to analyze two physics faculty at different institutions over a year and a half using three semistructured interviews each. The data enabled the identification of relevant context elements; the impact of these elements on physics faculty was explored by adapting a framework that examines instructors’ professional development. The analysis shows that both case study subjects used their physics expertise and growing understanding of their context to develop their physics teaching. However, this growth was enacted differently given the nature of their context, highlighting instructors’ strengths in navigating their local context to improve their physics teaching. The results show the subtleties of how context has a salient, complex, and evolving role in moderating faculty’s professional development. By taking a faculty-centric approach, this paper broadens the community’s awareness of the ways physics instructors develop their physics teaching. This work contributes to a relatively new lens by which the physics community views, discusses, and supports the professional development of physics faculty.

In previous work we analyzed databases for 95 classes to show that the percent grade scale was correlated with a much higher student fail rate than the 4.0 grade scale. This paper builds on this work and investigates equity gaps occurring under both scales. By employing a “course deficit model” we attribute the responsibility for closing the gaps to those who are responsible for the policies that guide the course. When comparing course grades in classes graded using the percent scale with those in courses graded using the 4.0 scale, we find that students identifying as belonging to racial or ethnic minorities underrepresented in physics suffer a grade penalty under both grade scales but suffer an extra penalty under percent scale graded courses. We then use the fraction of A grades each student earns on individual exam items as a proxy for the instructor’s perception of each student’s understanding of the course material to control for student understanding and find that the extra grade penalty students from groups underrepresented in physics students suffer under percent scale grading is independent of the student’s understanding of physics. When we control for more student level variables to determine the source of the grade scale dependent penalty, we find that it is primarily the low F grades (partial credit scores) on exam problems that are the source of these inequities. We present an argument that switching from percent scale grading to a 4.0 grade scale (or similar grades scale) could reduce equity gaps by 20%–25% without making any other course changes or controlling for any incoming differences between students.

Student learning in simple electric circuits has been extensively studied, which has revealed a large number of persistent misunderstandings. This study applies the conceptual framework model to investigate student difficulties in the knowledge integration perspective. The results are used to guide the design of a concept test that targets the different stages of knowledge integration in student learning of electric circuits. Specifically, two areas of research have been conducted. First, based on content analysis by experts and a review of the literature on students’ conceptual understandings, a conceptual framework for electric circuits is developed. The conceptual framework model is then applied to guide the development of a multiple-choice concept test for assessment of knowledge integration in learning electric circuits. Both qualitative and quantitative data were collected from high school students who had completed the learning of electric circuits. The results confirmed that the conceptual framework model can effectively represent the knowledge structures of students at different levels of knowledge integration. In addition, the assessment outcomes also reveal that the concept test is effective in identifying unique features of knowledge integration, including context dependence and fragmentation of knowledge components, memorization-based problem solving, difficulty in transfer to novel contexts, and lack of meaningful connections between microscopic and macroscopic models of electric current. The assessment outcomes can also provide practical information for instruction to promote knowledge integration in learning electric circuits.

Studying students’ problem-solving abilities in physics education research has consistently shown that novices focus on a problem’s surface features rather than its physical principles. Previous research has observed that some electricity and magnetism students confuse electricity and magnetism concepts, often presented in parallel problems (or problems with similar surface features). This confusion has been referred to as interference. It is essential to compare students’ performance in these problems to evaluate their understanding of these topics. The present work focuses on the students’ understanding of interactions between charged particles (i.e., electric force) and electric currents (i.e., magnetic force). We present and compare the findings on students’ conceptions when analyzing electric and magnetic interactions for different systems of field sources. We conducted this study with engineering students finishing a calculus-based course on electricity and magnetism. We administered a written, open-ended questionnaire with two sets of three items: one version contained only electricity problems, and the other contained only magnetism problems. Each item in the electricity version of the test had a parallel counterpart in the magnetism version. We used a phenomenographic approach to analyze our data to identify categories that emerged from students’ answers. We identified four main ideas in the results: (a) the rule of signs (ROS), which does not evidence a complete conceptual understanding of electric interactions; (b) the force-field confusion due to the similarity of electricity and magnetism contexts; (c) the importance of semiotic representation when answering an electricity and magnetism problem, where the student’s choice of representation indicates their understanding, and (d) the interference phenomenon, in which we find evidence of other factors besides those produced by the timing of instruction and administration of the tests. At the end of this work, we provide recommendations for instruction.

Interdisciplinary introduction to quantum information science (QIS) courses are proliferating at universities across the US, but the experiences of instructors in these courses have remained largely unexplored in the discipline-based education research (DBER) communities. Here, we address this gap by reporting on the findings of a survey of instructors teaching introduction to QIS courses at institutions across the U.S., primarily at the undergraduate or hybrid undergraduate and graduate level, as well as follow-up focus interviews with six individual instructors. Key themes from this analysis include challenges and opportunities associated with the diversity of instructor and student backgrounds, student difficulties with the mathematical formalism (especially though not exclusively with linear algebra), and the need for better textbooks and curricular materials. We also find that while course topics are ostensibly similar, each course is crafted by its instructor to tell a different story about QIS and to uniquely balance goals such as accessibility and academic rigor, such that no canonical introduction to QIS course emerges from our dataset. We discuss the implications of this finding with regard to the benefits and risks associated with standardization of curricula as QIS coursework matures.

This study investigated factors influencing Force and Motion Conceptual Evaluation (FMCE) pretest and post-test scores for a sample ($N=1116$ students) collected in the introductory calculus-based mechanics class at a large eastern land-grant university. Several academic and noncognitive factors were examined using correlation analysis and linear regression analysis to understand their relation to students’ physics conceptual understanding. High school physics preparation was the most important factor in predicting FMCE pretest score. The kind of high school physics class (normal or Advanced Placement) and the student’s academic performance in that class also greatly affected pretest scores. The optimal linear regression model explained 28% of the variance of pretest scores. Controlling for pretest score, ACT or SAT verbal and mathematics scores, students’ grade expectation, and self-efficacy significantly predicted post-test score. The optimal linear regression model explained 54% of the variance of post-test scores. Pretest scores completely captured the effect of high school preparation on post-test scores; if pretest scores were included in a model predicting post-test scores, then high school physics preparation variables were not significant. Gender differences were observed on both the pretest and the post-test. These differences were not substantially mediated by either academic or noncognitive factors.