Solving problems is crucial for learning physics, and not only final solutions but also their derivations are important. Grading these derivations is labor intensive, as it generally involves human evaluation of handwritten work. AI tools have not been an alternative, since even for short answers, they needed specific training for each problem or set of problems. Extensively pretrained AI systems offer a potentially universal grading solution without this specific training. This feasibility study explores an AI-assisted workflow to grade handwritten physics derivations using MathPix and GPT-4. We were able to successfully scan handwritten solution paths and achieved an R-squared of 0.84 compared to human graders on a synthetic dataset. The proposed workflow appears promising for formative feedback, but for final evaluations, it would best be used to assist human graders.

This study contributes to the literature on the role of communities of learners in the professional development of physics teachers. It offers insights from the Collabora—A Community of Learners on Laboratory Work program, designed to enhance the use of laboratories in secondary school physics teaching. The program’s foundation rested upon two pillars: a learning community approach and action research, grounded in the findings of physics education research. Furthermore, the program was structured to encompass the core features of effective professional development as outlined in the literature (content focus, active learning opportunities, coherence with teachers’ needs, and sufficient duration). The program spanned 2 years. During the first year, teachers engaged with and discussed different types of experiments, reflected on the assessment of scientific practices, and participated in action research aimed at improving laboratory activities in their classrooms. In the second year, they focused on integrating laboratory work within teaching-learning sequences developed through a backward design process. The research questions of this study were centered on examining the role and relevance of program features, with particular emphasis on the learning community and action research components, and on investigating the changes reported by teachers as a result of participating in the program. The findings emphasize the pivotal role of the teacher community, with reciprocal training identified as the “truly developmental” element. Moreover, they corroborate the relevance of action research in fostering a sense of ownership of research-based innovations. Over the course of the program, teachers reported changes in the personal domain, in the domain of practice, and, particularly in the second year, also in the domain of student outcomes. These changes included the use of different types of experiments, a greater sense of self-efficacy in the laboratory, and an increased focus on the design and assessment of laboratory work. We studied changes through a “growth” lens, both at the group level and within a subset of individual case studies. The latter analysis highlights different possible productive pathways to teachers’ growth, supporting a view of teacher professional development as complex and multifaceted. The program structure facilitated the processes of “enactment” and “reflection” that mediated the various changes.

Despite its crucial importance in physics, there are not many studies focusing on student difficulties and teaching strategies related to the (1D) wave equation in the PER literature. In order to contribute to fill this gap, we conducted a study with university students which focused on specific aspects that are crucial for understanding this equation. Our results include not only key learning difficulties and potential teaching strategies to circumvent them, but they also suggest that students can search for a deeper understanding of physics equations when prompted to do so.

Recent critiques of physics education research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a “causal reasoning primer” in PER, this paper discusses some of the fundamental issues in statistical causal inference. In reviewing these issues, we discuss well-established causal inference methods commonly applied in other fields and discuss their application to PER. Using simulated data sets, we illustrate (i) why analysis for causal inference should control for confounders but not control for mediators and colliders and (ii) that multiple proposed causal models can fit a highly correlated dataset. Finally, we discuss how these causal inference methods can be used to represent and explain existing issues in quantitative PER. Throughout, we discuss a central issue in observational studies: A good quantitative model fit for a proposed causal model is not sufficient to support that proposed model over alternative models. To address this issue, we propose an explicit role for observational studies in PER that draw statistical causal inferences: Proposing future intervention studies and predicting their outcomes. Mirroring the way that theory can motivate experiments in physics, observational studies in PER can predict the causal effects of interventions, and future intervention studies can test those predictions directly.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] Physics lab courses permanently undergo transformations, in recent times especially to adapt to the emergence of new digital technologies and the COVID-19 pandemic in which digital technologies facilitated distance learning. Since these transformations often occur within individual institutions, it is useful to get an overview of these developments by capturing the status quo of digital technologies and the related acquisition of digital competencies in physics lab courses. Thus, we conducted a survey among physics lab instructors ($N=79$) at German, Finnish, and Croatian universities. The findings reveal that lab instructors already use a variety of digital technologies and that the pandemic particularly boosted the use of smartphones and tablets, simulations, and digital tools for communication, collaboration, and organization. The participants generally showed a positive attitude toward using digital technologies in physics lab courses, especially due to their potential for experiments and students’ competence acquisition, motivational effects, and contemporaneity. Acquiring digital competencies is rated as less important than established learning objectives, however, collecting and processing data with digital tools was rated as an important competency that students should acquire. The instructors perceived open forms of labwork and particular digital technologies for specific learning objectives (e.g., microcontrollers for experimental skills) as useful for reaching their learning objectives. Our survey contributes to the reflection of what impact the emergence of digital technologies in our society and the COVID-19 pandemic had on physics lab courses and reveals first indications for the future transformation of hands-on university physics education.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] In this paper, we describe the results of a research project whose goals were to (1) develop and implement video-based experimental investigations using the Investigative Science Learning Environment (ISLE) approach and (2) study how students who engage with video experiments develop scientific abilities and learn physics ideas in comparison to students who do the same investigations using physical apparatus. We developed six parallel ISLE-based investigations for the students to engage in, either with apparatus or with video arrays created in the Pivot platform. We found that substituting 30% of the apparatus-based activities with video-based activities did not affect student development of conceptual physics knowledge. On the other hand, the development of certain scientific abilities was significantly affected by whether students experimented with physical apparatus or used video experiments.

Measurement uncertainty is an important topic in the undergraduate laboratory curriculum. Previous research on student thinking about experimental measurement uncertainty has focused primarily on introductory-level students’ procedural reasoning about data collection and interpretation. In this paper, we extended this prior work to study upper-level students’ thinking about sources of measurement uncertainty across experimental contexts, with a particular focus on classical and quantum mechanics contexts. We developed a survey to probe students’ thinking in the generic question “What comes to mind when you think about measurement uncertainty in [classical/quantum] mechanics?” as well as in a range of specific experimental scenarios and interpreted student responses through the lens of availability and accessibility of knowledge pieces. We found that limitations of the experimental setup were most accessible to students in classical mechanics while principles of the underlying physics theory were most accessible to students in quantum mechanics, even in a context in which this theory was not relevant. We recommend that future research probe which sources of uncertainty experts believe are relevant in which contexts and how instruction in both classical and quantum contexts can help students draw on appropriate sources of uncertainty in classical and quantum experiments.

Course-Based Undergraduate Research Experiences (CUREs) have been shown to provide students with a variety of learning benefits including better conceptual understanding, improved critical thinking and data literacy skills, and increased interest in pursuing scientific careers. Additionally, CUREs provide students with opportunities to participate in authentic research experiences that have a broader impact outside of the classroom. Despite the numerous benefits, the field of astronomy has lagged behind disciplines like biology and chemistry when it comes to including CUREs in the curriculum. Not limited to astronomy, however, is the lack of research opportunities and courses offered to students enrolled in undergraduate degree programs online. In the Fall of 2020, Arizona State University (ASU) introduced the nation’s first online bachelor’s degree program in astronomy and planetary sciences (APS). To make research accessible to a more diverse population of learners, it is imperative that students in this program have access to the same opportunities to participate in authentic research as those in the parallel in-person program. In this work, we describe the development, implementation, and assessment of a fully online CURE for astronomy majors as part of the APS program. We conducted a mixed methods analysis consisting of a Likert style survey administered pre- and postcourse as well as student interviews at the conclusion of the semester. Survey results from the course’s first two offerings ($N=24$) indicated that students’ research self-efficacy and science identity both improved. An exoplanet-specific multiple-choice assessment ($N=26$) showed statistically significant improvements in conceptual understanding postcourse. Additionally, student interview ($N=11$) responses relayed that students felt a stronger sense of belonging to both ASU and the larger astronomy community after participation in the course. The results from this study are encouraging and suggest that student participation in this online CURE led to similar improvements across a variety of outcomes previously identified in studies of in-person CUREs spanning multiple disciplines.

Social learning, the ability to perceive, interpret, and assess the behavior of one’s peers, is crucial for forming meaningful relationships and succeeding in various learning environments. Yet, the rise of online and hybrid settings poses new challenges to socialization. Here, we study the social interactions among 191 high school physics students in Chile, comparing online and hybrid classrooms that were assigned in the COVID-19 pandemic context. We found that students in hybrid settings were more connected and more likely to form casual relationships outside their immediate friend groups, which allowed them to gather new information from diverse sources. Along the same lines, in online classrooms, students who excelled in physics occupied more central positions in social networks. This trend was not evident in hybrid settings, suggesting that when social cues are limited, academic performance gains greater importance in establishing social hierarchies and potentially limiting access to diverse information. Our study highlights the importance of social interactions in educational contexts and raises questions about the impact of relational inaccessibility on virtual learning.

To explore the self-efficacy and physics identity of students across various physics career trajectories, as well as to delve into the motivations of male and female students in choosing to major in physics and pursue teaching, we conducted a survey within the Department of Physics at East China Normal University. This institution offers undergraduate physics programs with both teaching and nonteaching trajectories. The teaching trajectory caters to preservice teachers who will receive certifications to teach physics at the middle or high school level upon completing their degree. A total of 266 undergraduate students participated in the survey, ensuring a balanced representation of both genders and both teaching and nonteaching trajectories. A gender disparity was evident for self-efficacy, while no such gap emerged for physics identity. Conversely, distinct disparities emerged between different career trajectories, with significant differences observed in physics identity between students in the teaching and nonteaching trajectories. A predominant motivation for majoring in physics was “interest,” and the top two reasons for choosing the teaching career were consistent for both genders, with women assigning higher priority to job security.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] This study investigated the development of four new novice graduate teaching assistants (GTAs) and one nontenure track (NTT) faculty member assigned to teach an introductory physics laboratory course based on the investigative science learning environment (ISLE) approach. We describe the training that these instructors received and the progress that they made over their first semester of instruction. We used classroom observations scored using the Danielson Framework and the Reform Teaching Observation Protocol (RTOP) to assess each instructor’s teaching multiple times throughout the semester. All four GTAs demonstrated improvement of various degrees while the NTT did not. Classroom observations and debriefs improved the GTAs’ teaching and were used to tailor weekly training meetings to their needs. Our results suggest that new instructor training should feature specific, tangible teaching actions which new instructors can implement easily in their own classrooms. When these actions directly address issues that instructors have had, as indicated by conversations with the instructors and classroom observations, they were more likely to be adopted and have a noticeable effect on the quality of teaching.

The increasing and diversifying student enrollments in introductory physics courses make reliable, valid, and usable instruments for measuring student skills and gains ever more important. In introductory physics, in addition to teaching facts about mechanics, we also seek to teach our students the skills of “thinking like a physicist,” or expertise in and intuition for physical problem solving. How and when these expert, intuitive problem-solving skills emerge during a STEM education, or what the most effective teaching methods might be, are not certain. A facile survey to measure students’ “physics-thinking” skills in a pretest and post-test format is therefore desirable to measure and evaluate different pedagogical approaches. Prior investigators codified these skills as “epistemic games” (e.g., order-of-magnitude estimation, evaluating extreme cases) and developed and validated the math epistemic games survey (MEGS) to measure students’ ability to employ these techniques. The original survey instrument is reliable and valid but has drawbacks in its length and in students’ ability to recall questions between administrations. We employed factor analysis to split the MEGS into two mutually exclusive subtests and measured them to be equivalently reliable and valid as the full-length MEGS as originally formulated. The “split MEGS” is well suited for use as a pretest and post-test instrument to measure gains in expertise in problem solving in introductory physics courses.

Course-based undergraduate research experiences (CUREs) have emerged as a promising approach to enhance undergraduate STEM education by engaging students in authentic discovery. CUREs integrate genuine research projects into undergraduate courses, providing students with a real research experience while earning academic credit. While CUREs offer similar benefits to traditional research experiences, they have the advantage of reaching a larger student population and being accessible to all enrolled students. In this study, we assess the outcomes of the first reported large-enrollment physics CURE within an introductory physics laboratory. One of the primary learning objectives of this CURE, which took place at the University of Colorado Boulder from 2020 to 2021, was to foster the development of more expertlike attitude and beliefs about experimental physics among the students. To evaluate the impact of the CURE along this dimension, we employed the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS). Precourse and postcourse E-CLASS responses were collected from the CURE participants and compared with data from 363 other first-year physics labs, representing over 20 000 students. We found that students enrolled in the C-PhLARE CURE led to significantly higher scores on many of the E-CLASS items, even when controlling for precourse scores, students’ majors, and students’ genders. In particular, we observed that the C-PhLARE CURE had a significant effect on many of the E-CLASS items aligned with the objectives of the course; notably, students had more expertlike views in areas such as communicating scientific findings to peers, understandings of authentic research practices, and students’ belief in their own research capabilities. Additionally, we found no statistically significant negative impacts on any of the E-CLASS items as compared to the effects of other first-year physics labs. These results demonstrate the efficacy of this CURE in impacting students’ views of experimental physics.

Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for physics education research, and the challenges in developing them such as field testing with representative populations, this study seeks to examine to what extent a generative large language model could be utilized to generate a synthetic dataset for the FCI that exhibits content-related variability in responses. We use the recently introduced ChatGPT based on the GPT 4 generative large language model and investigate to what extent ChatGPT could solve the FCI accurately (RQ1) and could be prompted to solve the FCI as if it were a student belonging to a different cohort (RQ2). Furthermore, we study, to what extent ChatGPT could be prompted to solve the FCI as if it were a student having a different force- and mechanics-related preconception (RQ3). In alignment with other research, we found that ChatGPT could accurately solve the FCI. We furthermore found that prompting ChatGPT to respond to the inventory as if it belonged to a different cohort yielded no variance in responses, however, responding as if it had a certain preconception introduced much variance in responses that approximate real human responses on the FCI in some regards.

Much of physics involves the construction and interpretation of equations. Research on physics students’ understanding and application of mathematics has employed Sherin’s symbolic forms or Fauconnier and Turner’s conceptual blending as analytical frameworks. However, previous symbolic forms analyses have commonly treated students’ in-context understanding as their conceptual schema, which was designed to represent the acontextual, mathematical justification of the symbol template (structure of the expression). Furthermore, most conceptual blending analyses in this area have not included a generic space to specify the underlying structure of a math-physics blend. We describe a conceptual blending model for equation construction and interpretation, which we call symbolic blending, that incorporates the components of symbolic forms with the conceptual schema as the generic space that structures the blend of a symbol template space with a contextual input space. This combination complements symbolic forms analysis with contextual meaning and provides an underlying structure for the analysis of student understanding of equations as a conceptual blend. We present this model in the context of student construction of non-Cartesian differential length vectors. We illustrate the affordances of such a model within this context and expand this approach to other contexts within our research. The model further allows us to reinterpret and extend literature that has used either symbolic forms or conceptual blending.

At large institutions of higher education, students frequently have a choice whether to attend the introductory physics sequence asynchronously online, on-site in a traditional lecture setting, or in a reformed studio setting. In this study, we investigate how these different settings are correlated with measures of self-efficacy, interest in physics, and success in subsequent physics and engineering courses, which have the introductory physics sequence as prerequisites. As previous research indicates, some of these measures may depend on gender. We found that the course setting had no significant correlation with the grade in subsequent courses, but that studio settings gave students the feeling of being better prepared, particularly for subsequent courses that included laboratory or recitation components. We also found that gender was correlated with measures of interest in physics, where female students expressed significantly less interest in the subject, regardless of course setting.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] Uncertainty is an important concept in physics laboratory instruction. However, little work has examined how students reason about uncertainty beyond the introductory (intro) level. In this work we aimed to compare intro and beyond-intro students’ ideas about uncertainty. We administered a survey to students at 10 different universities with questions probing procedural reasoning about measurement, student-identified sources of uncertainty, and predictive reasoning about data distributions. We found that intro and beyond-intro students answered similarly on questions where intro students already exhibited expert-level reasoning, such as in comparing two data sets with the same mean but different spreads, identifying limitations in an experimental setup, and predicting how a data distribution would change if more data were collected. For other questions, beyond-intro students generally exhibited more expertlike reasoning than intro students, such as when determining whether two sets of data agree, identifying principles of measurement that contribute to spread, and predicting how a data distribution would change if better data were collected. Neither differences in institutions, student majors, lab courses taken, nor research experience were able to fully explain the variability between intro and beyond-intro student responses. These results call for further research to better understand how students’ ideas about uncertainty develop beyond the intro level.

Understanding measurement uncertainty is crucial in experimental physics, as it enables accurate and reliable measurements, facilitates comparison between measurements, and aids in designing experiments. Consequently, measurement uncertainty has emerged as a critical learning goal for many introductory physics labs. Here, we explore the impact of a recently transformed introductory physics lab at the University of Colorado Boulder on student understanding and interpretation of measurement uncertainty. The transformed course was explicitly designed to prioritize understanding measurement uncertainties as a learning goal and replaced verification labs with measurements where students could not predict the outcomes in advance. We used the physics measurement questionnaire to assess changes in student reasoning about measurement uncertainty at the beginning and end of the semester. Using a subparadigm coding scheme, we assessed different types of prevalent student reasoning and observed for trends in reasoning surrounding measurement uncertainty from the beginning to end of the lab course. Clustering algorithms were utilized to categorize student reasoning and compare these pre- and postsurvey responses. This analysis offers valuable insights into students’ reasoning about measurement uncertainty, including the diversity of initial reasoning clusters and the narrowing of reasoning elements into primarily more expertlike responses after the transformed course. However, challenges were observed in transitioning students from certain clusters, especially those that exhibited brevity in their presurvey responses. Overall, the findings reveal the potential for targeted interventions to deepen these students’ understanding of measurement uncertainty in experimental physics and underscore the significance of evidence-based instructional strategies in physics labs for improving student learning outcomes.

In this study, a conceptual framework of measurement uncertainty was developed and used to guide the development of a multiple-choice concept test for the assessment of students’ knowledge integration in learning measurement uncertainty. Based on assessment data and interview results, students were identified into three levels of knowledge integration including novice, intermediate, and expertlike. The reasoning pathways of students at different levels revealed a progression of reasoning from a rudimentary surface level to a deep understanding that can be mapped in the conceptual framework. This work demonstrates the possibility of identifying a quantitative categorization scheme to model knowledge integration as well as its utility in teaching and learning. Overall, the assessments and interviews revealed common and persistent difficulties in students’ understanding of measurement uncertainty. In addition, students at different levels of knowledge integration demonstrate unique types of knowledge states that can be represented in the conceptual framework, making it a useful tool for analyzing different reasoning pathways and knowledge structures.