The ability to express scientific concepts in mathematical terms and integrate scientific and mathematical reasoning about a phenomenon is a foundational cognitive process involved in scientific thinking. This process called “blended math-science sensemaking” (MSS) is a desired skill for all science, technology, engineering, and math (STEM) students, but few students are learning it, and there is little research on how to teach it. In this work we introduce the development and testing of a novel instructional method for teaching MSS that is suitable for use in STEM courses in undergraduate and K-12 educational settings. This study builds on our past work on developing and validating a framework for characterizing in detail the cognitive levels involved in such sensemaking. This work uses the unique power of interactive simulations for assessing and developing MSS. We designed instructional activities to help students use MSS in the contexts of heat capacity and Coulomb’s law. The heat capacity activity was piloted in a freshmen chemistry course and the Coulomb’s law activity was piloted in a freshmen physics course. The results indicate that for students who came in with no knowledge of the relevant equation the activity supported the development of both the equation, and their understanding of the mathematical relationships of the equation. These results indicate that the teaching approach helps students engage in MSS at higher levels of cognitive complexity.

Students’ motivational beliefs, such as disciplinary intelligence mindsets, can influence their physics performance and persistence. Intelligence mindset beliefs have long been argued to fall along of continuum between fixed and growth mindsets. Those with fixed physics mindsets believe that ability in physics is innate and unchangable, while those with growth mindset believe that ability in physics can be developed with effort. More recent research with physical science and engineering majors suggests these are somewhat separable beliefs, with some students believing aspects of both fixed and growth mindsets, and that students can hold different beliefs about typical other students versus beliefs about themselves (e.g., others could improve through effort but they themselves could not). In this study, 419 students in physics 1 for students pursuing bioscience majors took pre- and post-physics mindset surveys. We investigated whether the physics mindset views of students pursuing bioscience or health-related majors were separable into more nuanced dimensions, if the means and distribution of these views varied by gender or sex and over time, and if any of these views predicted course grade. Replicating prior findings with physical science and engineering majors, we found that intelligence mindsets can be divided into four separable but correlated constructs: my ability, my growth, others’ ability, and others’ growth. Further, in this bioscience or health-related majors group, the “ability” beliefs grew stronger and the “growth” beliefs became weaker over time. These shifts were particularly strong for women. The changes in beliefs were also stronger for “my” beliefs than “others” beliefs for both men and women Unfortunately, my ability and my growth scores were also the strongest predictors of course grades above and beyond academic preparation differences as assessed by high school GPA and SAT/ACT math scores. These findings have implications for eliminating classroom inequities, such as through the development of new mindset interventions.

Collaborative learning with peers can lead to students learning from each other and solving physics problems correctly not only in situations in which one student knows how to solve the problems but also when none of the students can solve the problems alone. We define the rate of construction as the percentage of groups collaborating on problem solving that solve the problem correctly out of all groups having at least one member who answered correctly and one incorrectly while solving the same problem individually first. We define the rate of co-construction on each problem as the percentage of collaborating groups that answered it correctly if no student in the group individually answered it correctly before the collaborative work. In this study, we investigated student learning measured by student performance on a validated quantum mechanics survey and rates of construction and co-construction of knowledge when students first worked individually after lecture-based instruction in relevant concepts and then worked with peers during class without receiving any feedback from the course instructor. We find that construction of knowledge consistently occurred at a high rate during peer collaboration. However, rates of co-construction were more varied. High rates of co-construction were generally achieved when approximately half of the students knew the correct answers initially. We also conducted an analysis of some of the survey questions that correlate with high rates of co-construction to gain some insight into what students converged on after peer interaction and what types of difficulties were reduced. Our findings can be valuable for instructors who want to provide in-class and out-of-class opportunities for peer collaboration in their physics courses.

The Energy and Momentum Conceptual Survey (EMCS) is a multiple-choice survey that contains conceptual problems involving a variety of energy and momentum concepts covered in a typical introductory physics course for science and engineering majors. Prior studies suggest that many concepts on the survey are challenging for introductory physics students and the average student scores after traditional lecture-based instruction are low. The research presented here investigates the progression in student understanding of the EMCS concepts including their evolution from the beginning to the end of their courses in introductory and advanced-level undergraduate physics after traditional lecture-based instruction. We find that on all EMCS questions on which less than 50% of the introductory physics students answered a question correctly after traditional instruction, less than two-thirds of the upper-level undergraduate students provided the correct response after traditional lecture-based instruction. We discuss the EMCS questions that remain challenging and the common alternate conceptions among upper-level students. The findings presented here are consistent with prior research showing that traditional instruction in upper-level courses, which typically focuses primarily on quantitative problem solving and often incentivizes the use of algorithmic approaches, is not effective in helping many students develop a functional understanding of underlying concepts.

A significant proportion of universities throughout the world switched from conventional face-to-face course delivery to emergency remote teaching (ERT) in response to the pervasive COVID-19 outbreak. A series of challenges are faced by both teachers and students as a result of the rapid and abrupt switch to ERT. Within the context of ERT, physics courses encounter certain challenges in contrast to certain liberal arts courses. In particular, this study sought to determine whether physics students and nonphysics students had diverse levels of flow experience and cognitive load when applying ERT during the global pandemic period. Furthermore, this study examined whether ERT for physics students varied depending on gender and educational level. Following the completion of their ERT courses at the end of both the Spring semester of 2020 and the Fall semester of 2022, a total of 1073 participants, including both physics majors and nonphysics majors, participated in our research. From the result, physics students had better performance in the flow experience encompassing its three constructs: enjoyment, engagement, and control. Physics students demonstrated a higher germane cognitive load and a lower extraneous cognitive load when compared to nonphysics students. Moreover, a considerably higher extraneous cognitive load was observed among male physics students than among their female counterparts during the ERT. Nonetheless, physics students at undergraduate and graduate levels did not significantly differ in their flow experiences or cognitive load. Overall, physics majors had a more positive perception of their ERT learning experience, and the impact of ERT on physics students was significantly less detrimental.

Research-validated multiple-choice questions comprise an easy-to-implement instructional tool that serves to scaffold student learning and formatively assess students’ knowledge. We present findings from the implementation, in consecutive years, of a research-validated multiple-choice question sequence [referred to in this study as a clicker question sequence (CQS)] on quantum measurement as it applies to two-state quantum systems. This study was conducted in an advanced undergraduate quantum mechanics course, in both online and in-person learning environments across three years. Student learning was assessed after traditional lecture-based instruction in relevant concepts, and their performance was compared with that on a similar assessment given after engaging with the CQS. We analyze, compare, and discuss the trends observed in the three implementations.

Understanding how relationships between instructors and students develop is important for understanding the undergraduate student experience. We expect the development of positive relationships is related to the social practices (e.g., greetings, using names, sympathizing, or empathizing with students) that instructors use in the course of normal classroom interactions with students. We recorded interactions between instructors and students in remote synchronous online physics problem-solving sessions and surveyed students about their perceptions of their instructors. We selected the highest-rated instructor and lowest-rated instructor in our sample and identified social practices in their conversations with students. We first characterized the frequency of social practice usage by each instructor in their conversations with students. We find that both instructors relied on a set of core social practices in most conversations with students, but that our higher-rated instructor used comparatively more positive commentary and sympathizing or empathizing behaviors than our lower-rated instructor. In comparison, our lower-rated instructor engaged in more negative commentary. Using network analysis, we then explored patterns in co-occurrences of social practices used by each instructor moment-to-moment in conversations and compared the instructors’ social practice network patterns. We find that our higher rated-instructor used a greater variety of social practices during moment-to-moment interactions with students, while our lower-rated instructor spent most of his time focused on classroom business. We suggest that professional development for instructors should include guidance on how messages are delivered in classes and encourage the use of high-impact social practices to foster positive relationships with students.

The recent advancement of large language models presents numerous opportunities for teaching and learning. Despite widespread public debate regarding the use of large language models, empirical research on their opportunities and risks in education remains limited. In this work, we demonstrate the qualities and shortcomings of using ChatGPT 3.5 for physics task development by prospective teachers. In a randomized controlled trial, 26 prospective physics teacher students were divided into two groups: the first group used ChatGPT 3.5 to develop text-based physics tasks for four different concepts in the field of kinematics for 10th-grade high school students, while the second group used a classical textbook to create tasks for the same concepts and target group. The results indicate no difference in task correctness, but students using the textbook achieved a higher clarity and more frequently embedded their questions in a meaningful context. Both groups adapted the level of task difficulty easily to the target group but struggled strongly with sufficient task specificity, i.e., relevant information to solve the tasks was missing. Students using ChatGPT for problem posing rated high system usability but experienced difficulties with output quality. These results provide insights into the opportunities and pitfalls of using large language models in education.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] Most, if not all, physics undergraduate degree programs include instructional lab experiences. Physics lab instructors, both faculty and staff, are instrumental to student learning in instructional physics labs. However, the faculty-staff dichotomy belies the complex, varied, and multifaceted landscape of positions that lab instructors hold in the fabrics of physics departments. Here we present the results of a mixed-methods study of the people who teach instructional labs and their professional contexts. Recruiting physics lab instructors across the United States, we collected 84 survey responses and conducted 12 in-depth interviews about their job characteristics, professional identities, resources, and experiences. Our investigation reveals that lab instructors vary in terms of their official titles, job descriptions, formal duties, personal agency, and access to resources. We also identified common themes around the value of instructional labs, mismatched job descriptions, and a broad set of necessary skills and expertise. Our results suggest that instructors often occupy overlapping roles that fall in between more canonical jobs in physics departments. By understanding the professional contexts of physics lab instructors, the rest of the physics community can better promote and engage with their critical work, improving laboratory learning both for students and for the lab instructors who teach and support them.

We add to a growing literature suggesting that demographic grade gaps should be attributed to biases embedded in the courses themselves. Changes in the structure of two different introductory physics classes were made while leaving the topics covered and the level of coverage unchanged. First, a class where conceptual issues were studied before doing any complicated calculations had zero final exam grade gap between students from underrepresented racial or ethnic groups and their peers. Next, four classes that offered students a retake exam each week between the regular bi-weekly exams during the term had zero gender gap in course grades. Our analysis indicates that demographic grade gaps can be attributed to the course structure (a course deficit model) rather than to student preparation (a student deficit model).

Students’ self-efficacy, interest, and perceived recognition from others in a given field have been shown to be very important for the development of their identity in that field, which is a critical predictor of students’ major and career decisions. Prior research suggests that students’ self-efficacy and interest play an important role in their engagement, performance, and persistence in STEM fields. However, very little has been investigated about the role of perceived recognition and validation by instructors on students’ self-efficacy and interest. Moreover, prior quantitative studies show that women often report a lower level of physics-perceived recognition, self-efficacy, and interest. In this study, we analyzed data from individual interviews with 38 female students to investigate their learning experiences in physics courses to obtain a qualitative understanding of the factors that shape their self-efficacy and interest. The results reveal that many interviewed women experienced negative perceived recognition or lack of positive recognition from their physics instructors or teaching assistants (TAs), which fell into three categories: feeling belittled for questions or efforts, feeling negatively recognized regarding abilities and potential, and feeling marginalized due to differential gender dynamics. In contrast, only a few interviewed women reported positive perceived recognition from instructors or TAs, which included recognition of abilities, encouragement to pursue goals, and acknowledgment of the normality of struggles. We find that positive perceived recognition enhanced students’ self-efficacy and interest, while negative perceived recognition or lack of positive recognition undermined them. These findings provide valuable insights for physics educators to improve their interactions with students by providing positive recognition and validation. Our research also suggests that it is important for instructors or TAs to internalize that it is not their intentions that matter but the impact they are having on their students.

Achieving knowledge integration for deep learning requires students to develop well-connected knowledge structures through the central idea of a concept. However, a number of studies on student learning in momentum have revealed persistent difficulties in understanding its fundamental concept, particularly in relating net force, time, and change in momentum with each other. This study evaluates the effectiveness of a teaching intervention based on the conceptual framework of momentum that targets knowledge integration. The evaluation outcomes of the intervention provide encouraging evidence suggesting that the conceptual framework model can be a valuable guide to the development of instruction for promoting knowledge integration and deep learning.

[This paper is part of the Focused Collection on Qualitative Methods in PER: A Critical Examination.] Qualitative research methods have provided key insights in physics education research (PER) by drawing on non-numerical data such as text or video data. While different methods towards qualitative research exist, they share two essential steps: recognizing patterns in the data and interpreting these patterns. Although these methods have led to the development of rigorous theory, there are challenges: As such methods require a series of judgments by the analyst, they are difficult to validate and reproduce. Further, they are hard to scale so that they are unavailable to the analysis of large-scale data. In this way, important phenomena may remain inaccessible to qualitative analysis. Reacting to these challenges and leveraging the potential of emerging methods of artificial intelligence (AI) such as machine learning and natural language processing, sociologist Nelson has proposed the concept of computational grounded theory (CGT). CGT proceeds in a process of three consecutive steps: In the first step, one leverages the power of computational techniques, especially natural language processing and unsupervised machine learning techniques, for pattern detection in large datasets—those of a size and scope that may prohibit human-driven analysis from the outset. In the second step, one relies on the integrative and interpretative capabilities of human researchers to add quality and depth to the quantity and breadth of the first step. In the last step, one again uses computational techniques to test the extent to which the detected and refined patterns from the first and second step hold throughout the whole dataset under investigation. Interestingly, CGT does not aim at simply automating parts of the qualitative process by using AI, but rather aims at integrating AI into the human analyst’s workflow within a qualitative analysis. This leads to an analytical system that can do something that is quantitatively and qualitatively different from what a human or machine can do alone. In this way, CGT aims at addressing questions about validity, reproducibility, and scalability in qualitative research while preserving the theoretical sensitivity and unique inferencing capabilities of the human analyst. In this paper, we provide a primer on CGT, present how it can be used to investigate the physics problem-solving approaches of $N=417$ students based on textual data, and discuss CGT’s potentials and challenges in PER. In consequence, this paper can provide critical input to the discussion of how emerging AI technologies can provide new avenues in qualitative PER.

[This paper is part of the Focused Collection on Qualitative Methods in PER: A Critical Examination.] Inquiring about students’ learning and their difficulties understanding the concepts and models of physics is a familiar challenge in physics education research. Researchers have developed various methodologies, such as phenomenography, to address it. Phenomenography is an empirical approach to determining how people experience and understand aspects of their surroundings and the physical world in qualitatively different ways. Rigorous phenomenographic analysis can be used to define categories to describe general ways the students experience the research phenomenon. The phenomenographic analysis process focuses on critical aspects of the collective experience rather than the richness of individual experience, assuming that there are a limited number of categories to describe the variations of experience for a given phenomenon. The possibility of defining a limited number of categories for experiencing a phenomenon on a collective level is one characteristic that makes phenomenographic analysis particularly appropriate for research that aims to enhance teaching and learning. We shall critically examine the strengths and weaknesses of phenomenographic research in this paper. The strengths include integral and holistic descriptions of people’s conceptions. Weaknesses include the risks of equating participants’ experiences with their descriptions of their own experiences. Our contribution weighs up the literature’s warnings about the validity and reliability of phenomenographic research. To provide an overview of phenomenography in physics education research, we conducted a literature review which identified and analyzed different approaches to data collection, data analysis, rigor, presentation of the results, and scope. We conclude by considering phenomenography as a research approach to learn how students perceive a concrete learning phenomenon, thus, providing an essential teaching design and preparation guide for instructors.

The preparation gap [Salehi et al., Phys. Rev. Phys. Educ. Res. 15, 020114 (2019)] refers to gaps in students’ prior knowledge that can negatively affect their learning as they engage in introductory physics courses. To better characterize the gap, the current study distinguished the impact of various prior knowledge components on learning gains. Measured components came from within the course domain (e.g., energy and force, angular kinematics) and outside it (e.g., algebra, vectors, calculus, and scientific reasoning). We conducted the study in two different institutional contexts: An algebra-based course offered at a Northeastern State University (NESU) and a calculus-based course offered at a Midwestern Private University (MWPU). Furthermore, we defined three levels of physics learning outcome measures with increasing difficulty. Multiple regression analysis was used to predict learning gains with the various prior knowledge components as predictor variables. The results indicate that greater prior knowledge from both within and outside the domain predicted higher learning gains and explained 30%–50% of the variance in outcome measures. Predictive, in-domain prior knowledge was the same for both groups—i.e., prior knowledge of energy and force, as measured by the Mechanics Baseline Test [Hestenes and Wells, Phys. Teach. 30, 159 (1992)]. Predictive, outside-domain prior knowledge differed between the groups. Better scientific reasoning was highly predictive of learning in the NESU (algebra-based) group but did not predict learning in the MWPU (calculus-based) group. Math prior knowledge predicted learning in both groups, although different topics within the math domain. These results suggest that measuring distinguishable components of prior knowledge will better characterize the preparation gap in ways that can be informative to educators. Specifically, measuring multiple, distinct types of prior knowledge can indicate which types are leading to a preparation gap for some students, putting them at a disadvantage for learning, whereas measuring a single type of prior knowledge or measuring prior knowledge too coarsely (without distinguishing among types) cannot provide sufficient diagnostic power.

We aim to graphically analyze the depth of conceptual understanding behind the Force Concept Inventory (FCI) responses of students, focusing on three questions (questions 1, 15, and 28). In our study, we created and implemented subquestions to clarify and quantify the students’ reasoning steps in reaching their responses to the original FCI questions. We used alluvial diagrams to visualize the responses of students on the subquestions and original FCI questions, representing the transition rates across the question options as flows. The flows represent common patterns of consistency and inconsistency in the students’ conceptual understandings across the original FCI question and the subquestions. By combining subquestions and alluvial diagrams as we did, we are able to efficiently analyze the depth of understanding for a given situation and to visualize the existence of a specific misconception. For example, we found that even among those who correctly answered FCI question 15 about Newton’s third law, many students incorrectly answered a corresponding subquestion asking about whether Newton’s third law holds at the end of the interaction between the two objects. This misconception turned out to be fairly robust, as these same students had a similar answering pattern for question 28. Alluvial diagrams can be used for a broad range of applications, including analysis of multiple-tier tests, testlets (e.g., FCI questions 8 through 11), tests for representational consistency (e.g., a representational variant of the FCI), and so on.

[This paper is part of the Focused Collection on Qualitative Methods in PER: A Critical Examination.] Qualitative studies in the domain of physics education research have become more common in the last several decades. Methodologically, this has been marked by an expansion of the types of data collected in physics education research (PER): namely, in the use of individual and group interviews, problem-solving sessions, and classroom observations—often in the form of video data. However, depictions of those video data in publications, a key opportunity for communicating analytic approaches and results, are often left out or are constructed without addressing some important implications of video data depictions. In this paper, we critically examine how video data can be depicted, first providing a rough taxonomy of depiction approaches. We then emphasize the case of line illustrations as a depiction approach with significant potential for improving (i) the transparency of analytic perspectives, (ii) communicational clarity, and (iii) the handling of ethical issues, such as participant anonymization, followed by a discussion of the limitations and challenges of using illustrations. In the Appendix, we provide a practical guide for researchers interested in making their own illustrations for academic publishing purposes.

[This paper is part of the Focused Collection on Qualitative Methods in PER: A Critical Examination.] Within physics education research (PER), resource theory has proven to be a useful framework for investigating knowledge and learning and informing instructional design. To analyze learning over longer timescales and across cases, PER scholars must first identify and describe the resources activated within and across physics contexts and domains. Despite its importance, a reliable method for identifying resources has not been clearly outlined. This paper presents guidelines for the design of research aimed at identifying knowledge resources. We begin by describing the origin, assumptions, and utility of resource theory. We then introduce methods of data collection and analysis. We end with a discussion of validity and reliability, drawing connections with general principles of qualitative research. With this work, we hope to promote coordination among the many PER scholars who utilize resource theory and to invite new scholars to join in its application and development.