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.

Demonstration interviews with 27 high school students (18–19 years old) were conducted in Zagreb, Croatia, using several standard experiments on interference and diffraction of light. Students were asked for their predictions, observations, and explanations of the experiments. In this process, many student difficulties were identified, both regarding their understanding of interference and diffraction of light, but also regarding their skills of systematic observation and description of experimental patterns. The observed difficulties were analyzed in the resource-based model, suggesting the activation of some $p$ prims, as well as other cognitive resources in the process of students’ attempts to predict, describe, and explain the interference and diffraction patterns.

We conducted a study with introductory and upper-division physics students in a Mexican university to learn how students independently recognize the electric field’s main characteristics in the electric field lines diagram and as a source or target representation in conversion processes. We used the theory of registers of semiotic representations and a phenomenographic approach as a framework to analyze data. The recognition and conversion abilities were explored through interpretation and construction tasks. We identified students’ main difficulties in recognition and conversion in the interpretation and construction tasks. In conversion processes, we found that when the electric field lines diagram is the source representation, students do not interpret the field lines’ density as the magnitude of the electric field. The difficulties of interpretation that arise in these conversion processes depend partially on the target representation. We also found that constructing electric field lines is especially difficult for students at both introductory and upper-division levels. Most students would prefer to draw vector field plots instead. We recommend that electricity and magnetism teachers and researchers be aware of the difficulties that the recognition and conversion in interpretation and construction tasks may represent for their students in understanding the electric field concept.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] This paper presents a historical analysis of the development, implementation, research, and assessment of an evidence-based introductory algebra-based physics course. The course is laboratory based and taught with an inquiry-based pedagogy. The course has remained unique and has been taught consistently for over 20 years. It is taught predominantly to health science majors and is completely laboratory based without a lecture and without a required text. Students learn by working through a guided inquiry manual in groups, doing experiments to explore the nature of the world around them. They develop both quantitative and qualitative models based on their experiments. The focus is as much on critical thinking, modeling, and thinking like a scientist, as on physics content. I discuss the course throughout the years, presented in the context of the field of physics education research historically and the state of our department locally. The results of multiple forms of assessment are presented chronologically. The assessments were designed to answer research questions about the students’ conceptual understanding and thinking skills, the effects of class size expansion, and questions about learning assistants’ pedagogical content knowledge. One finding was that the conceptual understanding of students in the inquiry-based course was significantly higher than students taught traditionally but not significantly different from students taught by other research-informed pedagogies. However, we found that students taught in the inquiry-based format showed more evidence of the application of thinking skills as assessed by our thinking skills rubric in free-response exam problems. We have demonstrated that it is possible to expand a laboratory-based, inquiry-based physics course from sections of 24 students to sections of 60 students with the help of learning assistants and we have developed and validated a set of questions that can be used to measure learning assistants’ pedagogical content knowledge or for learning assistant training purposes.

Physicists, physics students, and community members come together and engage with physics content through various forms of informal education programming. Our prior work has shown that the landscape of informal physics programs in the United States has a variety of formats, audience demographics, and content. Informal physics programs have many benefits for both facilitators and community members; however, we do not fully understand how the variety of program structures, designs, and organizations affect participants’ experiences. In this study, we apply an organizational theory framework to study the connectedness of six functional aspects of informal physics programs: Assessment, Audience, Institution, Personnel, Program, and Resources. We interview lead facilitators about the design and structure of their programs. Here we present an in-depth qualitative analysis of three cases. We find that the personnel, consisting of the faculty, staff, and physics students facilitating the educational activities, have a prominent role in these programs. Our findings show how personnel members influence the physics content, activities, and interactions with the audience. We discuss how understanding these aspects can help support improving the functionality of informal physics programs in equitable and inclusive ways.

Previous studies have shown the important role of different representations in the teaching and learning of physics. In this study, we used eye tracking to investigate the effect of different representations on the process of answering conceptual questions. We compared students’ scores and eye-tracking measures on isomorphic questions which contained graphical, pictorial, and verbal representations. On average, in two-thirds of cases, students were consistent in their answers (correct or incorrect) across all three representations. There was no statistically significant difference in students’ scores for different representations. However, eye-tracking measures suggest that it was easiest for students to extract information from verbal representations and most difficult from pictorial representations for the conceptual questions used in this study. These results could be useful to teachers and researchers when creating conceptual questions and, more generally, when teaching with multiple representations.

Textbooks play a fundamental role in teaching and learning in school science classrooms. In this paper, we investigate the presentation of the nature of the electromagnetic field in a dozen of the world’s most popular introductory university physics textbooks. We analyze, from an epistemologically based teaching approach, the didactic treatment of the electromagnetic field in relation to its sources, Maxwell’s laws and electromagnetic waves. With this objective, we elaborate a rational reconstruction of the developments that led to the formulation of the nature of the electromagnetic field, Maxwell’s laws and their meaning, as well as electromagnetic waves. Next, we formulate criteria based on the key aspects derived from the reconstruction that are useful in the evaluation of electromagnetism textbooks at the introductory level and apply them to the sample of selected books. In light of the results, we reflect on their consequences for teaching. Our analysis indicates the existence of certain inconsistencies in the approach to the electromagnetic field and its relationship with its sources, Maxwell’s laws and electromagnetic waves in many of the books analyzed.

We discuss the development and validation of the long version of a conceptual multiple-choice survey instrument called the Survey of Thermodynamic Processes and First and Second Laws-Long suitable for introductory physics courses. This version of the survey instrument is a longer version of the original shorter version developed and validated earlier. The 19 contexts including the exact wording of all of the problem situations posed in the two versions of the survey instrument are identical and the difference between the long and short versions of the instrument is only in the multiple-choice options. In particular, in the longer version of the survey instrument, there are no alternative conceptions explicitly embedded in the four multiple-choice options students choose from and the questions asked in a given context in one item of the shorter survey instrument were split into several items focusing, e.g., on different thermodynamic variables. After the development and validation of the longer version of the survey instrument, the final version was administered in 12 different in-person classes (four different institutions) in which students answered the questions in-class on paper scantron forms with the instructor as the proctor and 12 different in-person classes (five different institutions) in which students answered the questions online on Qualtrics within a two-hour period. This longer version of the survey instrument was administered to introductory physics students in various traditionally taught calculus-based and algebra-based classes before and after traditional lecture-based instruction in relevant concepts. It was also administered to upper-level undergraduates majoring in physics and Ph.D. students taught traditionally for bench marking purposes and for concurrent validity, which involved comparing advanced students’ performance with those of introductory students for whom the survey is intended. Similar to the shorter version, we find that although the longer version of the survey instrument focuses on thermodynamics concepts covered in introductory courses, it is challenging even for advanced students. A comparison with the baseline data on the longer version of the validated survey instrument presented here can help instructors evaluate the effectiveness of innovative pedagogies designed to help students develop a solid grasp of these concepts.

We have investigated the temporal patterns of algebra ($N=606$) and calculus ($N=507$) introductory physics students practicing multiple basic physics topics several times throughout the semester using an online mastery homework application called science, technology, engineering, and mathematics (STEM) fluency aimed at improving basic physics skills. For all skill practice categories, we observed an increase in measures of student accuracy, such as a decrease in the number of questions attempted to reach mastery, and a decrease in response time per question, resulting in an overall decrease in the total time spent on the assignments. The findings in this study show that there are several factors that impact a student’s performance and evolution on the mastery assignments throughout the semester. For example, using linear mixed modeling, we report that students with lower math preparation for the physics class start with lower accuracy and slower response times on the mastery assignments than students with higher math preparation. However, by the end of the semester, the less prepared students reach similar performance levels to their more prepared classmates on the mastery assignments. This suggests that STEM fluency is a useful tool for instructors to implement to refresh student’s basic math skills. Additionally, gender and procrastination habits impact the effectiveness and progression of the student’s response time and accuracy on the STEM fluency assignments throughout the semester. We find that women initially answer more questions in the same amount of time as men before reaching mastery. As the semester progresses and students practice the categories more, this performance gap diminishes between males and females. In addition, we find that students who procrastinate (those who wait until the final few hours to complete the assignments) are spending more time on the assignments despite answering a similar number of questions as compared to students who do not procrastinate. We also find that student mindset (growth vs fixed mindset) was not related to a student’s progress on the online mastery assignments. Finally, we find that STEM fluency practice improves performance beyond the effects of other components of instruction, such as lectures, group-work recitations, and homework assignments.

There is growing awareness that established structures of higher education are often predicated on problematic assumptions about merit, excellence, and rigor. Doctoral qualifying exams, for example, are required to advance to candidacy in many Ph.D. programs despite decades of documented concerns about the implications of standard modes for student equity and well-being. As more Ph.D. programs move to reform these exams and candidacy requirements, it is important to understand how Ph.D. programs, as academic organizations, construct the significance of the qualifying exam. A sociocultural lens suggests qualifying exams and the learning that enables their passage are symbolic rituals that move doctoral students from legitimate peripheral participation toward full membership and belonging in academic communities of practice. We conducted a comparative case study to understand how two Ph.D. programs in the physical sciences that have reformed their candidacy requirements—one elite and one middle ranked but striving for respect—constructed the significance and purpose of their qualifying exam and the broader transition to candidacy. Our inquiry included the contexts and mechanisms that mediated student learning. Through interviews with faculty, staff, and students, we found that the Ph.D. programs’ recognition of their status within their respective disciplines emerged as a crucial component in constructions about the significance of exams and candidacy. The middle-ranked Ph.D. program changed the exam and candidacy structure to reflect legitimate practices in their discipline. The elite Ph.D. program created multiple pathways toward candidacy to mitigate long-standing concerns about gender equity and student well-being. Despite the structural changes, the Ph.D. programs left intact cultural understandings of merit, excellence, and rigor that maintain inequity in doctoral socialization. Our findings suggest that researchers and practitioners should pay more attention to designing and implementing structures that facilitate faculty assessments of doctoral student learning.

The multiple-choice section of the final examination for the first-year Advanced Physics I course at the University of Newcastle, Australia between 2010 and 2018 was investigated for gender bias. A Mantel-Haenszel analysis revealed that approximately 20% of the multiple-choice questions exhibited statistically significant gender bias. A schema for characterizing the multiple-choice questions was proposed and used to analyze the entire question set. Male bias questions showed moderate to large bias and tended to include characteristics related to visualization, though not images. Several questions exhibited a moderate bias in favor of females and were characterized by requiring a numerical calculation involving a simple one-step equation. These results indicate that with continued development, gender bias analysis of physics questions based on a characterization schema may be used as a routine tool for testing for the presence and origin of gender gaps in student performance.

The aim of this research study was to explore learning, i.e., changes in the knowledge structures of three in-service science teachers for the subject of active galaxies, through group activity conducted as part of a teacher training program. Qualitative methods were used in this study consisting of creating visual representations of the teachers’ knowledge structures by analyzing texts, drawings, and data obtained through observations of and interviews with the teachers. The results show that new information acquired through conversations was incorporated into the teachers’ preexisting knowledge structures through elaboration and organization. In the case of a teacher with a cognitive conflict, acceptance of the new information depended on the teacher’s level of satisfaction with the explanations given. The main influence factor for the modification of knowledge structures was the teachers’ orientation to science teaching. These findings suggest that in-service teacher training based on group activities can effectively enhance teachers’ confidence in content knowledge (CK) of recent updates in scientific research. However, some format modifications are needed to guarantee efficient learning, such as the addition of a CK representation stage and discussion items based on the preactivity knowledge structure selected through expert review.

The practices of a departmental action team (DAT) are examined to characterize their change effort in their undergraduate program. The DAT’s effort was guided by the DAT model’s six core principles. The core principles are grounded in best practices for higher education and organizational change literature. Meeting minutes, facilitator journal entries, and exit interviews are analyzed to identify ways in which the core principles influenced the change effort. This analysis provides insight into how the core principles can be leveraged to enact positive change in education. Furthermore, this work provides a method for researchers to characterize other complex change efforts.