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The Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE) was designed to measure students' proficiency with measurement uncertainty concepts and practices across ten different assessment objectives to help facilitate the improvement of laboratory instruction focused on this important topic. To ensure the reliability and validity of this assessment, we conducted a comprehensive statistical analysis using classical test theory. This analysis includes an evaluation of the test as a whole, as well as an in-depth examination of individual items and assessment objectives. We make use of a previously reported on scoring scheme involving pairing items with assessment objectives, creating a new unit for statistical analysis referred to as a "couplet." The findings from our analysis provide evidence for the reliability and validity of SPRUCE as an assessment tool for undergraduate physics labs. This increases both instructors' and researchers' confidence in using SPRUCE for measuring students' proficiency with measurement uncertainty concepts and practices to ultimately improve laboratory instruction. Additionally, our results using couplets and assessment objectives demonstrate how these can be used with traditional classic test theory analysis.

Concepts and practices surrounding measurement uncertainty are vital knowledge for physicists and are often emphasized in undergraduate physics laboratory courses. We have previously developed a research-based assessment instrument--the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE)--to examine student proficiency with measurement uncertainty along a variety of axes, including sources of uncertainty, handling of uncertainty, and distributions and repeated measurements. We present here initial results from the assessment representing over 1500 students from 20 institutions. We analyze students' performance pre- and postinstruction in lab courses and examine how instruction impacts students with different majors and gender. We find that students typically excel in certain areas, such as reporting the mean of a distribution as their result, while they struggle in other areas, such as comparing measurements with uncertainty and correctly propagating errors using formulas. Additionally, we find that the importance that an instructor places in certain areas of measurement uncertainty is uncorrelated with student performance in those areas.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] Measurement uncertainty is a critical feature of experimental research in the physical sciences, and the concepts and practices surrounding measurement uncertainty are important components of physics lab courses. However, there has not been a broadly applicable, research-based assessment tool that allows physics instructors to easily measure students' knowledge of measurement uncertainty concepts and practices. To address this need, we employed evidence-centered design to create the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE). SPRUCE is a pre-post assessment instrument intended for use in introductory (first and second year) physics lab courses to help instructors and researchers identify student strengths and challenges with measurement uncertainty. In this paper, we discuss the development of SPRUCE's assessment items guided by evidence-centered design, focusing on how instructors' and researchers' assessment priorities were incorporated into the assessment items and how students' reasoning from pilot testing informed decisions around item answer options. We also present an example of some of the feedback an instructor would receive after implementing SPRUCE in a pre-post fashion, along with a brief discussion of how that feedback could be interpreted and acted upon.

Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5 m telescope at Observatorio de Sierra Nevada, the 2.5 m Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical rebrightening around 10 ^3 –10 ^4 s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multiwavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fallback accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines.

In recent years, much of the emphasis for transformation of introductory STEM courses has focused on "active learning", and while this approach has been shown to produce more equitable outcomes for students, the construct of "active learning" is somewhat ill-defined and is often used as a "catch-all" that can encompass a wide range of pedagogical techniques. Here we present an alternative approach for how to think about the transformation of STEM courses that focuses instead on what students should know and what they can do with that knowledge. This approach, known as three-dimensional learning (3DL), emerged from the National Academy's "A Framework for K-12 Science Education", which describes a vision for science education that centers the role of constructing productive causal accounts for phenomena. Over the past 10 years, we have collected data from introductory biology, chemistry, and physics courses to assess the impact of such a transformation on higher education courses. Here we report on an analysis of video data of class sessions that allows us to characterize these sessions as active, 3D, neither, or both 3D and active. We find that 3D classes are likely to also involve student engagement (i.e. be active), but the reverse is not necessarily true. That is, focusing on transformations involving 3DL also tends to increase student engagement, whereas focusing solely on student engagement might result in courses where students are engaged in activities that do not involve meaningful engagement with core ideas of the discipline.

The Burst Observer and Optical Transient Exploring System (BOOTES) was first designed as an asset of autonomous telescopes that started to be deployed in 1998, taking 24 years to be fully developed around the earth. Nowadays BOOTES has became a global network of robotic telescopes, being the first one present in all continents, as of 2022. Here we present the details of the network and review its achievements over the last 2 decades regarding follow-up observations of high-energy transient events. Moreover, considering the recent operations of neutrino and gravitational wave detectors, some hot-topic expectations related to robotic astronomy are discussed within the framework of multi-wavelength astrophysics.

Abstract Objective Lymphocytes express tyrosine hydroxylase (TH), the rate-limiting enzyme for the synthesis of dopamine, norepinephrine and epinephrine. This suggests a broader role for cathecholamines in lymphocyte function, as well as the potential secretion of catecholamines by tumors of lymphoid origin. Our aim was to evaluate the expression of Th by murine lymphoma cells in an in vivo mouse model. For this, L5178Y-R lymphoma cells were implanted in nerve-intact and sympathectomized male BALB/c mice. Relative Th gene expression in tumor and brain was determined by quantitative PCR. Body composition, tumor volume, and plasma TH1/TH2/TH17 cytokines were also evaluated as markers of tumor-host condition and anti-tumor immune response in absence of adrenergic innervation. Results We found a significant (p = 0.045) 3.3-fold decrease of Th gene expression in tumor and a non-significant (p = 0.60) 6.9-fold increase in brain after sympathectomy. Sympathectomized mice also showed a significant increase in tumor mass at days 18 (p = 0.032) and 28 (p = 0.022) and increased interscapular fat (p = 0.04). TH1/TH2 and TH17 cytokines levels in plasma from sympathectomized tumor-bearing mice were not different from control mice. Conclusion The L5178Y-R lymphoma does not express Th during in vivo progression.

Drinking water has and will continue to be at the foundation of our nation’s well-being and there is a growing interest in United States (US) drinking water quality. Nearly 30% of the United States population obtained their water from community water systems that did not meet federal regulations in 2019. Given the heavy interactions between society and drinking water quality, this study integrates social constructionism, environmental injustice, and sociohydrological systems to evaluate local awareness of drinking water quality issues. By employing text analytics, we explore potential drivers of regional water quality narratives within 25 local news sources across the United States. Specifically, we assess the relationship between printed local newspapers and water quality violations in communities as well as the influence of social, political, and economic factors on the coverage of drinking water quality issues. Results suggest that the volume and/or frequency of local drinking water violations is not directly reflected in local news coverage. Additionally, news coverage varied across sociodemographic features, with a negative relationship between Hispanic populations and news coverage of Lead and Copper Rule, and a positive relationship among non-Hispanic white populations. These findings extend current understanding of variations in local narratives to consider nuances of water quality issues and indicate opportunities for increasing equity in environmental risk communication.

Rubric-based admissions are claimed to help make the graduate admissions process more equitable, possibly helping to address the historical and ongoing inequities in the U.S. physics graduate school admissions process that have often excluded applicants from minoritized races, ethnicities, genders, and backgrounds. Yet, no studies have examined whether rubric-based admissions methods represent a fundamental change in the admissions process or simply represent a new tool that achieves the same outcome. To address that, we developed supervised machine learning models of graduate admissions data collected from our department over a seven-year period. During the first four years, our department used a traditional admission process and switched to a rubric-based process for the following three years, allowing us to compare which parts of the applications were driving admissions decisions. We find that faculty focused on applicants’ physics GRE scores and grade-point averages when making admissions decisions before the implementation of the rubric. While we were able to develop a sufficiently good model whose results we could trust for the data before the implementation of the rubric, we were unable to do so for the data collected after the implementation of the rubric, despite multiple modifications to the algorithms and data such as implementing Tomek Links. Our inability to model the second dataset despite being able to model the first combined with model comparison analyses suggests that rubric-based admission does change the underlying process. These results suggest that rubric-based holistic review is a method that could make the graduate admission process in physics more equitable.

We present a detailed prompt emission and early optical afterglow analysis of the two very-high-energy (VHE) detected bursts GRB 201015A and GRB 201216C, and their comparison with a subset of similar bursts. Time-resolved spectral analysis of multistructured GRB 201216C using the Bayesian binning algorithm revealed that during the entire duration of the burst, the low-energy spectral index ( α _pt ) remained below the limit of the synchrotron line of death. However, statistically some of the bins supported the additional thermal component. Additionally, the evolution of spectral parameters showed that both the peak energy ( E _p ) and α _pt tracked the flux. These results were further strengthened using the values of the physical parameters obtained by synchrotron modeling of the data. Our earliest optical observations of both bursts using the F/Photometric Robotic Atmospheric Monitor Observatorio del Roque de los Muchachos and Burst Observer and Optical Transient Exploring System robotic telescopes displayed a smooth bump in their early optical light curves, consistent with the onset of the afterglow due to synchrotron emission from an external forward shock. Using the observed optical peak, we constrained the initial bulk Lorentz factors of GRB 201015A and GRB 201216C to Γ _0 = 204 and Γ _0 = 310, respectively. The present early optical observations are the earliest known observations constraining outflow parameters and our analysis indicate that VHE detected bursts could have a diverse range of observed luminosity within the detectable redshift range of present VHE facilities.

As systematic inequities in higher education and society have been brought to the forefront, graduate programs are interested in increasing the diversity of their applicants and enrollees. Yet, structures in place to evaluate applicants may not support such aims. One potential solution to support those aims is rubric-based holistic review. Starting in 2018, our physics department implemented a rubric-based holistic review process for all applicants to our graduate program. The rubric assessed applicants on 18 metrics covering their grades, test scores, research experiences, noncognitive competencies, and fit with the program. We then compared faculty’s ratings of applicants by admission status, sex, and undergraduate program over a three-year period. We find that the rubric scores show statistically significant differences between admitted and nonadmitted students as hoped. We also find that differences in rubric scores based on sex or undergraduate program reflected known systematic inequities such as applicants from smaller and less prestigious undergraduate universities scoring lower on the physics GRE and women performing more volunteer work in academia. Our results then suggest rubric-based holistic review as a possible route to making graduate admissions in physics more equitable.

Liver is a unique organ in displaying a reparative and regenerative response after acute/chronic damage or partial hepatectomy, when all the cell types must proliferate to re-establish the liver mass. The NADPH oxidase NOX4 mediates Transforming Growth Factor-beta (TGF-β) actions, including apoptosis in hepatocytes and activation of stellate cells to myofibroblasts. Aim of this work was to analyze the impact of NOX4 in liver regeneration by using two mouse models where Nox4 was deleted: 1) general deletion of Nox4 (NOX4−/−) and 2) hepatocyte-specific deletion of Nox4 (NOX4hepKO). Liver regeneration was analyzed after 2/3 partial hepatectomy (PH). Results indicated an earlier recovery of the liver-to-body weight ratio in both NOX4−/− and NOX4hepKO mice and an increased survival, when compared to corresponding WT mice. The regenerative hepatocellular fat accumulation and the parenchyma organization recovered faster in NOX4 deleted livers. Hepatocyte proliferation, analyzed by Ki67 and phospho-Histone3 immunohistochemistry, was accelerated and increased in NOX4 deleted mice, coincident with an earlier and increased Myc expression. Primary hepatocytes isolated from NOX4 deleted mice showed higher proliferative capacity and increased expression of Myc and different cyclins in response to serum. Transcriptomic analysis through RNA-seq revealed significant changes after PH in NOX4−/− mice and support a relevant role for Myc in a node of regulation of proliferation-related genes. Interestingly, RNA-seq also revealed changes in the expression of genes related to activation of the TGF-β pathway. In fact, levels of active TGF-β1, phosphorylation of Smads and levels of its target p21 were lower at 24 h in NOX4 deleted mice. Nox4 did not appear to be essential for the termination of liver regeneration in vivo, neither for the in vitro hepatocyte response to TGF-β1 in terms of growth inhibition, which suggest its potential as therapeutic target to improve liver regeneration, without adverse effects.

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.

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.

Surveys have long been used in physics education research to understand student reasoning and inform course improvements. However, to make analysis of large sets of responses practical, most surveys use a closed-response format with a small set of potential responses. Open-ended formats, such as written free response, can provide deeper insights into student thinking, but take much longer to analyze, especially with a large number of responses. Here, we explore natural language processing as a computational solution to this problem. We create a machine learning model that can take student responses from the Physics Measurement Questionnaire as input, and output a categorization of student reasoning based on different reasoning paradigms. Our model yields classifications with the same level of agreement as that between two humans categorizing the data, but can be done by a computer, and thus can be scaled for large datasets. In this work, we describe the algorithms and methodologies used to create, train, and test our natural language processing system. We also present the results of the analysis and discuss the utility of these approaches for analyzing open-response data in education research.

In this work, blood flow dynamics was analyzed in a realistic thoracic aorta (TA) model under unsteady-state conditions via velocity contours, secondary flow, pressure and wall shear stress (WSS) distributions. Our results demonstrated that the primary flow velocity is skewed towards the inner wall of the ascending aorta; but this skewness shifts towards the posterior wall in the aortic arch and then towards the anterior-outer wall in the descending aorta. Within the three arch branches, the flow velocity is skewed to the distal walls with flow reversal along the proximal walls. Strong secondary flow motion is observed in the TA, especially at the inlet of the arch branches. WSS is highly dynamic, but was found to be the lowest along the proximal walls of the arch branches. Finally, pressure was found to be low along the inner aortic wall and in the proximal walls of the arch branches, and high around the three stagnation regions distal to the arch branches and along the outer wall of the ascending aorta.

The importance of improving STEM education is of perennial interest, and to this end, the education community needs ways to characterize transformation efforts. Three-dimensional learning (3DL) is one such approach to transformation, in which core ideas of the discipline, scientific practices, and crosscutting concepts are combined to support student development of disciplinary expertise. We have previously reported on an approach to the characterization of assessments, the Three-Dimensional Learning Assessment Protocol (3D-LAP), that can be used to identify whether assessments have the potential to engage students in 3DL. Here we present the development of a companion, the Three-Dimensional Learning Observation Protocol (3D-LOP), an observation protocol that can reliably distinguish between instruction that has potential for engagement with 3DL and instruction that does not. The 3D-LOP goes beyond other observation protocols, because it is intended not only to characterize the pedagogical approaches being used in the instructional environment, but also to identify whether students are being asked to engage with scientific practices, core ideas, and crosscutting concepts. We demonstrate herein that the 3D-LOP can be used reliably to code for the presence of 3DL; further, we present data that show the utility of the 3D-LOP in differentiating between instruction that has the potential to promote 3DL from instruction that does not. Our team plans to continue using this protocol to evaluate outcomes of instructional transformation projects. We also propose that the 3D-LOP can be used to support practitioners in developing curricular materials and selecting instructional strategies to promote engagement in three-dimensional instruction.

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large datasets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming courses by including interactive engagement, demonstrated that students often move away from scientistlike views due to science education, and has injected robust assessment into the physics classroom via concept inventories. The work presented here examines the impact that machine learning may have on physics education research, presents a framework for the entire process including data management, model evaluation, and results communication, and demonstrates the utility of this framework through the analysis of two types of survey data.

One argument for keeping the physics Graduate Record Exam (GRE) is that it can help applicants who might otherwise be missed in the admissions process stand out. In this work, we evaluate whether this claim is supported by physics graduate school admissions decisions. We used admissions data from five Ph.D.-granting physics departments over a 2-year period (N=2537) to see how the fraction of applicants admitted varied based on their physics GRE scores. We compared applicants with low GPAs to applicants with higher GPAs, applicants from large undergraduate universities to applicants from smaller undergraduate universities, and applicants from selective undergraduate institutions to applicants from less selective undergraduate institutions. We also performed a mediation and moderation analysis to provide statistical rigor and to better understand the previous relationships. We find that for applicants who might otherwise have been missed (e.g., have a low GPA or attended a small or less selective school), having a high physics GRE score did not seem to increase the applicant’s chances of being admitted to the schools. However, having a low physics GRE score seemed to penalize otherwise competitive applicants (i.e., applicants with mid to high GPAs). Thus, our work suggests that the physics GRE does not, in fact, help applicants who might otherwise be missed stand out.

Introductory physics lab courses serve as the starting point for students to learn and experience experimental physics at the undergraduate level. They often focus on measurement uncertainty, an essential topic for practicing physicists and a foundation for more advanced lab learning. As such, measurement uncertainty has been a focus when studying and improving introductory physics lab courses. There is a need for a research-based assessment explicitly focused on measurement uncertainty that captures the breadth of learning related to the topic, and that has been developed and documented in an evidence-centered way. In this work, we present the first step in the development of such an assessment, with the goal of establishing the breadth and depth of the domain of measurement uncertainty in introductory physics labs. We conducted and analyzed interviews with introductory physics lab instructors across the US, identifying prevalent concepts and practices related to measurement uncertainty, and their level of emphasis in introductory physics labs. We find that instructors discuss a range of measurement uncertainty topics beyond basic statistical ideas like mean and standard deviation, including those connected to modeling, another lab learning goal. We describe how these findings will be used in the subsequent development of the assessment, called the Survey Of Physics Reasoning On Uncertainty Concepts In Experiments (SPRUCE).

Despite limiting access to applicants from underrepresented racial and ethnic groups, the practice of using hard or soft Graduate Record Examination (GRE) cutoff scores in physics graduate program admissions is still a popular method for reducing the pool of applicants. The present study considers whether the undergraduate institutions of applicants have any influence on the admissions process by modeling a physics GRE cutoff score with application data from admissions offices of five universities. Two distinct approaches based on inferential and predictive modeling are conducted. While there is some disagreement regarding the relative importance between features, the two approaches largely agree that including institutional information significantly aids the analysis. Both models identify cases where the institutional effects are comparable to factors of known importance such as gender and undergraduate GPA. As the results are stable across many cutoff scores, we advocate against the practice of employing physics GRE cutoff scores in admissions.

[This paper is part of the Focused Collection on Curriculum Development: Theory into Design.] The communities of practice framework has become an essential framework for understanding identity development both in physics education research (PER) and in science, technology, engineering, and mathematics, more broadly. However, the use of communities of practice as a learning theory that informs curriculum design is significantly less prevalent within the PER community. One possible reason for this is that communities of practice as a theory originated in professional environments and it subsequently moved towards a framework that is centered around informing management practices. Some significant interpretations and negotiations need to be completed in order to apply and to design for communities of practice in the classroom environment. In this paper, we outline an introductory physics course called Projects and Practices in Physics (P-Cubed) that was designed using the communities of practice as a guiding framework. We present the curriculum decisions that focus specifically on the adaption process from professional practice to the classroom context along with the theoretical underpinnings of the curriculum design decisions that went into the development of the P-Cubed classroom.

We have used an unsupervised machine learning method called latent Dirichlet allocation (LDA) to thematically analyze all papers published in the Physics Education Research Conference Proceedings between 2001 and 2018. By looking at co-occurrences of words across the data corpus, this technique has allowed us to identify ten distinct themes or “topics” that have seen varying levels of prevalence in physics education research (PER) over time and to rate the distribution of these topics within each paper. Our analysis suggests that although all identified topics have seen sustained interest over time, PER has also seen several waves of increased interest in certain topics, beginning with initial interest in qualitative, theory-building studies of student understanding, which gave way to a focus on problem solving in the late 2000s. Since 2010 the field has seen a shift toward more sociocultural views of teaching and learning with a particular focus on communities of practice, student identities, and institutional change. Based on these results, we suggest that unsupervised text analysis techniques like LDA may hold promise for providing quantitative, independent, and replicable analyses of educational research literature.

An increasing number of introductory physics courses are seeking to incorporate “authentic practices,” and a large area of focus in this trend is the incorporation of computational problems into the curriculum. These problems offer students an opportunity to engage with the programming practices and numerical problem-solving methods used by physicists. Understanding how instructors approach teaching such problems is essential for improving instruction and problem design. We conducted a phenomenographic study using semistructured interviews with undergraduate learning assistants in a problem-based introductory mechanics course that incorporates several computational problems. The learning assistants’ prior involvement as students, along with their relatively fewer experiences with programming and physics compared to the faculty instructors, give them a unique perspective on teaching in the course. We present here the results of our analysis: the identification of four approaches that learning assistants make take to teaching computational problems in this course. These approaches, programming focus, learning physics via computation focus, computation as a tool focus, and shifting perceptions of learning focus, provide a lens for understanding the different ways learning assistants perceive computation, the degree to which they take up course-intended learning goals surrounding computation, the factors that may impact the approaches they take, and how we might affect their approaches through training and support.