Search for: All records

Teachers may perceive student work in terms of its alignment with instructional norms (normativity) and its potential to advance lesson goals (serviceability), yet how these distinctions surface in professional discourse remains underexplored. This paper uses natural language processing to analyze discourse from 45 professional development meetings involving 77 in-service mathematics teachers who discussed student work artifacts while designing a geometry lesson storyboard. We find that teachers maintained a consistently positive evaluative stance toward all student work artifacts. Semantic patterns suggest that teachers often juxtaposed artifacts, using pairing as a strategy to position diverse approaches while seemingly compensating for perceived limitations in advancing the lesson goal. Talk amount trended higher for less normative artifacts — consistent with prior research — but did not reach statistical significance, likely due to small sizes at the artifact level. 

This study investigates how secondary mathematics teachers from diverse professional and cultural contexts—U.S. vs. non-U.S. in this study—recognize and attend to instructional features within problem-based but teacher-centered lesson representations. A cross-case analysis of asynchronous annotations from the teachers across five countries (Bulgaria, India, the Philippines, South Africa, and the U.S.) revealed that U.S. teachers were significantly more likely to identify the lack of student voice as a notable departure from expected practice. Conversely, many non-U.S. teachers attended to and positively evaluated the teacher’s guidance in the lesson. Overall, these findings suggest that teachers from different pedagogical contexts may bring different expectations and assumptions to their interpretation of instructional materials. 

We explore the hypothesis that mathematics teacher educators have professional obligations that constitute resources and constraints for managing practice-based professional development (PD). We do so by inspecting how twelve facilitators from varied institutional positions, who led a two-year implementation of StoryCircles, reflected on their work of facilitation. We interviewed them with a protocol that made explicit the hypothesis above. Using caricature analysis inspired by The Lord of the Rings, where a fellowship of companions supports one individual in carrying the Ring, we developed facilitator caricatures that capture different orientations that facilitators can take when supporting teachers’ professional development. Findings reveal that while all facilitators recognized disciplinary, interpersonal, individual, and institutional obligations as relevant to their work, what distinguished them was which obligation they treated as the anchor in moments of uncertainty, reflecting the multiple sources of legitimacy for knowledge of mathematics teaching practice that facilitators may recognize when that practice becomes the content of PD. This study contributes to theorizing the professional obligations that shape facilitation of practice-based mathematics PD and how facilitator identity is jointly constituted by systemic affordances and the personal resources individual facilitators bring to their role. 

We examine changes in teachers’ performance selecting student work for discussion in a problem-based lesson after participating in a simulation of reviewing student work. Using measures that describe how participants engaged in the simulation, we show that participants whose behaviour in the learning simulation showed attention to student work which was serviceable for the instructional goal of the lesson were likely to be those who made the largest gains in performance after the learning simulation. 

We report preliminary results of selected questions from a national survey of instructors of geometry courses for secondary teachers about the nature of instructor-student interactions. Survey responses (n= 118) are used to indicate six latent constructs describing aspects of instructor-student interaction that in turn quantify hypothesized characteristics of two didactical contracts, which we call inquiry in geometry and study of geometry. We found that instructors whose highest degree is in mathematics education are less likely to rely on a study of geometry contract than instructors whose highest degree is in mathematics. Also, instructors who have previously taught high school geometry are less likely to lecture. 

We report partial analysis of a survey of instructors of undergraduate geometry courses for teachers, attending to how they described the nature of the mathematical work they engage students in and the opportunities to learn that students had. Analysis of latent construct correlations showed that engagement of students in inquiry into geometry was significantly associated with opportunity to learn about mathematical definitions and conjecturing and engagement of students in the study of geometry was significantly associated with opportunity to learn about axioms and about history of geometry. Latent variable means comparisons showed group differences in claimed opportunity to learn between instructors whose highest degree was in mathematics and those whose highest degree was in mathematics education. 

A comprehensive Bayesian analysis of the 3D dynamics of high-energy nuclear collisions is presented. We perform a systematic model-to-data comparison using simulations of large and small collision systems, and a broad range of measurements from the PHENIX, STAR, PHOBOS, and BRAHMS collaborations spanning nearly two decades of RHIC operations. In particular, we perform fully 3D multi-stage simulations including rapidity-dependent energy deposition with global energy conservation using the 3D Glauber model, along with relativistic viscous hydrodynamics with MUSIC. We calibrate the model on rapidity- and pT-differential observables and analyze the respective constraints on initial state and transport properties they provide. We emphasize the additional constraints provided by rapidity-dependent measurements, the differences in large and small system calibrations, and the tension exhibited by particular observables. We use our calibrated model to make predictions of observables in p-Au and 3He-Au collisions. Furthermore, we facilitate direct comparison of experimental measurements by highlighting the dependence of flow measurements on the rapidity of the regions of interest and reference, as well as the importance of the centrality selection. In particular, we examine the apparent differences between the STAR and PHENIX v2 and v3 measurements in small systems. 

This paper explores the potential of online lesson visualization and annotation tools in fostering international lesson-centered teacher collaboration. In an era where teachers face diverse challenges and limited opportunities for peer-to-peer collaboration, leveraging digital tools for asynchronous exchanges emerges as a promising avenue for professional development. This paper will illustrate the potential of emerging technologies for supporting cross-cultural exchanges in which teachers can share insights, perspectives, and innovative practices in durable and archivable forms, thereby enriching the collective knowledge base for teaching. We share data from an ongoing project focused on engaging groups of secondary mathematics teachers in collectively refining a single storyboarded lesson. Through collaborative lesson development and iterative refinement, we illustrate how these tools transcend temporal and spatial constraints by sharing data gathered from three different groups involved in cross-cultural exchange (one situated in the western part of the U.S, one situated in the eastern part of the U.S., and one situated in Bulgaria) centered on storyboard representation of a lesson. In this way, we provide insights on how the lean graphics of the storyboard and the asynchronous nature of annotation can foster a culture of continuous improvement and mutual support among mathematics teachers spread over large geographic distances. Ultimately, we advocate for the widespread adoption of online multimedia authoring tools as integral components of contemporary approaches to cross-cultural collaboration on lessons for facilitating meaningful exchanges and promoting excellence in teaching and learning on a global scale 

We investigate medium-induced modifications to jet substructure observables that characterize hard components in central Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV. Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $\gamma$ -tagged jets. The results show that quark jets undergo a nonmonotonic modification compared with gluon jets in observables such as the Pb-Pb to $p\text{−}p$ ratio of the soft drop prong angle $r_g$ , the relative prong transverse momentum $k_{T,g}$ , and the groomed mass $m_g$ distributions. Due to this nonmonotonic modification, $\gamma$ -tagged jets, enriched in quark jets, provide surprisingly clear signals of medium-induced structural modifications, distinct from effects dominated by selection bias. Further systematic studies demonstrate that these effects are dominated by recoil medium response. This work highlights the potential of hard substructures in $\gamma$ -tagged jets as powerful tools for probing the jet-medium interactions in high-energy heavy-ion collisions, in particular by enabling detailed investigations of jet-medium parton scatterings via their associated medium response. All simulations for $\gamma$ -tagged jet analyses carried out in this paper used triggered events containing at least one hard photon, which highlights the utility of these observables for future Bayesian analysis. 

Existing building recognition methods, exemplified by BRAILS, utilize supervised learning to extract information from satellite and street-view images for classification and segmentation. However, each task module requires human-annotated data, hindering the scalability and robustness to regional variations and annotation imbalances. In response, we propose a new zero-shot workflow for building attribute extraction that utilizes large-scale vision and language models to mitigate reliance on external annotations. The proposed workflow contains two key components: image-level captioning and segment-level captioning for the building images based on the vocabularies pertinent to structural and civil engineering. These two components generate descriptive captions by computing feature representations of the image and the vocabularies, and facilitating a semantic match between the visual and textual representations. Consequently, our framework offers a promising avenue to enhance AI-driven captioning for building attribute extraction in the structural and civil engineering domains, ultimately reducing reliance on human annotations while bolstering performance and adaptability.