Supporting Teachers to Customize Curriculum for Self-Directed Learning

Guiding teachers to customize curriculum has shown to improve science instruction when guided effectively. We explore how teachers use student data to customize a web-based science unit on plate tectonics. We study the implications for teacher learning along with the impact on student self-directed learning. During a professional development workshop, four 7th grade teachers reviewed logs of their students’ explanations and revisions. They used a curriculum visualization tool that revealed the pedagogy behind the unit to plan their customizations. To promote self-directed learning, the teachers decided to customize the guidance for explanation revision by giving students a choice among guidance options. They took advantage of the web-based unit to randomly assign students (N = 479) to either a guidance Choice or a no-choice condition. We analyzed logged student explanation revisions on embedded and pre-test/post-test assessments and teacher and student written reflections and interviews. Students in the guidance Choice condition reported that the guidance was more useful than those in the no-choice condition and made more progress on their revisions. Teachers valued the opportunity to review student work, use the visualization tool to align their customization with the knowledge integration pedagogy, and investigate the choice option empirically. These findings suggest that the teachers’ decision to offer choice among guidance options promoted aspects of self-directed learning

Comparing Expert and ChatGPT-authored Guidance Prompts

Students bring a multitude of ideas and experiences to the classroom while they are reasoning about scientific phenomena. They often need timely guidance to refine build upon their initial ideas. In this study we explore the development of guidance prompts to provide students with personalized, real-time feedback in the context of a pedagogically grounded chatbot. In the current version of the tool, guidance prompts are authored by learning scientists who are experts in the content of the items and in Knowledge Integration pedagogy. When students engage with the chatbot, an idea detection model is used to determine the ideas that are present in a student explanation and then the expert-authored guidance prompts are assigned based on rules about which ideas are or are not present in the student explanation. While this approach allows for close attention to and control of the pedagogical intent of each prompt, it is time consuming and not easily generalizable. Further this rule-based approach limits the ways in which students can interact with the chatbot. The work in progress study presented in this paper explores the potential of using generative AI to create similarly pedagogically grounded guidance prompts as a first step towards increasing the generalizability and scalability of this approach. Specifically, we ask: using criteria from the Knowledge Integration Pedagogical Framework, how do ChatGPT 3.5-authored guidance prompts compare to human expert-authored guidance prompts? We find that while prompt engineering can enhance the alignment of ChatGPT-authored guidance prompts with pedagogical criteria, the human expert-authored guidance prompts more consistently meet the pedagogical criteria

Teacher-informed Expansion of an Idea Detection Model for a Knowledge Integration Assessment

Students come to science classrooms with ideas informed by their prior instruction and everyday observations. Following constructivist pedagogy, assessments that encourage students to elaborate their ideas, distinguish among them, and link the most promising ones can capture students' potential and help teachers plan their lessons. In this investigation, we study an assessment that engages students in a dialog to refine their response to a Knowledge Integration (KI) question. Our Research Practice Partnership (RPP) initially trained a Natural Language Processing (NLP) idea detection model on 1218 student responses from 5 schools and identified 13 student ideas. The original model had an overall micro-averaged F-score of 0.7634. After classroom testing, three RPP expert teachers with 10+ years of experience reviewed the classroom data and expanded the model, adding six additional ideas including two that they described as precursor ideas because they foreshadowed more sophisticated reasoning. We trained the idea detection model on these 19 ideas using a dataset from 13 teachers and 1206 students across 8 public schools. The updated model had a somewhat lower overall micro-averaged F-score of 0.7297. The two precursor ideas were among the top four detected ideas. The assessment, using the updated model, guided students to express significantly more ideas. A regression model showed that the updated model was associated with greater KI score gains. Expanding the model, thus, created an assessment that motivated students to express more ideas and to achieve higher KI scores. It also provides teachers with deeper insights into their students' understanding of science

A framework for convection and boundary layer parameterization derived from conditional filtering

A new theoretical framework is derived for parameterization of subgrid physical processes in atmospheric models; the application to parameterization of convection and boundary layer fluxes is a particular focus. The derivation is based on conditional filtering, which uses a set of quasi-Lagrangian labels to pick out different regions of the fluid, such as convective updrafts and environment, before applying a spatial filter. This results in a set of coupled prognostic equations for the different fluid components, including subfilter-scale flux terms and entrainment/detrainment terms. The framework can accommodate different types of approaches to parameterization, such as local turbulence approaches and mass-flux approaches. It provides a natural way to distinguish between local and nonlocal transport processes, and makes a clearer conceptual link to schemes based on coherent structures such as convective plumes or thermals than the straightforward application of a filter without the quasi-Lagrangian labels. The framework should facilitate the unification of different approaches to parameterization by highlighting the different approximations made, and by helping to ensure that budgets of energy, entropy, and momentum are handled consistently and without double counting. The framework also points to various ways in which traditional parameterizations might be extended, for example by including additional prognostic variables. One possibility is to allow the large-scale dynamics of all the fluid components to be handled by the dynamical core. This has the potential to improve several aspects of convection-dynamics coupling, such as dynamical memory, the location of compensating subsidence, and the propagation of convection to neighboring grid columns

Non-invasive imaging of atherosclerotic plaque macrophage in a rabbit model with F-18 FDG PET: a histopathological correlation

BACKGROUND: Coronary atherosclerosis and its thrombotic complications are the major cause of mortality and morbidity throughout the industrialized world. Thrombosis on disrupted atherosclerotic plaques plays a key role in the onset of acute coronary syndromes. Macrophages density is one of the most critical compositions of plaque in both plaque vulnerability and thrombogenicity upon rupture. It has been shown that macrophages have a high uptake of (18)F-FDG (FDG). We studied the correlation of FDG uptake with histopathological macrophage accumulation in atherosclerotic plaques in a rabbit model. METHODS: Atherosclerosis was induced in rabbits (n = 6) by a combination of atherogenic diet and balloon denudation of the aorta. PET imaging was performed at baseline and 2 months after atherogenic diet and coregistered with magnetic resonance (MR) imaging. Normal (n = 3) rabbits served as controls. FDG uptake by the thoracic aorta was expressed as concentration (μCi/ml) and the ratio of aortic uptake-to-blood radioactivity. FDG uptake and RAM-11 antibody positive areas were analyzed in descending aorta. RESULTS: Atherosclerotic aortas showed significantly higher uptake of FDG than normal aortas. The correlation of aortic FDG uptake with macrophage areas assessed by histopathology was statistically significant although it was not high (r = 0.48, p < 0.0001). When uptake was expressed as the ratio of aortic uptake-to-blood activity, it correlated better (r = 0.80, p < 0.0001) with the macrophage areas, due to the correction for residual blood FDG activity. CONCLUSION: PET FDG activity correlated with macrophage content within aortic atherosclerosis. This imaging approach might serve as a useful non-invasive imaging technique and potentially permit monitoring of relative changes in inflammation within the atherosclerotic lesion

MRI Discriminates Thrombus Composition and ST Resolution after Percutaneous Coronary Intervention in Patients with ST-Elevation Myocardial Infarction

Histological composition of material obtained by thrombus aspiration during percutaneous coronary intervention (PCI) in patients with ST-segment elevation acute myocardial infarction (STEMI) is highly variable. We aimed to characterize this material using magnetic resonance imaging (MRI) and to correlate MRI findings with the success of PCI in terms of ST-segment resolution. Thrombus aspiration during primary or rescue PCI was attempted in 100 consecutive STEMI patients, of whom enough material for MRI was obtained in 59. MR images were obtained at 9.4T and T1 and T2 values were measured. Patients with (n = 31) and without (n = 28) adequate ST resolution 120 min after PCI (≥70% of pre-PCI value) had similar baseline characteristics except for a higher prevalence of diabetes mellitus in the latter (10 vs. 43%, p = 0.003). T1 values were similar in both groups (1248±112 vs. 1307±85 ms, respectively, p = 0.7). T2 values averaged 31.2±10.3 and 36.6±12.2 ms; in thrombus from patients with and without adequate ST resolution (p = 0.09). After adjusting for diabetes and other baseline characteristics, lower T2 values were significantly associated with inadequate ST resolution (odds ratio for 1 ms increase 1.08, CI 95% 1.01–1.16, p = 0.027). Histology classified thrombus in 3 groups: coagulated blood (n = 38), fibrin rich (n = 9) and lipid-rich (n = 3). Thrombi composed mostly of coagulated blood were characterized as being of short (n = 10), intermediate (n = 15) or long evolution (n = 13), T2 values being 34.0±13.2, 31.9±8.3 and 31.5±7.9 ms respectively (p = NS). In this subgroup, T2 was significantly higher in specimens from patients with inadequate perfusion (35.9±10.3 versus 28.6±6.7 ms, p = 0.02). This can be of clinical interest as it provides information on the probability of adequate ST resolution, a surrogate for effective myocardial reperfusion