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In this scholarly inquiry article, we will reflect on the early trajectory of the NSF-funded MAGIC STEM project (Mobilizing Academic Growth in Inquiry and Collaboration in STEM), designed to foster practitioner-driven scholarship among higher education faculty drawn both from a research university and local community colleges. Launched with a successful academic community kickoff, the project engaged a diverse group of STEM educators who organized into three disciplinary clusters: physical sciences/engineering, life/biological sciences, and mathematical/computer sciences. These clusters provided natural communities of practice in which participants could share challenges, articulate researchable questions, and reflect on their evolving instructional mindsets. Following two online structured cohort meetings during the spring semester, a one week long Summer Institute served as the project’s first intensive opportunity for faculty development and practitioner reflection. Activities included explorations of evidence-based instructional practices (EBIPs), Scholarship of Teaching and Learning (SoTL), and scaffolded opportunities to identify, refine, and commit to discipline-aligned research questions. Participants moved fluidly between exposure to SoTL tools (e.g., systematic reviews, qualitative and quantitative data analysis, ethical considerations) and peer dialogue within their Faculty Learning Communities (FLCs). By the close of the Institute, faculty groups had formulated researchable questions grounded in their teaching practice and had begun to see themselves as contributors to the broader STEM education discourse. Future efforts were planned for the completion of a systematic review and generation of an implementation proposal that emerged from FLC discussions. This chapter will offer a reflective practitioner narrative on how project design, disciplinary clustering, and the Summer Institute created conditions for STEM faculty to shift from adopters of EBIPs to scholarly inquirers into their own teaching. It will highlight the tensions and affordances of this process, drawing from participant reflections and project leadership observations, and will suggest lessons for cultivating a practitioner’s mindset that sustains both individual growth and collective transformation in STEM education. 

IUSE Funded: Motivating STEM Academics for Growth, Innovation and Collaboration Through a Regional Faculty Learning Community There is significant evidence in the literature (Freeman et al., 2014; Shadle et al., 2017; Theobald et al., 2020) of the need to grow awareness among higher education faculty members to utilize research-based practices that support all learners in STEM education. In most institutions, faculty also lack several key resources for success in implementing change (Shadle et al., 2017; Schinske et al., 2017), including a lack of awareness of the research, access to the research publications, training on how to use the research and time to implement new teaching practices. Our two-year, NSF IUSE-funded program aims to mitigate some of the issues raised above. In our region, three community colleges act as STEM feeder schools for a public research university. We have built three Faculty Learning Communities (FLCs) of STEM faculty, primarily from these partners, many of whom have existing mutual professional relationships. The FLCs capture the diverse nature of the disciplines in our STEM umbrella. Separately, the FLCs meet at least three times each semester over the course of an academic year and explore evidence-based instructional practices (EBIPs) literature. An additional goal is to perform a systematic review of the STEM education literature, driven by a research question that has arisen organically from their engagement with transformative works in the field. The ultimate goal is “Developing Reflective Teachers,” following Henderson et al.’s “Best Practices for Creating Change” (2011), while noting that “successful strategies include coordinated and focused efforts lasting over an extended period of time,” and “deliberately focus on changing faculty conceptions.” FLC meetings began in the spring of 2025 and were designed to create community among participants, while clarifying the philosophical basis of the project. The faculty read and discussed seminal works such as Freeman et al., 2014; Theobald et al., 2020; and Transforming Undergraduate STEM Education: Supporting Equitable and Effective Teaching (2025). Additionally, faculty were directed to consider questions that arose about their own teaching practices and what sorts of questions they would like to explore in a systematic review. Faculty were exposed to tools that support educational research, as these often differed from their disciplinary tools. One of the goals of this project is to create scholars of teaching and learning; to this effect, in the summer of 2025, we organized a week-long summer institute focusing on the details of a systematic review as well as workshops on implementing select EBIPs. FLC activities have continued and will go through Spring 2026. Participants' awareness of and comfort with the STEM education literature are measured through pre-/post-surveys and reflection logs that are completed after each FLC meeting. Results we will present include self-assessment of learning gains after the Summer Institute and emergent themes from the reflection logs. 

This Letter extends a recent IceCube sterile neutrino search to include unstable sterile neutrinos within the context of a model termed $3+1+\mathrm{Decay}$ , which expands upon the $3+1$ model by introducing sterile neutrino decay to invisible particles with coupling constant $g^2$ . The model is attractive since it reduces tension between oscillation experiments within the global fits and with constraints that come from cosmological observables. The analysis uses 10.7 years of up-going muon neutrino data with energy 500 GeV to 100 TeV and with improved reconstruction and modeling of systematics. The best-fit point is found to be $g^2=0$ , ${\sin }^2\left(2{\theta }_{24}\right)=0.16$ , and $\mathrm{\Delta }{m}_{41}^2=3.5{\mathrm{eV}}^2$ , in agreement with the recent $3+1$ sterile neutrino search. Values of $g^2\ge \pi$ are excluded at 95% confidence level. This result substantially limits decay parameter space indicated by recent global fits, disfavoring the decay scenario. 

The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas from ten parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the ten morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain, with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally-controlled region of the delta. Islands at the delta front, under the mixed influence of tides, fluvial-estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method does not distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous natural morphology. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems. 

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube’s surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses.