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  1. Online professional development (PD) can support broader accessibility than traditional face-to- face PD. However online delivery presents challenges for characteristics of high-quality PD, such as collaborative knowledge building and community development, that have proven positive outcomes in face-to- face modes. A few comparative studies have demonstrated equivalent outcomes when PD activities have been translated from a successful face-to-face implementation to an online format. This study investigates whether an online version of PD for high school biology teachers on using computer-supported complex systems curriculum and instruction can achieve the same high impact as the face-to-face version. We describe changes in designmore »decisions to accommodate the online mode and measure impact on teachers’ perceptions of their experiences and student outcomes. The results show positive teacher perceptions in both PD formats and roughly equal student outcomes. However, teachers articulated other benefits to online activities that indicate opportunities for improved access to high-quality PD.« less
    Free, publicly-accessible full text available August 10, 2022
  2. Many researchers have identified the need for a more holistic understanding of the role of feedback in supporting learning in online environments. This study explores how our design, development, and implementation of an online feedback facilitation system influenced high school science teachers’ learning in an asynchronous teacher professional development online course. We then describe teachers’ and facilitators’, i.e., feedback providers’, perceptions of the effectiveness of the system’s features for supporting participants’ learning and engagement. Our work also responds to recent calls for developing a more nuanced understanding of how the complexity of feedback influences learning and the need for moremore »qualitative research on online facilitators’ and learners’ experiences working with new technologies. Results demonstrated that, despite the difficulty of analyzing the complex variables influencing learners’ interactions and perceptions of the feedback system, designing adaptive feedback systems that draw on the principles of design- based implementation research (DBIR) offer promise for enhancing the systems’ contributions to teacher learning.« less
  3. Free, publicly-accessible full text available March 1, 2023
  4. Free, publicly-accessible full text available March 1, 2023
  5. Abstract This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two ν μ -sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale ismore »shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.« less
    Free, publicly-accessible full text available December 1, 2022
  6. A bstract The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affectsmore »the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.« less
    Free, publicly-accessible full text available December 1, 2022
  7. Free, publicly-accessible full text available October 1, 2022
  8. Abstract Accurate knowledge of electron transport properties is vital to understanding the information provided by liquid argon time projection chambers (LArTPCs). Ionization electron drift-lifetime, local electric field distortions caused by positive ion accumulation, and electron diffusion can all significantly impact the measured signal waveforms. This paper presents a measurement of the effective longitudinal electron diffusion coefficient, D L , in MicroBooNE at the nominal electric field strength of 273.9 V/cm. Historically, this measurement has been made in LArTPC prototype detectors. This represents the first measurement in a large-scale (85 tonne active volume) LArTPC operating in a neutrino beam. This ismore »the largest dataset ever used for this measurement. Using a sample of ∼70,000 through-going cosmic ray muon tracks tagged with MicroBooNE's cosmic ray tagger system, we measure D L = 3.74 +0.28 -0.29 cm 2 /s.« less
    Free, publicly-accessible full text available September 1, 2022