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Wedeveloped an instructional development workshop for science, technology, engineering, and math (STEM) instructors in higher education to promote their adoption of active learning. Our workshop design was based on a proposed framework for motivating adult learners consisting of five elements: (1) expertise of presenters, (2) relevance of content, (3) choice in application, (4) praxis, and (5) group work. We assessed the participating instructors’ attitudes (i.e., motivation to use active learning and intentions and motivation to use strategies to reduce student resistance to active learning) immediately before and after the workshop and again five to six months later. We also assessed participants’ satisfaction with the workshop. Analyses of our data provided evidence of a change in participants’ motivation to use active learning and both their intentions and motivation to use strategies to reduce student resistance to active learning following the workshop. Our quantitative findings and thematic analysis of survey results support the use of the proposed framework for designing instructional development workshops for STEM faculty. The results also show short-term instructional development workshops can be effective and suggest caution in extrapolating immediate post-workshop assessment to the longer-term. 

Students with attention deficit hyperactivity disorder (ADHD) represent a growing fraction of the college population. We plan to study the experiences of college students with ADHD majoring in science, engineering, and mathematics (SEM) and explore how those experiences relate to academic success (i.e., academic achievement, persistence, and creativity). For this work-in-progress paper, we present our project’s conceptual framework and share how specific aspects of it may relate to the academic success of students with ADHD. Our framework is based on Terenzini and Reason’s college impact model, which includes precollege characteristics and experiences, the organizational context, the college experience, and students’ educational outcomes (i.e., academic success). We also describe the quantitative portion of our two-part research study that will analyze longitudinal data from three nationally-administered, multi-institutional surveys. That analysis will guide further qualitative research focused on the college experience and academic success of college students with ADHD. 

Despite many studies confirming that active learning in STEM classrooms improves student outcomes, instructors’ adoption of active learning has been surprisingly slow. This work-in-progress paper describes our broader research study in which we compare the efficacy of a traditional active learning workshop (AL) and an extended version of this workshop that also specifically highlights instructor strategies to reduce resistance (AL+) on instructors’ beliefs about and actual adoption of active learning in undergraduate STEM classrooms. Through a randomized control trial (RCT), we aim to understand the ways in which these workshops influence instructors’ motivation to adopt and the actual use of active learning. This RCT involves instructors and students at a large number of institutions including two-year college, four-year college, and large research institutions in three regions of the country and strategies to reduce student resistance to active learning. We have developed and piloted three instruments, which allow for triangulation of classroom data: an instructor survey, a student survey, and a classroom observation protocol. This work-in-progress paper will cover the current progress of our research study and present our research instruments. 

Despite many studies confirming that active learning in STEM classrooms improves student outcomes, instructors’ adoption of active learning has been surprisingly slow. This work-in-progress paper describes our broader research study in which we compare the efficacy of a traditional active learning workshop (AL) and an extended version of this workshop that also specifically highlights instructor strategies to reduce resistance (AL+) on instructors’ beliefs about and actual adoption of active learning in undergraduate STEM classrooms. Through a randomized control trial (RCT), we aim to understand the ways in which these workshops influence instructors’ motivation to adopt and the actual use of active learning. This RCT involves instructors and students at a large number of institutions including two-year college, four-year college, and large research institutions in three regions of the country and strategies to reduce student resistance to active learning. We have developed and piloted three instruments, which allow for triangulation of classroom data: an instructor survey, a student survey, and a classroom observation protocol. This work-in-progress paper will cover the current progress of our research study and present our research instruments. 

Despite many studies confirming that active learning in STEM classrooms improves student outcomes, instructors;' adoption of active learning has been surprisingly slow. This work-in-progress paper describes our broader research study in which we compare the efficacy of a traditional active learning workshop (AL) and an extended version of this workshop that also specifically highlights instructor strategies to reduce resistance (AL+) on instructors' beliefs about and actual adoption of active learning in undergraduate STEM classrooms. Through a randomized control trial (RCT), we aim to understand the ways in which these workshops influence instructors' motivation to adopt and the actual use of active learning. This RCT involves instructors and students at a large number of institutions including two-year college, four-year college, and large research institutions in three regions of the country and strategies to reduce student resistance to active learning. We have developed and piloted three instruments, which allow for triangulation of classroom data: an instructor survey, a student survey, and a classroom observation protocol. This work-in-progress paper will cover the current progress of our research study and present our research instruments. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours. 

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}^{40}\mathrm{Ar}$and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. Published by the American Physical Society2025 

Abstract This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of thedE/dxmodel on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.