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Online modes of teaching and learning have gained increased attention following the COVID-19 pandemic, resulting in education delivery trends likely to continue for the foreseeable future. It is therefore critical to understand the implications for student learning outcomes and their interest in or affinity towards the subject, particularly in water science classes, where educators have traditionally employed hands-on outdoor activities that are difficult to replicate online. In this study, we share our experiences adapting a field-based laboratory activity on groundwater to accommodate more than 700 students in our largest-enrollment general education course during the pandemic. As part of our adaptation strategy, we offered two versions of the same exercise, one in-person at the Mirror Lake Water Science Learning Laboratory, located on Ohio State University’s main campus, and one online. Although outdoor lab facilities have been used by universities since at least the 1970s, this research is novel in that 1) it considers not only student achievement but also affinity for the subject, 2) it is the first of its kind on The Ohio State University’s main campus, and 3) it was conducted during the COVID-19 pandemic, at a time when most university classes were unable to take traditional field trips. We used laboratory grades and a survey to assess differences in student learning and affinity outcomes for in-person and online exercises. Students who completed the in-person exercise earned better scores than their online peers. For example, in Fall 2021, the median lab score for the in-person group was 97.8%, compared to 91.7% for the online group. The in-person group also reported a significant ( p < 0.05) increase in how much they enjoyed learning about water, while online students reported a significant decrease. Online students also reported a significant decrease in how likely they would be to take another class in water or earth sciences. It is unclear whether the in-person exercise had better learning and affinity outcomes because of the hands-on, outdoor qualities of the lab or because the format allowed greater interaction among peers and teaching instructors (TAs). To mitigate disparities in student learning outcomes between the online and in-person course delivery, instructors will implement future changes to the online version of the lab to enhance interactions among students and TAs. 

Research in the area of internet-of-things, cyber physical- systems, and smart health often employ sensor systems at residences for continuous monitoring. Such research oriented residential monitoring systems (RRMSs) usually face two major challenges, long-term reliable operation management and validation of system functionality with minimal human effort. Targeting these two challenges, this paper describes a monitor of monitoring systems with ground-truth validation capabilities, M2G. It consists of two subsystems, the Monitor2 system and the Ground-truth validation system. The Monitor2 system encapsulates a flexible set of general-purpose components to monitor the operation and connectivity of heterogeneous sensor devices (e.g. smart watches, smart phones, microphones, beacons, etc.), a local base-station, as well as a cloud server. It provides a user-friendly interface and supports different types of RRMSs in various contexts. The system also features a ground truth validation system to support obtaining ground truth in the field. Additionally, customized alerts can be sent to remote administrators and other personnel to report any dysfunction or inaccuracy of the system in real time. M2G is applied to three very different case studies: the M2FED system which monitors family eating dynamics, an in-home wireless sensing system for monitoring nighttime agitation, and the BESI system which monitors behavioral and environmental parameters to predict health events and to provide interventions. The results indicate that M2G is a comprehensive system that (i) requires small cost in time and effort to adapt to an existing RRMS, (ii) provides reliable data collection and reduction in data loss by detecting faults in real-time, and (iii) provides a convenient and timely ground truth validation facility. 

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.