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7. One Institution\u27s Journey Toward Redefining and More Holistically Assessing Teaching Effectiveness
No full textFaculty voice, administrator frustration, low student response rates, and stalled reform efforts to enact substantive changes to how the University of Missouri (MU) evaluated teaching fueled the desire on campus to find a better way. In 2019, MU began a journey away from an overemphasis of a single item on student course evaluations to a constituent-informed, evidence-based, multi-measure system to evaluate and document teaching quality. By sparking a campus-wide conversation and initiative, a new model of effective and inclusive teaching was created that led to new instruments, all aligned to a framework that is proving useful in our overall mission to continuously reflect on and improve teaching for improved student learning (MU Teaching for Learning Center, 2024). As with all major campus transformations, change is slow and as some tensions are resolved, new ones emerge. MU’s journey may hearten readers at the outset of their own journey to transform how teaching is evaluated. We hope this chapter is useful to you in describing our process so that it can be applied in your context. Our inclusive efforts were recognized through the MU Shared Governance Award in 2021, celebrating a collaborative participatory approach for one of the most essential facets of the academy: teaching quality for student learning. The MU model is licensed with a Creative Commons license so that others can reuse, remix, and/or revise it to come alive for different campus contexts
Merging Multiple Analyses of SLSTR Vis-SWIR Vicarious Calibration Results
No full textThe Sentinel-3 SLSTR instrument is primarily designed to measure Sea and Land Surface Temperatures for meteorological and climate research applications. As well as spectral bands in the thermal infrared for measurement of surface temperatures, the radiometer is equipped with channels in the visible to short wavelength infrared range, primarily for daytime cloud screening and scene classification. However, the VIS-SWIR channels are used in the retrievals of Fire Radiative Power (FRP) and land applications when used in synergy with the OLCI instrument data. Furthermore, when combined with the dual view capability of the radiometer, the data from the VIS-SWIR channels are used for measurements of aerosol and cloud properties not possible with a single view instrument.
The use of the VIS-SWIR channels in Sentinel-3 level-2 data products, demands that they be radiometrically calibrated to standards traceable to SI. Demonstrating direct traceability to SI is not so straightforward because the L1 processing has many inputs, and several assumptions are made about the instrument model. Analysis of the radiometric model used in the L1 processing shows that there are several key factors that affect the radiometric performances, for instance:
• Calibration of the diffuser-based calibrator
• Long term stability of the calibrator
• Response non-linearity
• Ground-orbit changes – e.g., where there are differences between the pre-launch test conditions and flight operations.
Analysis has been performed by several groups to assess the on-orbit performance of the VIS-SWIR channels using Pseudo Invariant Calibration Sites (PICS). An earlier analysis performed by RAL for the ESA-funded Sentinel-3 Mission Performance Centre (MPC) established calibration corrections for users to apply to the Level-1 data products [1]. Since then, further activities have continued to monitor the calibration performance by RAL using comparisons to measurements of historical sensors, and by ACRI-ST using the DIMITRI toolkit. In addition, EUMETSAT using MICMICS intercalibration and calibration (radiative transfer) modules, and CNES using the SADE database and MUSCLE (associated) tools have provided further independent measurements. Although each group uses the same reference sites and input Level-1 data, differences in the analysis methods, in particular the reference sensor used, can result in small differences in the results. A working group has been set up to compare the results of the different methods, with a view to providing updated coefficients for the absolute radiometric calibration and the long-term stability of the calibration. As the various methodologies use different references, a key challenge is to define and align to a common reference sensor that covers the full spectral range of SLSTR.
References:
1. Sentinel-3 SLSTR VIS and SWIR Channel Vicarious Calibration Adjustments - Sentinel-3 Mission Performance Centr
Creating a Sense of Belonging Through University Archives
No full textCreating a Sense of Belonging
Can University Archives be used to create a sense of belonging on university campuses?
What avenues were/could be available to connect students to campus history and university archives?
How do you assess a sense of belonging and impact of university archives
Hearing from Working Nurses: Incorporating Real-World Knowledge Practices Into the Framework to Better Instruct Tomorrow’s Healthcare Professionals
No full textThere is a certain duality to the role of librarians in higher education as the job requires preparing students for their current academic work, such as doing research for writing papers, while at the same time preparing students for their future professional information work.1 Librarians seeking guidance on teaching information literacy more comprehensively are left to study the professional standards of the targeted professional group,2 and find or carry out research on these practitioners.3 Synthesizing and translating these findings into instructional approaches while staying true to existing information literacy standards, however, can be challenging.4 In this chapter we demonstrate how the ACRL Framework for Information Literacy for Higher Education,5 the guiding document for information literacy curriculum in academia, has enough flexibility to prepare nursing students for both academic and professional information practices
Proceedings of the 5th International Seminar on Dam Protections Against Overtopping
No full textEditors: Sherry Hunt, Bryan Heiner, Tony Wahl, Brian Crooksto
Feasibility and Potential of Electric Air Taxis for Long Distance Airport Access/Egress Trips
No full textGetting to the airport is often one of the most stressful parts of a trip, especially for people who live far from the airport. In the United States, many travelers live more than an hour’s drive from a major airport, and their choices such as driving, using public transit, or paying for ride-hailing can be time-consuming, expensive, and inconvenient. A new technology, Electric Air Taxis (EATs), is being developed as a potential solution. These small, battery-powered aircraft could offer faster, cleaner, and more direct connections to airports. However, the success of EATs depends not only on the technology itself but also on whether people are willing to use them, how they compare with existing travel options, and how policymakers plan for them. This dissertation investigates the feasibility, public acceptance, and demand potential of Electric Air Taxis for long-distance airport trips. Using surveys of over 1,000 air travelers and advanced forecasting methodology, the research explores how travelers perceive EATs, how they make choices between EATs and other travel modes, and who is most likely to adopt this technology as it becomes available. The work is organized around five main objectives: understanding public perceptions, analyzing trade-offs in travel decisions, connecting adoption with individual innovativeness, identifying patterns across early, majority and late adopters, and testing future policy scenarios in a regional case study. The findings show that intention to use EAT is shaped by travelers’ attitudes toward the service, their perceptions of ease of use and usefulness for airport trips, the influence of people around them, as well as by characteristics of EATs such as compatibility with lifestyles, observability of benefits, and opportunities to try the service before committing. Travelers greatly value saving time, often considering faster trips worth paying more for. At the same time, the ease of using EATs and the level of trust people have in the service strongly influence whether they would choose them. Interestingly, the idea of fully autonomous travel was viewed negatively, with the strongest concern reported for EATs. Not everyone will adopt EATs at the same pace. This study first validated the well-known Diffusion of Innovation theory in the context of airport travel and observed clear differences among adoption groups. Early adopters valued EATs primarily for their time savings, relative advantage, and visibility to others. The majority emphasized compatibility with their lifestyle and reliable performance when choosing a mode. Laggards, by contrast, remained highly sensitive to cost and were skeptical about ease of use and the trialability of EATs. Recognizing these differences is essential for designing strategies that encourage widespread adoption. The research also includes a case study of Northern Utah, focusing on trips from Cache County to Salt Lake City International Airport. By combining real-world travel data with survey results, the study forecasts future demand under different scenarios. Results suggest that EATs could capture a meaningful share of airport trips if priced competitively and if vertiports (takeoff and landing sites) are strategically located. However, the analysis also raises equity concerns: EATs may primarily benefit higher-income travelers, while shifts in demand could reduce public transit ridership, affecting those who rely on it most. In summary, this dissertation makes both theoretical and practical contributions. It combines behavioral theories of technology adoption with advanced transportation models to better understand how people might adopt EATs. It also provides policymakers, planners, and industry leaders with evidence-based insights for designing services, setting policies, and addressing challenges. Furthermore, by identifying which groups are most likely to use EAT, what conditions encourage them, and what concerns may hold them back, this research provides practical guidance for planning and integrating EATs into the future of airport travel
Lessons Learned From the Hydraulic Design of the Prado Dam Labyrinth Spillway
No full textThe U.S. Army Corps of Engineers has completed the design of a new labyrinth weir spillway at Prado Dam, in southern California. The proposed labyrinth weir would consist of an arced configuration, 7-cycles, with varying weir wall height from 28 ft to 41 ft with upstream head over the weir cycles of up to 30 ft. This paper highlights the key lessons learned from the hydraulic design of the new labyrinth weir, including significant fluctuating negative pressures at the weir wall resulting in hydrodynamic loads that should be considered during structural design and submergence of the weir at high heads
Faxes, Emails, and CAD: A Case Study of the Changing Landscape in Born-digital Design Records, 1994-2006
No full textThe article examines the technological changeover from analog to born digital design records in the mid-1990s, and is a case study in how one San Francisco Bay Area landscape architecture firm navigated the paradigm shift. The firm retained extensive documentation of the project management/construction administration for large scale projects, and this case study shows how archivists can utilize the analog documentation (like faxes and printed emails) to gain a greater understanding of how design firms began using Computer Aided Drafting and how CAD changed design and the process of archiving
AI Recipe Blog is Evaluated Similarly to a Recipe Blog Created by Nutrition and Dietetic Students
No full textWith the growing use of AI, it is important to know target audiences’ perceptions of its use. A convenience sample of students were invited to take an online survey in which they were randomly assigned to Group 1 (evaluated a student-generated blog; n = 456) or Group 2 (evaluated an AI-generated blog; n = 492). The results of independent t-tests and chi-squared tests indicated no group differences in ratings of ease of recipe preparation, time to prepare the recipe, utilization of common ingredients, and frequency of intended use of the blog. The student-generated blog was rated higher on budget friendliness (p = 0.025). A total of 42% indicated they would be less willing to use a blog if they knew it was AI-generated, while 43% indicated that it would make no difference and 4.4% indicated being more likely to view the AI-generated blog. Two researchers used a thematic analysis approach to evaluate participants’ free responses regarding the likelihood of using a recipe blog that was AI-generated. Participant perceptions of an AI-generated blog ranged from very positive to very negative. Some themes highlighted the potential benefits of AI or a more neutral stance indicating that “a recipe is a recipe”. The majority of themes highlighted the benefits of content that was created, verified, or tested by humans, or espoused a human touch. Students should be trained to cater to consumer preferences, and to add value in a world that includes AI-generated content
Improved Streamflow Forecasting Through SWE-Augmented Spatio-Temporal Graph Neural Networks
No full textStreamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 and Kling–Gupta Efficiency (KGE) of 0.84 in the multivariate setting at the critical downstream node, Lees Ferry. Compared to the univariate setup, SWE-enhanced predictions reduced Root Mean Square Error (RMSE) by 12.8%. Seasonal and spatial analyses showed the greatest improvements at high-elevation and mid-network stations, where snowmelt dynamics dominate runoff. These findings demonstrate that spatio-temporal learning frameworks, especially STGNNs, provide a scalable and physically consistent approach to streamflow forecasting under variable climatic conditions