71-80 of 192 results
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Implementing Active Learning Techniques in an Undergraduate Aviation Meteorology Course
PI Daniel Halperin
PI Joseph Keebler
CO-I Robert Eicher
CO-I Thomas Guinn
CO-I Kim Chambers
Student feedback from end-of-course evaluations repeatedly indicated a desire to change the format of the course by de-emphasizing the PowerPoint-based lectures. The goal of the present study was to determine whether including a set of new active-learning techniques in an Aviation Weather course would result in better student understanding (as measured by exam scores) and make the course more engaging (as measured by end-of-course evaluations). During 2018-19, three instructors implemented five different active-learning techniques into their classes (i.e., the experimental group), while two instructors continued to use the unrevised course materials (i.e., the control group). The new active-learning techniques, described below, included daily quizzes, polling questions, flipped classroom sessions, in-class activities, and assertion-evidence-based lectures. All sections used the same assignments and exams, allowing for direct assessment of the effectiveness of the active-learning techniques. Analyses of Variance (ANOVA) tables were used to determine the statistical significance of the differences in exam scores. Indirect assessments in the form of end-of-course evaluations were also examined.
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Adding Tropical Cyclone Verification Capabilities to the Model Evaluation Tools – Tropical Cyclone (MET-TC) Software
PI Daniel Halperin
Producing reliable tropical cyclone (TC) genesis forecasts is an operational priority. The National Hurricane Center uses several TC genesis guidance products for their Tropical Weather Outlook. Furthermore, global model output is used in many TC genesis guidance products and is considered an important source of deterministic TC genesis forecast guidance. This project creates a standard framework for verifying deterministic and probabilistic TC genesis forecasts using the TC-Gen tool in the Model Evaluation Tools software package.
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Demonstration of an Electrostatic Dust Shield on the Lunar Surface
PI Troy Henderson
This project will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface.
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Hazard Detection and Avoidance for Lunar Landing
PI Troy Henderson
This project develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander
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Improved Image Processing for Orbit Estimation
PI Troy Henderson
This project seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes.
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Using Machine Learning to Improve Forecasting of Deep Convection
PI Christopher Hennon
CO-I Ronny Schroeder
CO-I Curtis James
CO-I Abd AlRahman AlMomani
We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).
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Data-enabled Science & Engineering Education (IUSE 1626602, 2016-2020),
PI Matthew Ikle
CO-I Hong Liu
CO-I Michael Wolyniak
CO-I Raphael Isokpehi
This project will develop a virtual department across four partner campuses to provide computer science education to students at campuses that are individually too small to support this kind of department. The new department will focus on the analysis of "big data" - large sets of computational and observational data - that are becoming increasingly prevalent in STEM. Cyber-learning techniques such as recorded lectures, archived materials, blog participation, and active learning approaches will be combined to offer a set of classes in big data science spanning meteorology, environmental science, biology and chemistry. By combining students from different campuses into the same courses, problems with minimal resources and limited potential enrollments on the individual campuses can be overcome. In particular, the project will focus on developing courses in biology and earth science, areas where students are not attracted by traditional computer science classes.
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The Covariant Stone-von Neumann Theorem for Actions of Abelian Groups on C*-algebras of Compact Operators
PI Lara Ismert
This is a collaborative work with Leonard Huang, Ph.D. at the University of Nevada, Reno.
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CONVECT (Convective Organization aNd Venting Experiment in Complex Terrain)
PI Curtis James
CO-I Ronny Schroeder
CONVECT is a major meteorological field research project being proposed for July - August 2025 in north-central Arizona. The project is aimed at improving our understanding and ability to predict the convective development and organization of boundary layer thermals, thunderstorms, and mesoscale convective systems during the North American Monsoon (NAM).
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Peer Review within a Learning Management System (LMS) in a Face-to-Face (F2F) Course
PI Wendi Kappers
The purpose of this research study is to investigate student collaboration and the effectiveness of peer review on the part of the reviewer to increase understanding of information literacy. Focus upon the Learning Management System (LMS) to support automation of peer review activities is a secondary purpose.
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71-80 of 192 results