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Description
Many high school students demonstrate an overall lack of interest in science. Traditional teaching methodologies seem to be unsuccessful at engaging students \u2014 one explanation is that students often interpret what they learn in school as irrelevant to their personal lives. Active learning and case based learning methodologies seem to be more effective at promoting interest and understanding of scientific principles. The purpose of our research was to implement a lab with updated teaching methodologies that included an active learning and case based curriculum. The lab was implemented in two high school honors biology classes with the specific goals of: significantly increasing students' interest in science and its related fields; increasing students' self-efficacy in their ability to understand and interpret the traditional process of the scientific method; and increasing this traditional process of objectively understanding the scientific method. Our results indicated that interest in science and its related fields (p = .011), students' self-efficacy in understanding the scientific method (p = .000), and students' objective understanding of the scientific method (p = .000) statistically significantly increased after the lab was administered; however, our results may not be as meaningful as the p-values imply due to the scale of our assessment.
ContributorsCotten, Kathryn (Author) / Hoffner, Kristin (Thesis director) / Stout, Valerie (Committee member) / Lynskey, Jim (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2012-12
Description
This thesis addresses the impact of algorithmic programming on judicial decision-making and the court-appointed attorney (CAA) process, focusing on the intersection of technology and judicial discretion at the Tempe Municipal Court. The primary inquiry of this thesis explores how algorithmic and digital programming, creating an automated system, can enhance equitable access to legal representation for indigent criminal defendants by making the CAA process more uniform. This project implements back-end algorithmic calculations to provide judges with system recommendations by using the Qualtrics survey software to create a digital version of the paper-based Form 5C. The "System Recommendation Tool" streamlines the process by presenting concise encapsulations of defendants' Form 5C responses and algorithmically derived recommendations regarding CAA qualification and contribution amounts.
Significant disparities between the digital system's recommendations and judicial outcomes emerge through analyzing 80 Form 5Cs and their corresponding judicial decisions. These disparities underscore the need for further refinement of the digital system and the possibility of increased use of judicial discretion and consideration of additional factors beyond the Form 5C. While recognizing the system's potential benefits, this research emphasizes the importance of continuous testing and refinement and ongoing consultation with judges. Ultimately, the digital system is a complementary tool to judicial decision-making rather than a replacement.
ContributorsSharma, Poorva (Author) / Broberg, Gregory (Thesis director) / Kane, Kevin (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of Social Transformation (Contributor)
Created2024-05
Description
This paper furthers the examination of the complex relationship between discrimination, identity, and voting habits. This analysis uses data from the Arizona Youth Identity Project conducted in October and September of 2020 to uncover the salient correlations amongst different identities with experiences of discrimination, American Identity, and presidential candidate preference in the 2020 election among the youth voting population in Arizona. The research shows that for this demographic of voters, identities including race, gender, social class, and age are crucial when uncovering patterns of levels of discrimination, American Identity, and candidate preference The study also went further to highlight relationships among intersections of both race and gender with the same measured outcomes.
ContributorsErnaut, Isabella (Author) / Martin, Nathan (Thesis director) / Neuner, Fabian (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of Social Transformation (Contributor)
Created2024-05
Description
In 2022, a previous team of computer science and accounting students worked together to design and build a fully-functioning website to automate accounting transactions. They created dynamic accounting applications using software frameworks such as React and Express. They then used the services provided by Amazon Web Services to make the website available online. The stakeholders of the project wanted to expand upon the services provided by the website so they entrusted our team with implementing new features and applications to the software system. Using the same software frameworks and services of the previous team, we redesigned the website and increased its functionality to better meet the needs of accounting automation.
ContributorsJain, Sejal (Author) / Macabou, Elise (Co-author) / Lim, Jonathan (Co-author) / Villani, Jacob (Co-author) / Chen, Yinong (Thesis director) / Hunt, Neil (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor) / School of Public Affairs (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
This thesis project focused on determining the primary causes of flight delays within the United States then building a machine learning model using the collected flight data to determine a more efficient flight route from Phoenix Sky Harbor International Airport in Phoenix, Arizona to Harry Reid International Airport in Las Vegas, Nevada. In collaboration with Honeywell Aerospace as part of the Ira A. Fulton Schools of Engineering Capstone Course, CSE 485 and 486, this project consisted of using open source data from FlightAware and the United States Bureau of Transportation Statistics to identify 5 primary causes of flight delays and determine if any of them could be solved using machine learning.
The machine learning model was a 3-layer Feedforward Neural Network that focused on reducing the impact of Late Arriving Aircraft for the Phoenix to Las Vegas route. Evaluation metrics used to determine the efficiency and success of the model include Mean Squared Error (MSE), Mean Average Error (MAE), and R-Squared Score.
The benefits of this project are wide-ranging, for both consumers and corporations. Consumers will be able to arrive at their destination earlier than expected, which would provide them a better experience with the airline. On the other side, the airline can take credit for the customer's satisfaction, in addition to reducing fuel usage, thus making their flights more environmentally friendly.
This project represents a significant contribution to the field of aviation as it proves that flights can be made more efficient through the usage of open source data.
ContributorsRosenbloom, Yonatan (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Govindillam, Sreenivasan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
Little is known about the state of Arctic sea ice at any given instance
in time. The harshness of the Arctic naturally limits the amount of in situ data that can be collected, resulting in gathered data being limited in both location and time. Remote sensing modalities such as satellite Synthetic Aperture Radar (SAR) imaging and laser altimetry help compensate for the lack of data, but suffer from uncertainty because of the inherent indirectness. Furthermore, precise remote sensing modalities tend to be severely limited in spatial and temporal availability, while broad methods are more accessible at the expense of precision. This thesis focuses on the intersection of these two problems and explores the possibility of corroborating remote sensing methods to create a precise, accessible source of data that can be used to
examine sea ice at local scale.
ContributorsBaker, John (Author) / Cochran, Douglas (Thesis director) / Wei, Hua (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
I study some comparative statics implications of disappointment-averse preferences
for optimal portfolios. Specifically, I find that risk-averse disappointment-averse investors
increase investment in a risky asset as a result of a monotone likelihood ratio improvement in
the asset’s distribution, a subset of First Order Stochastic improvements. This gives a testable
implication between the disappointment aversion model, and alternatives, including expected
utility. I also discuss previously noted implications for disappointment aversion in helping
explain the equity premium puzzle.
ContributorsWarrier, Raghav (Author) / Schlee, Edward (Thesis director) / Almacen, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Manually determining the health of a plant requires time and expertise from a human. Automating this process utilizing machine learning could provide significant benefits to the agricultural field. The detection and classification of health defects in crops by analyzing visual data using computer vision tools can accomplish this. In this paper, the task is completed using two different types of existing machine learning algorithms, ResNet50 and CapsNet, which take images of crops as input and return a classification that denotes the health defect the crop suffers from. Specifically, the models analyze the images to determine if a nutritional deficiency or disease is present and, if so, identify it. The purpose of this project is to apply the proven deep learning architecture, ResNet50, to the data, which serves as a baseline for comparison of performance with the less researched architecture, CapsNet. This comparison highlights differences in the performance of the two architectures when applied to a complex dataset with a multitude of classes. This report details the data pipeline process, including dataset collection and validation, as well as preprocessing and application to the model. Additionally, methods of improving the accuracy of the models are recorded and analyzed to provide further insights into the comparison of the different architectures. The ResNet-50 model achieved an accuracy of 100% after being trained on the nutritional deficiency dataset. It achieved an accuracy of 88.5% on the disease dataset. The CapsNet model achieved an accuracy of 90% on the nutritional deficiency dataset but only 70% on the disease dataset. In comparing the performance of the two models, the ResNet model outperformed the other; however, the CapsNet model shows promise for future implementations. With larger, more complete datasets as well as improvements to the design of capsule networks, they will likely provide exceptional performance for complex image classification tasks.
ContributorsChristner, Drew (Author) / Carter, Lynn (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Introduction: This research examined the prevalence of Chinese older adults’ risk factors (perceived racial discrimination, fear of COVID-19), protective factors (resilience, community support), and mental health status (depression, PTSD) in Chinese older adults aged 65 years and above during the COVID-19 pandemic. Furthermore, the relationships between risk and protective factors and their mental health outcomes were explored.
Methods: This study was a secondary data analysis using the anonymous survey data collected by a research team. Descriptive statistics were used to describe the distributions of the variables; and hierarchical multiple regression models were conducted to examine their relationships.
Results: The sample included 90 Chinese older adults in the United States. The participants demonstrated a moderate level of fear of COVID-19 (M= 21.55, SD = 4.75; range 10-33). The participants scored on the lower end of the perceived discrimination scale (M = .40, SD = 1.44 before COVID-19; M = .77, SD = 1.54 during COVID-19; range 0-7). Resilience (M = 29.02, SD = 5.78 on a scale of 0-40) demonstrated a moderate to moderately high level of resilience. As for community support, 40.3% of participants reported receiving assistance or information regarding COVID-19 from local Asian organizations, indicating a moderate level of community support. The participants reported a relatively low score for PTSD (M = 0.75 SD = 1.17 on a scale from 0 to 5) or depression (M = 2.76 SD = 2.72 on a scale from 0 to 27). Consistent with hypotheses, findings of hierarchical regression models suggested that the risk factors fear of COVID-19 and perceived racial discrimination were positively associated with PTSD symptoms while resilience was negatively associated with PTSD symptoms. Differently, none of the risk factors were significantly associated with depression symptoms while resilience showed a negative relationship with depression symptoms.
Conclusion: The findings of this research will help public health officials better understand the needs of minority and aging communities to best support them during crises similar to the COVID-19 pandemic.
ContributorsMang, David (Author) / Chia-Chen Chen, Angela (Thesis director) / Li, Wei (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2024-05
Description
This thesis investigates the quality and usefulness of "DeepDEMs" from Moon and Mars images, which are Digital Elevation Models (DEMs) created using deep learning from single optical images. High-resolution DEMs of Moon and Mars are increasingly critical for gaining insights into the slope and the elevation of the terrain in the region which helps in identifying the landing sites of possible manned missions and rovers. However, many locations of interest to scientists who use remote sensing to study the Earth or other planetary bodies have only visible image data coverage, and not repeated stereo image coverage or other data collected specifically for DEM generation. Thus, Earth and planetary scientists, geographers, and other academics want DEMs in many locations where no data resources (repeat coverage or intensive remote sensing campaigns) have been assigned for geomorphic or topographic study. One specific use for deep learning-generated terrain models would be to assess probable sites in the lunar south polar area for NASA's future Artemis III mission which aims to return people to the lunar surface. While conventional techniques (for example, needing two stereo pictures from satellites for photogrammetry) work well, this high-resolution data only covers a small portion of the planets. Furthermore, older approaches need lengthy processing durations as well as human calibration and tweaking to achieve high-quality DEMs. To address the coverage and processing time concerns, we evaluated deep learning algorithms for creating DEMs of the Moon and Mars' surfaces. We explore how the findings of this study may be used to create elevation models for planetary mapping in the future using automated methods.
ContributorsJain, Rini (Author) / Rastogi, Anant (Co-author) / Kerner, Hannah (Thesis director) / Adler, Jacob (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor)
Created2024-05