Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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Description
Aquatic macroinvertebrates are important for many ecological processes within river ecosystems and, as a result, their abundance and diversity are considered indicators of water quality and ecosystem health. Macroinvertebrates can be classified into functional feeding groups (FFG) based on morphological-behavioral adaptations. FFG ratios can shift due to changes in normal disturbance patterns, such as changes in precipitation, and from human impact. Due to their increased sensitivity to environmental changes, it has become more important to protect and monitor aquatic and riparian communities in arid regions as climate change continues to intensify. Therefore, the diversity and richness of macroinvertebrate FFGs before and after monsoon and winter storm seasons were analyzed to determine the effect of flow-related disturbances. Ecosystem size was also considered, as watershed area has been shown to affect macroinvertebrate diversity. There was no strong support for flow-related disturbance or ecosystem size on macroinvertebrate diversity and richness. This may indicate a need to explore other parameters of macroinvertebrate community assembly. Establishing how disturbance affects aquatic macroinvertebrate communities will provide a key understanding as to what the stream communities will look like in the future, as anthropogenic impacts continue to affect more vulnerable ecosystems.
ContributorsSainz, Ruby (Author) / Sabo, John (Thesis director) / Grimm, Nancy (Committee member) / Lupoli, Christina (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.
ContributorsToye, Abigail Elizabeth (Author) / Linde, Jennifer (Thesis director) / Abele, Kelsey (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This research looks at a group of students from Tumaini Children's Home in Nyeri, Kenya. The purpose of this paper is to explore why this particular group of students is so academically successful. Quantitative research was taken from the average 2013 test scores of Tumaini students who took the Kenyan Certificate of Primary Education (KCPE) exam in comparison to the scores of students who are not residing in the orphanage. Qualitative research involves interviews from those students who live in Tumaini and interviews from adults who are closely connected to the orphanage. The purpose is to understand why the students are performing so well academically and what support they have created for themselves that allows them to do so.
ContributorsTooker, Amy Elizabeth (Author) / Puckett, Kathleen (Thesis director) / Cocchiarella, Martha (Committee member) / Barrett, The Honors College (Contributor) / Division of Teacher Preparation (Contributor)
Created2014-12
Description
The Latino population is the fastest growing minority group in the United States (U.S Census Bureau, 2003). Such a rapidly changing demographic stresses the importance of implementing strategies into the community social framework to accommodate for cultural and language differences. This research paper seeks to answer: what factors influence the sense of community among Latino families in Phoenix? The following questions will help to assess the dynamic relationship between sense of community and literacy 1) what is the perceived importance of literacy among Latino families living in Phoenix? 2) How is language development reflected among the family dynamics within a predominantly collectivist culture? It is hypothesized that both collectivism and literacy are the main influences on sense of community among this population.
ContributorsBennett, Julie (Author) / Glenberg, Arthur (Thesis director) / Restrepo, Laida (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of International Letters and Cultures (Contributor)
Created2015-05
Description
The objective of this research is to determine an approach for automating the learning of the initial lexicon used in translating natural language sentences to their formal knowledge representations based on lambda-calculus expressions. Using a universal knowledge representation and its associated parser, this research attempts to use word alignment techniques to align natural language sentences to the linearized parses of their associated knowledge representations in order to learn the meanings of individual words. The work includes proposing and analyzing an approach that can be used to learn some of the initial lexicon.
ContributorsBaldwin, Amy Lynn (Author) / Baral, Chitta (Thesis director) / Vo, Nguyen (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Introduction/Purpose: This paper describes the process of the community needs assessment phase of program implementation for the Student Health Outreach for Wellness (SHOW) clinic. Homeless individuals are more likely (than non homeless individuals) to experience serious illness, depression and mental illness. Access to health care has been identified as a barrier to receiving appropriate health care to manage the diseases and conditions clients may have. SHOW's vision is to operate on Saturdays utilizing Health Care for the Homeless (HCH) to offer extended primary health care hours, along with offering health promotion programming to address the biopsychosocial components of their health. Ultimately, this aims to reduce the homeless population's need to visit emergency room departments for non- urgent, primary care visits. Methods: To validate the need for this clinic's operation of programming and health services, a community needs assessment was conducted to collect data about the population's current health status. Forty-three people (n=43) ages 20-76 (M = 44.87) were surveyed by a trained research team using interview questionnaires. Results: The results show a prevalence of self\u2014reported physical and behavioral conditions, and support that this population would benefit from extended hours of care. Mental and behavioral health conditions are the most prevalent conditions (with the highest rates of depression (41.86%) and anxiety disorder (32.56%)), followed by the common cold (23.36%) and back pain (16.28%). The average reported emergency department (ED) visits within the past six months was 1.18 times. Almost everyone surveyed would visit a free medical clinic on the Human Services Campus (HSC) staffed by health staff and health professional students on the weekends (93.18%). Conclusion: Overall, the community needs assessment conducted for SHOW supports the need for weekend access to health care facilities and an interest in health programming for this population.
ContributorsShqalsi, Eneida Agustin (Author) / Hoffner, Kristin (Thesis director) / Harrell, Susan (Committee member) / Harper, Erin (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-05
Description
Artificial intelligence (AI) is a burgeoning technology, industry, and field of study. While interest levels regarding its applications in marketing have not yet translated into widespread adoption, AI holds tremendous potential for vastly altering how marketing is done. As such, AI in marketing is a crucial topic to research. By analyzing its current applications, its potential use cases in the near future, how to implement it and its areas for improvement, we can achieve a high-level understanding of AI's long-term implications in marketing. AI offers an improvement to current marketing tactics, as well as entirely new ways of creating and distributing value to customers. For example, programmatic advertising and social media marketing can allow for a more comprehensive view of customer behavior, predictive analytics, and deeper insights through integration with AI. New marketing tools like biometrics, voice, and conversational user interfaces offer novel ways to add value for brands and consumers alike. These innovations all carry similar characteristics of hyper-personalization, efficient spending, scalable experiences, and deep insights. There are important issues that need to be addressed before AI is extensively implemented, including the potential for it to be used maliciously, its effects on job displacement, and the technology itself. The recent progression of AI in marketing is indicative that it will be adopted by a majority of companies soon. The long-term implications of vast implementation are crucial to consider, as an AI-powered industry entails fundamental changes to the skill-sets required to thrive, the way marketers and brands work, and consumer expectations.
ContributorsCannella, James (Author) / Ostrom, Amy (Thesis director) / Giles, Charles (Committee member) / Department of Marketing (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks. For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task. This research project aims to investigate human-robot trust through a collaborative game of logic that can be played with a human and a robot together. This thesis details the development of a game of logic that could be used for this purpose. The game of logic is based upon a popular game in AI research called ‘Wumpus World’. The original Wumpus World game was a low-interactivity game to be played by humans alone. In this project, the Wumpus World game is modified for a high degree of interactivity with a human player, while also allowing the game to be played simultaneously by an AI algorithm.
ContributorsBoateng, Andrew Owusu (Author) / Sodemann, Angela (Thesis director) / Martin, Thomas (Committee member) / Software Engineering (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05