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.

Displaying 1 - 4 of 4
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Description

Social injustice issues are a familiar, yet very arduous topic to define. This is because they are difficult to predict and tough to understand. Injustice issues negatively affect communities because they directly violate human rights and they span a wide range of areas. For instance, injustice issues can relate to

Social injustice issues are a familiar, yet very arduous topic to define. This is because they are difficult to predict and tough to understand. Injustice issues negatively affect communities because they directly violate human rights and they span a wide range of areas. For instance, injustice issues can relate to unfair labor practices, racism, gender bias, politics etc. This leaves numerous individuals wondering how they can make sense of social injustice issues and perhaps take efforts to stop them from occurring in the future. In an attempt to understand the rather complicated nature of social injustice, this thesis takes a data driven approach to define a social injustice index for a specific country, India. The thesis is an attempt to quantify and track social injustice through social media to see the current social climate. This was accomplished by developing a web scraper to collect hate speech data from Twitter. The tweets collected were then classified by their level of hate and presented on a choropleth map of India. Ultimately, a user viewing the ‘India Social Injustice Index’ map should be able to simply view an index score for a desired state in India through a single click. This thesis hopes to make it simple for any user viewing the social injustice map to make better sense of injustice issues.

ContributorsDeosthali, Shefali (Author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Mathews, Nicolle (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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Description
In the age of information, collecting and processing large amounts of data is an integral part of running a business. From training artificial intelligence to driving decision making, the applications of data are far-reaching. However, it is difficult to process many types of data; namely, unstructured data. Unstructured data is

In the age of information, collecting and processing large amounts of data is an integral part of running a business. From training artificial intelligence to driving decision making, the applications of data are far-reaching. However, it is difficult to process many types of data; namely, unstructured data. Unstructured data is “information that either does not have a predefined data model or is not organized in a pre-defined manner” (Balducci & Marinova 2018). Such data are difficult to put into spreadsheets and relational databases due to their lack of numeric values and often come in the form of text fields written by the consumers (Wolff, R. 2020). The goal of this project is to help in the development of a machine learning model to aid CommonSpirit Health and ServiceNow, hence why this approach using unstructured data was selected. This paper provides a general overview of the process of unstructured data management and explores some existing implementations and their efficacy. It will then discuss our approach to converting unstructured cases into usable data that were used to develop an artificial intelligence model which is estimated to be worth $400,000 and save CommonSpirit Health $1,200,000 in organizational impact.
ContributorsBergsagel, Matteo (Author) / De Waard, Jan (Co-author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Burns, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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Description

Engaging users is essential for designers of any exhibit, such as the human-computer interface, the visual effects, or the informational content. The need to understand users’ experiences and learning gains has motivated a focus on user engagement across computer science. However, there has been limited review of how human-computer interaction

Engaging users is essential for designers of any exhibit, such as the human-computer interface, the visual effects, or the informational content. The need to understand users’ experiences and learning gains has motivated a focus on user engagement across computer science. However, there has been limited review of how human-computer interaction research interprets and employs the concepts in museum and exhibit settings, specifically their joint effects. The purpose of this study is to assess users’ experience and learning outcome, while interacting with a web application part of an exhibit that showcases the NASA Psyche spacecraft model. This web application provides an interactive menu that allows the user to navigate on the touch panel installed within the Psyche Spacecraft Exhibit. The user can press the button on the menu which will light up the corresponding parts of the model with a detailed description displayed on the panel. For this study, participants were required to take a questionnaire, a pretest, and a posttest. They were also required to interact with the web application while wearing an Emotiv EPOC+ EEG headset that measures their emotions while they were visiting the exhibit. During the study, data such as questionnaire results, sensed emotions from the EEG headset, and pretest and posttest scores were collected. Using the information gathered, the study explores user experience and learning gains through both biometrics and traditional tools. The findings show that users felt engaged and frustrated the most and that users gained more knowledge but at varying degrees from the interaction. Future work can be done to lower the levels of frustration and keep learning gains at a more consistent rate by improving the exhibit design to better meet various learning needs and visitor profiles.

ContributorsMa, Yumeng (Author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Gonzalez Sanchez, Javier (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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Description
Unstructured data management proves an increasingly valuable asset for organizations today as the amount of data organizations own increases every year. The purpose of this project is to detail the process which ServiceNow and CommonSpirit Health use in developing their new IntelliRoute model which aims to classify and auto-resolve a

Unstructured data management proves an increasingly valuable asset for organizations today as the amount of data organizations own increases every year. The purpose of this project is to detail the process which ServiceNow and CommonSpirit Health use in developing their new IntelliRoute model which aims to classify and auto-resolve a significant portion of CommonSpirit Health’s more than 3,000,000 HR service-related cases. This paper examines typical strategies used to manage unstructured data and ServiceNow’s approach. Their approach focuses on data labelling by attaching a criticality sentiment to unstructured data and relating helpful knowledge base articles. The labelled data is then used to train an Artificial Intelligence model which automatically labels cases and refers appropriate knowledge articles.
ContributorsDe Waard, Jan (Author) / Bergsagel, Matteo (Co-author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Burns, Christopher (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05