Barrett

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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Using ML to Predict Online Course Ratings

Description

The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand…

The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand what the students need. One of those tools is an online course ratings predictor. Using the predictor, online course instructors can learn the qualities that majority course takers deem as important, and thus they can adjust their lesson plans to fit those qualities. Meanwhile, students will be able to use it to help them in choosing the course to take by comparing the ratings. This research aims to find the best way to predict the rating of online courses using machine learning (ML). To create the ML model, different combinations of the length of the course, the number of materials it contains, the price of the course, the number of students taking the course, the course’s difficulty level, the usage of jargons or technical terms in the course description, the course’s instructors’ rating, the number of reviews the instructors got, and the number of classes the instructors have created on the same platform are used as the inputs. Meanwhile, the output of the model would be the average rating of a course. Data from 350 courses are used for this model, where 280 of them are used for training, 35 for testing, and the last 35 for validation. After trying out different machine learning models, wide neural networks model constantly gives the best training results while the medium tree model gives the best testing results. However, further research needs to be conducted as none of the results are not accurate, with 0.51 R-squared test result for the tree model.
ContributorsWidodo, Herlina (Author) / VanLehn, Kurt (Thesis director) / Craig, Scotty (Committee member) / Barrett, The Honors College (Contributor) / Department of Management and Entrepreneurship (Contributor) / Computer Science and Engineering Program (Contributor)
Created2021-12
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An Introduction to Unstructured Case Management

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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The Growth of Artificial Intelligence in Healthcare

Description
This project discusses what Artificial Intelligence (AI) is and how it is beneficial for society. The project is in favor of AI and talks about how AI is becoming apparent in everyday use cases, along with technology such as phones and cars. The majority of the thesis shows how AI…

This project discusses what Artificial Intelligence (AI) is and how it is beneficial for society. The project is in favor of AI and talks about how AI is becoming apparent in everyday use cases, along with technology such as phones and cars. The majority of the thesis shows how AI is helpful for healthcare and can assist physicians and nurses do their jobs. Also, how AI helps with medical imaging, drug discovery, dieting, medical devices, and other applicable cases.
ContributorsRahman, Hashim (Author) / Rowans, Leslie (Thesis director) / Connell, Janice (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2022-05
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The Impact of AI on the Health Care Industry

Description
In recent years, artificial intelligence (AI) has become an increasingly important resource for individuals and corporations. In the health care industry, there is a growing demand for AI and related technologies to assist in everything from surgery to janitorial duties. While there is the usual skepticism surrounding AI, it is…

In recent years, artificial intelligence (AI) has become an increasingly important resource for individuals and corporations. In the health care industry, there is a growing demand for AI and related technologies to assist in everything from surgery to janitorial duties. While there is the usual skepticism surrounding AI, it is important to understand how it works, its benefits, and its risk. Medically speaking, AI has the potential to aid physicians in diagnosis and treatment recommendations. The technology, however, presents issues surrounding data privacy as well as embedded biases in the algorithms used for intelligent systems. Mitigating these potential risks will be important to the successful implementation of AI in a health care setting. As artificial intelligence becomes more prominent in this space, there is a valid fear that it will result in severe job displacement and force many people out of their fields. This transition towards a more automated hospital will require many medical personnel to learn new skills and move towards jobs that require a more significant amount of empathy to thrive in the new economy. For decades, people have been worried about the effects of more automation and machinery, but we have seen that where some jobs are replaced with technology, other jobs are created. The findings of this thesis show that while AI is likely to replace some human workers, there is minimal reason to worry about complete job displacement in the near future. Long-term, the significance of AI in job replacement is more of an unknown. However, with knowledge about how artificial intelligence could be impacting us now, patients, physicians, and hospitals can better prepare and understand the impact that AI will have on each of them specifically.
ContributorsMcSpadden, Lana (Author) / Simon, Alan (Thesis director) / Ostrom, Amy (Committee member) / Barrett, The Honors College (Contributor) / Business Data Analytics (Contributor)
Created2022-05
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A Survey Study of the Current Understanding of Artificial Intelligence and Machine Learning in Medical Practice among Healthcare Professionals and the Lay Public

Description
Artificial intelligence (AI) and machine learning (ML) is rapidly evolving with enormous impact on a wide range of individual and societal matters including in health care, now and in the future. The goal of this research project is to assess the current knowledge level of AI and ML in health…

Artificial intelligence (AI) and machine learning (ML) is rapidly evolving with enormous impact on a wide range of individual and societal matters including in health care, now and in the future. The goal of this research project is to assess the current knowledge level of AI and ML in health care among healthcare professionals and the lay public. Results from this research will identify knowledge gaps and educational opportunities to improve future use and applications of AI and ML in health care.
ContributorsShen, Maria (Author) / Martin, Thomas (Thesis director) / Wheatley-Guy, Courtney (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-05
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Artificial Intelligence with Graph Neural Networks Applied to a Risk-like Board Game

Description
This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes.…

This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes. Further, almost all of these games are played on a rectangular grid. Contrarily, this project develops an AI player, referred to as GG-net, to play the online strategy game Warzone, which is based on the classic board game Risk. Warzone is played on a wide variety of irregularly shaped maps. Prior research has struggled to create an effective AI for Risk-like games due to the immense branching factor. The most successful attempts tended to rely on manually restricting the set of actions the AI considered while also engineering useful features for the AI to consider. GG-net uses no human knowledge, but rather a genetic algorithm combined with a graph neural network. Together, these methods allow GG-net to perform competitively across a multitude of maps. GG-net outperformed the built-in rule-based AI by 413 Elo (representing an 80.7% chance of winning) and an approach based on AlphaZero using graph neural networks by 304 Elo (representing a 74.2% chance of winning). This same advantage holds across both seen and unseen maps. GG-net appears to be a strong opponent on both small and medium maps, however, on large maps with hundreds of territories, inefficiencies in GG-net become more significant and GG-net struggles against the rule-based approach. Overall, GG-net was able to successfully learn the game and generalize across maps of a similar size, albeit further work is required for GG-net to become more successful on large maps.
ContributorsBauer, Andrew (Author) / Yang, Yezhou (Thesis director) / Harrison, Blake (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
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Creative Frameworks: Developing Accessible Technological Frameworks for Creative Expression

Description
Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done…

Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done with industry standard performance technology and protocols to create an accessible interface for creative expression. Artificial intelligence models were created to generate art based on simple text inputs. Users were then invited to display their creativity using the software, and a comprehensive performance showcased the potential of the system for artistic expression.
ContributorsPardhe, Joshua (Author) / Lim, Kang Yi (Co-author) / Meuth, Ryan (Thesis director) / Brian, Jennifer (Committee member) / Hermann, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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Creative Frameworks: Developing Accessible Technological Frameworks for Creative Expression

Description
Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done…

Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done with industry standard performance technology and protocols to create an accessible interface for creative expression. Artificial intelligence models were created to generate art based on simple text inputs. Users were then invited to display their creativity using the software, and a comprehensive performance showcased the potential of the system for artistic expression.
ContributorsLim, Kang Yi (Author) / Pardhe, Joshua (Co-author) / Meuth, Ryan (Thesis director) / Brian, Jennifer (Committee member) / Hermann, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05

Saying HAI: Augmenting Human Connection with Artificial Intelligence

Description
This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.
ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)
Created2022-05
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Hatfield Final Project (Spring 2022)

Description

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.
ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)
Created2022-05