Matching Items (723)
Filtering by
- Creators: Computer Science and Engineering Program
Description
This thesis investigates the design, implementation, and theoretical underpinnings of partially fluid team dynamics in tabletop game systems - an emergent and under-theorized mechanic wherein players may alter team affiliations once per round, after which alliances become fixed until the round’s conclusion. Drawing on comparative analysis of traditional and modern card games such as Zhao Peng You and French Tarot, this work offers a formal definition of the mechanic and situates it within the broader landscape of cooperative and competitive game theory. The central contribution is the development of an original board game informed by the Chinese zodiac, designed to operationalize and interrogate the principles of strategic alliance formation under conditions of bounded fluidity. Through iterative prototyping, playtesting, and rule-balancing, the design process is used to examine core tensions between player agency, emergent narrative, and systemic clarity. This thesis ultimately argues that partially fluid team mechanics offer a novel space for dynamic social interaction, supporting both strategic depth and interpersonal engagement, while avoiding the cognitive overload and alignment ambiguity characteristic of more chaotic or deduction-based team systems.
ContributorsShin, Matthew (Author) / Mack, Robert (Thesis director) / Loebenberg, Abby (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2025-05
Description
P.O.F.T.S. is an open-source fitness-tracking platform designed with transparent data privacy, a remarkable level of customization, and frequent user interaction in mind. Unlike other commercial fitness apps on the current market, P.O.F.T.S. gives the users total control over their data for a personalized fitness experience, including workout recommendations, social leaderboards, and devices synced with wearables.
ContributorsMejari, Vikas (Author) / Alfred, Heinric (Co-author) / Byrne, Jared (Thesis director) / Howell, Travis (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This project is a multi-layered sculpture built from wood, threads, beads, and bracelets, each representing a stage of life from early childhood to young adulthood. The layers correspond to different periods of time, with the materials acting as tangible markers of memory. The work is a personal piece that explores how memories change, fade, or become harder to access over time, yet remain interconnected and influential in shaping who we are.
ContributorsJohnson, Sydney (Author) / Takada, Emy (Thesis director) / Scott Lynch, Jacquelyn (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Art (Contributor)
Created2025-05
Description
In this project we are creating a tool for students that will help them gain dynamic educational
pathways based on their projects. By having a dataset of a college’s course standards and
matching the student’s work we get to show the student which courses they can gain potential
Microcredits for. By utilizing Artificial Intelligence (AI) and Natural Language Processing
(NLP) with Bidirectional Encoder Representations from Transformers (BERT) based sentence
embeddings, Facebook AI Similarity Search (Faiss) for similarity search, and KMeans for
clustering, we identify and group top-matching courses from a Firestore database to show to
students. In order to visualize the data, we use Neo4J to create a graphical nodal representation
of this data. This allows for dynamic and endless credit possibilities and creativity for students to
encourage them to try for new courses and learning.
ContributorsSingh, Suhani (Author) / Osburn, Steven (Thesis director) / Ernsberger, Karl (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Artificial intelligence has transformed the way individuals engage with information, particularly in the realm of education. Traditional AI models, such as ChatGPT and other large language models (LLMs), have become widely used as learning tools, offering assistance to students, teachers, faculty, and more. While these models have significantly improved accessibility to knowledge, many models remain static in their approach, offering generalized responses that fail to adapt to an individual’s learning style, pace, or comprehension level.
In contrast, adaptive AI models have emerged as a potential solution to the limitations of traditional AI-driven learning. Unlike static models that generate responses based on predefined training data, adaptive AI dynamically adjusts its content, difficulty, and instructional strategies based on real-time user feedback and performance. This shift from generic assistance to personalized learning could have profound implications for education, including increased engagement, improved knowledge retention, and enhanced motivation.
This thesis explores the benefits, limitations, and psychological effects of adaptive AI-driven learning models, specifically the prototypes of MyEdMaster’s Adaptive Virtual Assistant. By examining how these models influence user engagement, motivation, and learning effectiveness, the research aims to highlight their potential in personalized education. A key component of this study is the development of a custom adaptive AI model for MyEdMaster, designed to personalize learning pathways based on a user profile, user performance, and behavior. To assess its impact, the model underwent A/B testing against traditional models like ChatGPT4, alongside observational assessments, surveys, and feedback mechanisms to refine MyEdMaster’s model and synthesize meaningful conclusions about its effectiveness in enhancing individualized learning experiences.
A total of 33 participants took part in this study, representing a diverse demographic range with ages spanning from 8 to 62 years old. The sample included individuals at various educational stages, such as middle and high school students, undergraduate and graduate learners, and adults engaged in lifelong education. This diversity offered valuable insights into the impact of adaptive AI-driven learning models across different age groups.
ContributorsAganon, Shannen Mae (Author) / Meuth, Ryan (Thesis director) / Leddo, John (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Language acquisition is a lengthy and often monotonous process that can challenge a learner’s motivation over time. While existing tools such as Anki and Duolingo utilize effective spaced repetition systems to enhance memory retention, many lack the engagement necessary to maintain consistent study habits for users with low intrinsic motivation. This paper explores the potential of ludology—the study of games and their mechanics—as a framework for enhancing long-term language learning. Through the design and implementation of a game titled the Museum of Memory (and Art) (MOMaA), this thesis investigates how gameplay elements such as feedback loops, progression systems, and mnemonic devices can be used to reinforce daily language practice. The game centers around importing flashcard decks from Anki and gamifying their review via engaging mechanics set within a customizable 3D museum. A user study comparing MOMaA to traditional flashcard software evaluates the effectiveness of this gamified approach, both quantitatively and qualitatively. Findings suggest that while MOMaA slightly underperforms in raw retention compared to Anki, it demonstrates higher user engagement and satisfaction—supporting the hypothesis that game-based learning environments can serve as valuable supplements to more conventional methods for language acquisition.
ContributorsLovelace, Noah (Author) / Kobayashi, Yoshihiro (Thesis director) / Selgrad, Justin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor)
Created2025-05
Description
This thesis presents the development and testing of a novel ultrasonic communication system for IoT applications, focusing on secure locks and underwater data transmission. A hybrid On-Off Keying (OOK) and Phase Shift Keying (PSK) modulation scheme is implemented on a 500 Hz carrier, combined with amplitude modulation on an ultrasonic carrier. The system proposes to address challenges such as multi-path propagation, noise sensitivity reduction, and clock synchronization, achieving a data rate of 18-24 bytes per second in low bandwidth mode under optimal conditions. Hardware-software co-design principles are applied using low-cost microcontrollers and analog circuitry. Experimental results demonstrate robustness of the proposed system when operating in controlled environments but at the same time highlight its limitations in noisy settings, especially with obstacles reflecting ultrasonic signals. Future work could focus on improving data retention and possibly integrating neural networks for waveform detection. Likewise, if time permits, construct a duplex link and test its underwater propagation range.
ContributorsNikitin, Evan (Author) / Meuth, Ryan (Thesis director) / Indela, Soumya (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This paper presents the design and implementation of a data visualization system that connects REDCap, a secure data capture platform, with Tableau to support real-time tracking of participants in child mental health intervention research. Conducted within the ASU YFaCS Lab, the project replaces manual Excel-based workflows with automated dashboards that visualize recruitment, screening, and enrollment patterns. The system improves reporting efficiency, reduces human error, and supports Institutional Review Board (IRB) compliance. The final dashboard framework is modular, reusable, and scalable across other intervention studies and research contexts.
ContributorsBandi, Rajanandini (Author) / Osburn, Steven (Thesis director) / O'Hara, Karey (Committee member) / Torres, Marisela (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2025-05
Description
Have you ever been nervous to work out? FitPath is here for you! FitPath is a personalized fitness application that allows you to generate custom workouts that best fit your needs. You can also connect with other gym-goers to see their tracked workouts and meal plans, allowing you to learn and absorb lifestyles that fit you best! Join FitPath: Your Journey. Your Goals. Your Path.
ContributorsSingh, Yash (Author) / Raghu, Narendiran (Co-author) / Jutla, Pranav (Co-author) / Byrne, Jared (Thesis director) / Howell, Travis (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor)
Created2025-05
Description
Understanding the factors that drive foraging decisions in worker honey bees (Apis mellifera L.) is a first step toward understanding bees’ collective behavior. In this study, we tested the hypothesis that a honey bee's decision to leave the hive to forage is best predicted by its age, followed in decreasing importance by a recent visit to the hive's "dance floor" where returned foragers communicate their findings, the time of day, and the time of year. Using spatial tracking data collected from an observation hive at the University of Konstanz, Germany, a data cleaning pipeline was developed and the best machine learning algorithm to predict individual foraging events based on these factors was identified. The Random Forest model achieved the highest predictive performance after addressing dataset imbalance, and provided insight into the relative importances of each factor. Our results disproved the central role of age and confirmed the significance of recent social contact in predicting foraging behavior, with time of day and seasonality contributing secondary effects. These findings deepen our understanding of honey bee decision-making and highlight the power of computational approaches in studying collective behavior.
ContributorsHamada, Diya (Author) / Daniels, Bryan (Thesis director) / Pavlic, Theodore (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05