Topographic maps are an invaluable tool for planning routes through unfamiliar terrain. However, accurately planning routes on topographic maps is a time- consuming and error-prone task. One factor is the difficulty of interpreting the map itself, which requires prior knowledge and practice. Another factor is the difficulty of making choices between possible routes that have different trade-offs between length and the terrain they traverse. To alleviate these difficulties, this thesis presents a system to automate the process of finding routes on scanned images of topographic maps. The system allows users to select any two points on a topographic map and identify their specific preferences for their route. This system extracts terrain and contour line data from topographic map images using image processing techniques and then uses the A* Search algorithm to find a route between the specified points. This system can be used as a starting point for hand-drawn routes, as a means of considering alternative routes, or to entirely replace drawing routes by hand. This thesis also presents a user study which shows that this system produces routes in a significantly shorter time than hand-drawn routes, and with a similar level of accuracy.

The use of Virtual Reality (VR) in a variety of professional, military, governmental, and educational fields has continued to expand over the past several decades, and the recent Covid-19 pandemic has brought attention to this field. This study surveys 154 college students over 23 questions that include various demographics that can be used to look for discriminators, multiple-choice VR-related questions, as well as a few free-form questions about use of VR in learning environments. The students’ experience with, interest in, and thoughts on how to best use VR vary considerably. The Covid-19 pandemic is found to have limited impact thus far in terms of VR use, but the interest in using VR in schools since then has generally increased quite a bit. Commitment to invest in VR were it to be expanded and provide continual feedback varies quite a bit as well but is strong. A statistical 2 analysis shows that, at a high confidence level, males generally are more experienced with VR in general, have a greater interest in seeing VR implemented further, and are more committed to radical changes in educational methodology than females are. In addition, it is found that Hispanics/Latinos, Black / African Americans, Pacific Islanders, and those of mixed race are more inclined to provide continual feedback as regards the implementation of VR in the school curriculum than (non-Hispanic) White and Asian people are.

In this dissertation, we investigate various aspects of signed graphs, with a particular focus on signings and sign-symmetric signed graphs. We begin by examining the complete graph on six vertices with one edge deleted ($K_6$\textbackslash e) and explore the different ways of signing this graph up to switching isomorphism. We determine the frustration index (number) of these signings and investigate the existence of sign-symmetric signed graphs. We then extend our study to the $K_6$\textbackslash 2e graph and the McGee graph with exactly two negative edges. We investigate the distinct ways of signing these graphs up to switching isomorphism and demonstrate the absence of sign-symmetric signed graphs in some cases. We then introduce and study the signed graph class $\mathcal{S}$, which includes all sign-symmetric signed graphs, we prove several theorems and lemmas as well as discuss the class of tangled sign-symmetric signed graphs. Also, we study the graph class $\mathcal{G}$, consisting of graphs with at least one sign-symmetric signed graph, prove additional theorems and lemmas, and determine certain families within $\mathcal{G}$. Our results have practical applications in various fields such as social psychology and computer science.

The growing volume of opinion data presents a significant challenge for policymakers striving to distill public sentiment into actionable decisions. This study aims to explore the capability of large language models (LLMs) to synthesize public opinion data into coherent policy recommendations. We specifically leverage Mistral 7B and Mixtral 8x7B models for text generation and have developed an architecture to process vast amounts of unstructured information, integrate diverse viewpoints, and extract actionable insights aligned with public opinion. Using a retrospective data analysis of the Polis platform debates published by the Computational Democracy Project, this study examines multiple datasets that span local and national issues with 1600 statements posted and voted upon by over 3400 participants. Through content moderation, topic modeling, semantic structure extraction, insight generation, and argument mapping, we dissect and interpret the comments, leveraging voting data and LLMs for both quantitative and qualitative insights. A key contribution of this thesis is demonstrating how LLM reasoning techniques can enhance content moderation. Our content moderation approach shows performance improvements using comment deconstruction in multi-class classification, underscoring the trade-offs between moderation strategies and emphasizing a balance between precision and cautious moderation. Using comment clustering, we establish a hierarchy of semantically linked topics, facilitating an understanding of thematic structures and the generation of actionable insights. The generated argument maps visually represent the relationships between topics and insights, and highlight popular opinions. Future work will leverage advanced semantic extraction and reasoning techniques to enhance insight generation further. We also plan to generalize our techniques to other major discussion platforms, including Kialo. Our work contributes to the understanding of using LLMs for policymaking and offers a novel approach to structuring complex debates and translating public opinion into actionable policy insights.

Hands-on experiential learning activities are an important component of computing education disciplines. Laboratory environments provide learner access to real world equipment for completing experiments. Local campus facilities are commonly used to host laboratory classes. While campus facilities afford hands-on experience with real equipment high maintenance costs, restricted access, and limited flexibility diminish laboratory effectiveness. Web-based simulation and remote laboratory formats have emerged as low cost options, which allow open access and learner control. Simulation lacks fidelity and remote laboratories are considered too complex for novice learners. A web-based combined laboratory format incorporates the benefits of each format while mitigating the shortcomings. Relatively few studies have examined the cognitive benefits of web-based laboratory formats in meeting computing education students’ goals. A web-based combined laboratory model that incorporates motivation strategies was developed to address non-traditional computing education students’ preferences for control of pace and access to learning. Internal validation of the laboratory model was conducted using pilot studies and Delphi expert review techniques. A panel of instructors from diverse computing education backgrounds reviewed the laboratory model. Panel recommendations guided enhancement of the model design.

Blind and visually impaired students are under-represented in the science, technology, engineering, and mathematics disciplines of higher education and the workforce. This is due primarily to the difficulties they encounter in trying to succeed in mathematics courses. While there are sufficient tools available to create Braille content, including the special Nemeth Braille used in the U.S. for mathematics constructs, there are very few tools to allow a blind or visually impaired student to create his/her own mathematical content in a manner that sighted individuals can use. The software tools that are available are isolated, do not interface well with other common software, and may be priced for institutional use instead of individual use. Instructors are unprepared or unable to interact with these students in a real-time manner. All of these factors combine to isolate the blind or visually impaired student in the study of mathematics. Nemeth Braille is a complete mathematical markup system in Braille, containing everything that is needed to produce quality math content at all levels of complexity. Blind and visually impaired students should not have to learn any additional markup languages in order to produce math content. This work addressed the needs of the individual blind or visually impaired student who must be able to produce mathematical content for course assignments, and who wishes to interact with peers and instructors on a real-time basis to share mathematical content. Two tools were created to facilitate mathematical interaction: a Nemeth Braille editor, and a real-time instant messenger chat capability that supports Nemeth Braille and MathML constructs. In the Visually Impaired view, the editor accepts Nemeth Braille input, displays the math expressions in a tree structure which will allow sub-expressions to be expanded or collapsed. The Braille constructs can be translated to MathML for display within MathType. Similarly, in the Sighted view, math constructs entered in MathType can be translated into Nemeth Braille. Mathematical content can then be shared between sighted and visually impaired users via the instant messenger chat capability. Using Math in the Dark software, blind and visually impaired students can work math problems fully in Nemeth Braille and can seamlessly convert their work into MathML for viewing by sighted instructors. The converted output has the quality of professionally produced math content. Blind and VI students can also communicate and share math constructs with a sighted partner via a real-time chat feature, with automatic translation in both directions, allowing VI students to obtain help in real-time from a sighted instructor or tutor. By eliminating the burden of translation, this software will help to remove the barriers faced by blind and VI students who wish to excel in the STEM fields of study.

With the shift to learn and consume information through our mobile devices, most academic research is still only presented in long-form text. The Stanford Scholar Initiative has explored the segment of content creation and consumption of academic research through video. However, there has been another popular shift in presenting information from various social media platforms and media outlets in the past few years. Snapchat and Instagram have introduced the concept of tappable “Stories” that have gained popularity in the realm of content consumption. To accelerate the growth of the creation of these research talks, I propose an alternative to video: a tappable Snapchat-like interface. This style is achieved using AMP, Google’s open source project to optimize web experiences on mobile, and particularly the AMP Stories visual medium. My research seeks to explore how the process and quality of consuming the content of academic papers would change if instead of watching videos, users would consume content through Stories on mobile instead. Since this form of content consumption is still largely unresearched in the academic context, I approached this research with a human-centered design process, going through a few iterations to test various prototypes before formulating research questions and designing an experiment. I tested various formats of research consumption through Stories with pilot users, and learned many lessons to iterate from along the way. I created a way to consume research papers in a Stories format, and designed a comparative study to measure the effectiveness of consuming research papers through the Stories medium and the video medium. The results indicate that Stories are a quicker way to consume the same content, and improve the user’s pace of comprehension. Further, the Stories medium provides the user a self-paced method—both temporally and content-wise—to consume technical research topics, and is deemed as a less boring method to do so in comparison to video. While Stories gave the learner a chance to actively participate in consumption by tapping, the video experience is enjoyed because of its reduced effort and addition of an audio component. These findings suggest that the Stories medium may be a promising interface in educational contexts, for distributing scientific content and assisting with active learning.

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum.

This thesis introduces a new methodology for robust form parameter design of complex hull form geometry via constraint programming, automatic differentiation, interval arithmetic, and truncated hierarchical B- splines. To date, there has been no clearly stated methodology for assuring consistency of general (equality and inequality) constraints across an entire geometric form parameter ship hull design space. In contrast, the method to be given here can be used to produce guaranteed narrowing of the design space, such that infeasible portions are eliminated. Furthermore, we can guarantee that any set of form parameters generated by our method will be self consistent. It is for this reason that we use the title feasible form parameter design. In form parameter design, a design space is represented by a tuple of design parameters which are extended in each design space dimension. In this representation, a single feasible design is a consistent set of real valued parameters, one for every component of the design space tuple. Using the methodology to be given here, we pick out designs which consist of consistent parameters, narrowed to any desired precision up to that of the machine, even for equality constraints. Furthermore, the method is developed to enable the generation of complex hull forms using an extension of the basic rules idea to allow for automated generation of rules networks, plus the use of the truncated hierarchical B-splines, a wavelet-adaptive extension of standard B-splines and hierarchical B-splines. The adaptive resolution methods are employed in order to allow an automated program the freedom to generate complex B-spline representations of the geometry in a robust manner across multiple levels of detail. Thus two complementary objectives are pursued: ensuring feasible starting sets of form parameters, and enabling the generation of complex hull form geometry.

A key challenge of the learning sciences is moving research results into practice. Educators on the front lines perceive little value in the outputs of education research and demand more “usable knowledge”. This work explores the potential instead of usable artifacts to translate knowledge into practice, adding scientists as stakeholders in an interaction design process. The contributions are two effective systems, the scientific and contextual principles in their design, and a research model for scientific research through interaction design. College student study practices are the domain chosen for the development of these methods. Iterative ethnographic fieldwork identified two systems that would be likely to advance both learning in practice and knowledge for applying the employed theories in general. Nudge was designed to improve students’ study time management by regularly emailing students with explicit recommended study activities. It reconceptualizes the syllabus into an interactive guide that fits into modern students' attention streams. Examplify was designed to improve how students learn from worked example problems by modularizing them into steps and scaffolding their metacognitive behaviors though problem-solving and self-explanation prompts. It combines these techniques in a way that is exceedingly easy to author, using existing answer keys and students' self-evaluations. Nudge and Examplify were evaluated experimentally over a full semester of a lecture-based introductory chemistry course. Nudge messages increased students’ sense of achievement and interacted with students’ existing time management skills to improve exam grades for poorer students. Among students who could choose whether to receive them, 80% did. Students with access to Examplify had higher exam scores (d=0.26), especially on delayed measures of learning (d=0.40). A key design decision in Examplify was not clearly resolvable by existing theory and so was tested experimentally by comparing two variants, one without prompts to solve the steps. The variant without problem solving was less effective (d=0.77) and less used, while usage rates of the variant with problem solving increased over time. These results support the use of the design methods employed and provide specific empirical recommendations for future designs of these and similar systems for implementing theory in practice.