Browsing by Subject "Data visualization"

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The application of visualization methods to educational data sets with inspiration from statistical and fluid mechanics
(2014-05) Bendinelli, Anthony James; Marder, Michael P., 1960-
This dissertation focuses on the development of visualization methods that enable us to examine longitudinal data in a unique way. We take inspiration from statistical and fluid mechanics to represent our data as a "flow" through time. Our visualizations represent vector fields (or flow plots), streamlines, and trajectories, and they are constructed in a similar manner to how one might analyze the aggregate motion of particles in a fluid. However, the subject of our research extends beyond ordinary fluid mechanics. We will use our visualizations to examine statewide standardized test scores in Texas from 2003 to 2011. The nature of the data makes it a perfect match for our methodology, since students' test scores tend to change over time in a semi-deterministic but nonlinear manner. Furthermore, our methods represent a departure from the standard ways of analyzing educational data. By visualizing the changes in students' test scores over a nine-year period, we discovered that our flow plots were changing with the eventual graduating class of 2012. The change in our visualizations was caused by an educational policy known as the Student Success Initiative, or SSI. The policy forced students to pass their standardized tests in 5th and 8th grade, or risk being held back a grade. To help with this process, students who initially failed were given extra instruction and additional opportunities to take the test. SSI was implemented in such a way that it would affect the class of 2012 and beyond, although we did not know of the program's existence until our plots had been developed. SSI had a successful impact on the educational career of Texas students; a far greater percentage of students were able to pass the 5th and 8th grade standardized tests after SSI was implemented. The striking feature of SSI, however, is that it also significantly improved test scores in 6th, 7th, 9th, and 10th grade. Despite its success at improving test scores over many years and grades, the program was eventually defunded. This was partially due to an inability to construct a lengthy longitudinal analysis of the program's influence. Our methodology would have conclusively shown the effectiveness of the SSI policy. Despite the defunding of the SSI, I am confident our methodology can be extended to illustrate changes in other data systems. These systems may or may not be related to education; our code and techniques are designed to be as universal as possible. We will explore several extensions to other data sets at the end of this dissertation.
Data visualization as craft
(2011-05) Rowe, Cathryn Elaine; Shields, David, M.F.A.; Hall, Peter, 1965-
For my MFA, I have decided to explore data visualization not as an automated technology but as a craft—a systematic and precise practice done entirely by hand. Though the craft-based approach is not appropriate for all types of data creation and visualization, as an investigatory tool it grants a level of access and intimacy lacking in computerized analyses. I discuss the limitations and benefits of this type of approach, as well as provide an overview of key influences and precedents. I have also included select projects developed over the course of my studies that highlight my use of data visualization for a range of subjects and intents, including reading piano sheet music more easily and investigating a photographer’s compositional process. The report concludes by projecting how this craft-based approach for data visualization may be integrated with an automated method.
Geometric fault detection using 3D Kiviat plots and their applications
(2017-05) Wang, Ray Chen; Baldick, Ross; Baldea, Michael; Arapostathis, Ari; Edgar, Thomas F; Ghosh, Joydeep
The surge in large-scale data being collected through various social and economic systems comes along with the ever-increasing need to understand and gain insight from the data being collected. This has spurred on the development and advent of big data analytics in many different areas such as healthcare, e-commerce, and group-sharing applications. This applies also to the process industry as well, as the development of more complex processes, which in turn require increased monitoring, mean that a larger amount of data are being collected than previously seen. This data is not only high in volume (measurements taken with a high sampling frequency), but also high in dimensionality (many sensors set up throughout the process). Process monitoring requires the continuous observation of such high dimensional and high volume data, but current visualization techniques do not lend themselves to do doing so. Furthermore, parallel to process monitoring is the desire for fault detection capability -- to detect faults as soon as they occur or predict them before they occur. For that reason it is ideal if there is a visualization technique that also contributes to fault detection efforts, so that both process monitoring and fault detection is satisfied. To that end, in this dissertation the development of three-dimensional (3D) Kiviat diagrams and its use in fault detection is explored in great detail. In Kiviat diagrams, axes are laid out radially around a center point, in contrast to axes being perpendicular to one another in traditional score plots, or in parallel to one another as seen in parallel coordinates. This theoretically allows for an infinite number of axes, and therefore high dimensional data, to be plotted on one figure at once. Due to the time-explicit nature of process data, the addition of a third axis normal to the Kiviat diagram is proposed as well. In the Kiviat diagram representation, each sample forms a polygon on the plot. This is taken advantage of for fault detection purposes by condensing each polygon into its centroid. By doing so the state of the process at every point in time can be represented by its centroid -- this allows for multivariate fault detection to be performed. Using these centroids, a variety of fault detection mechanisms are proposed specific to the types of processes the data is obtained from. The mechanisms are developed for 3 process types commonly seen in industry -- continuous processes, batch processes, and periodic processes. For each process type the fault detection mechanism is detailed and case studies are laid out, demonstrating the application of the method.
Su voz, su decisión : data-driven system to support day laborers in making informed employment decisions
(2016-08) Narya, Shrankhla; Gorman, Carma; Bias, Randolph; Park, Jiwon; Boggess, Bethany
Wage theft and worksite injury is a significant issue for day workers in Texas and across the nation. In Texas, where a majority of day laborers are undocumented and therefore more vulnerable, the most urgent issue that needs to be addressed is exploitation, which is often compounded by laborers’ lack of access to information about worker rights and employers’ reputations. In 2014 alone, 524 workers were documented to have been killed on their job site in Texas, while many deaths went unreported. And between 2010 and 2014, more than 40,000 Texan workers were victims of wage theft amounting to a total of more than $70 million. Operating in the field of ICT (Information and Communication Technology), I hypothesize that access to both English- and Spanish-language information about worker rights and potential employers’ labor violation-related records can empower workers to make informed employment decisions that will increase their safety and prosperity. At a time when the field of design and advanced digital technologies can skew toward serving the privileged and elite of the society, I am using technology to help members of marginalized/disadvantaged communities use information to improve their economic condition and quality of life: in short, to effect social justice from the bottom up. In my thesis project, Su Voz, Su Decisión, meaning your voice, your decision, I use the methodology of user-centered design to design a mobile app that provides a system of information access for day laborers in Austin. In this report, I will discuss the process of user experience (UX) design that I followed to design the solution, which is a mobile app for Android and iOS, and how I used physical data visualization to represent the data that helped me create the app.
The impact of learner metacognition and goal orientation on problem-solving in a serious game environment
(2018-08) Liu, Sa, Ph. D.; Liu, Min, Ed. D.; Horton, Lucas; Resta, Paul; Keating, Xiaofen
To understand the impact of two learner characteristics—metacognition and goal orientation—on problem-solving, this study investigated 159 undergraduate learners’ metacognition, goal orientations, and problem-solving performances and processes in a laboratory setting using a Serious Game (SG) environment—Alien Rescue (AR)—that adopts Problem-based Learning (PBL) pedagogy for teaching space science. Utilizing multiple data sources, including computer log data and problem-solving solution scores within the SG, survey data, gameplay screencast videos, and interview data, this study combined a sequential mixed method design and serious games analytics techniques to answer the following two questions: (a) To what extent are learner problem-solving performance differences based on learner characteristics, and why? (b) To what extent are learner problem-solving process differences based on learner characteristics, and why? The results indicated that (a) learner metacognition affected problem-solving. Specifically, there were statistically significant differences in learner problem-solving performances based on metacognition, and learners also demonstrated different problem-solving processes based on metacognition. (b) Learner goal orientation impacted problem-solving. Particularly, learners in different goal orientation groups had different problem-solving processes. (c) The interaction between metacognition and goal orientations had an impact on learner problem-solving performances. Specifically, learners were clustered into three groups based on these two characteristics, including (a) high metacognition and high multiple goal orientations, (b) low metacognition and medium multiple goal orientations, and (c) medium metacognition and low multiple goal orientations. Learner problem-solving performances were statistically significant based on these three clusters. In addition, learner metacognition and goal orientations together could predict learner problem-solving performances. (d) The interaction between metacognition and goal orientations also had an impact on learner problem-solving processes. These differences in learner problem-solving performances and processes can be explained by learner characteristic differences, the problem complexity, SG design, and Dunning-Kruger effects (i.e., the cognitive bias that people of low metacognitive ability might mistakenly assess their metacognitive level as higher than it is). In addition, this study summarized 10 steps of how to be a successful and efficient problem solver in AR. These steps are as follows: 1) identify the problem correctly; 2) explore the 3D environment by visiting all rooms in AR and look over all tools; 3) discover what one alien species needs to survive in Alien Database; 4) search the Solar System Database for possible planets; 5) develop hypotheses about where this alien species can live; 6) figure out if there is any missing information needed for making a decision; 7) launch probes to gather information in the Probe Design room; 8) check the data from the probe in the Mission Control room; 9) decide whether the selected planet is a good choice for the selected alien species; 10) if so, write a recommendation message with the justification in the Communication Center—if not, go back to step 4. This research offers additional understanding of learner characteristic impacts on problem-solving in SG environments with PBL pedagogy. It can also contribute to future designs of these environments to benefit learners based on their metacognitive levels. In addition, the study limitations and further research in this area are discussed.
Uses and consequences of data visualization and analytic tools in online games
(2012-05) Givens, Travis Wayne; Wilkins, Karin Gwinn, 1962-; Chen, Wenhong
This thesis examines the usage of and attitudes toward data visualization and analytic tools in three genres of online games. Using an online survey, this research analyzes responses from participants regarding their play habits and attitudes online. Several scales are generated identifying different player demographics such as emotional attitudes, competitive attitudes, technological attitudes, spectator involvement, and overall attitudes toward information customization. In addition, several genre specific scales are created for massive multiplayer online games (MMO), real time strategy (RTS) and first person shooting (FPS) games. This research concludes that competitive attitudes are moderately correlated with information customization and implementation of data visualization tools. Additionally, the relationship between the usage of data visualization tools are strongest with the MMO genre compared to the RTS or FPS genres. In addition, my research shows a strong preference between the responses for the usage of data visualization tools amongst those who report higher levels of spectator involvement with online games. Finally, my research concludes that there is a strong relationship between the amount of time players spend playing online games and the attitudes toward and usage of data visualization tools.