Matching Items (55)
Description
Programming is quickly becoming as ubiquitous and essential a skill as general mathematics. However, many elementary and high school students are still not aware of what the computer science field entails. To make matters worse, students who are introduced to computer science are frequently being fed only part of what it is about rather than its entire construction. Consequently, they feel out of their depth when they approach college. Research has discovered that by teaching computer science and programming through a problem-driven approach and focusing on a combination of syntax and computational thinking, students can be prepared when entering higher levels of computer science education.
This thesis describes the design, development, and early user testing of a theory-based virtual world for computer science instruction called System Dot. System Dot was designed to visually manifest programming instructions into interactable objects, giving players a way to see coding as tangible entities rather than text on a white screen. In order for System Dot to convey the true nature of computer science, a custom predictive recursive descent parser was embedded in the program to validate any user-generated solutions to pre-defined logical platforming puzzles.
Steps were taken to adapt the virtual world to player behavior by creating a system to detect their learning style playing the game. Through a dynamic Bayesian network, System Dot aims to classify a player’s learning style based on the Felder-Sylverman Learning Style Model (FSLSM). Testers played through the first half of System Dot, which was enough to test out the Bayesian network and initial learning style classification. This classification was then compared to the assessment by Felder’s Index of Learning Styles Questionnaire (ILSQ). Lastly, this thesis will also discuss ways to use the results from the user testing to implement a personalized feedback system for the virtual world in the future and what has been learned through the learning style method.
This thesis describes the design, development, and early user testing of a theory-based virtual world for computer science instruction called System Dot. System Dot was designed to visually manifest programming instructions into interactable objects, giving players a way to see coding as tangible entities rather than text on a white screen. In order for System Dot to convey the true nature of computer science, a custom predictive recursive descent parser was embedded in the program to validate any user-generated solutions to pre-defined logical platforming puzzles.
Steps were taken to adapt the virtual world to player behavior by creating a system to detect their learning style playing the game. Through a dynamic Bayesian network, System Dot aims to classify a player’s learning style based on the Felder-Sylverman Learning Style Model (FSLSM). Testers played through the first half of System Dot, which was enough to test out the Bayesian network and initial learning style classification. This classification was then compared to the assessment by Felder’s Index of Learning Styles Questionnaire (ILSQ). Lastly, this thesis will also discuss ways to use the results from the user testing to implement a personalized feedback system for the virtual world in the future and what has been learned through the learning style method.
ContributorsKury, Nizar (Author) / Nelson, Brian C (Thesis advisor) / Hsiao, Ihan (Committee member) / Kobayashi, Yoshihiro (Committee member) / Arizona State University (Publisher)
Created2017
Description
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for decades in political science and communications research. Media framing offers an “interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
ContributorsAlashri, Saud (Author) / Davulcu, Hasan (Thesis advisor) / Desouza, Kevin C. (Committee member) / Maciejewski, Ross (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2018
Description
The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation.
ContributorsSubbiah, Naveen Kumar (Author) / Patel, Vimla L. (Thesis advisor) / Hsiao, Sharon (Thesis advisor) / Sen, Ayan (Committee member) / Atkinson, Robert K (Committee member) / Arizona State University (Publisher)
Created2018
Description
This research start utilizing an efficient sparse inverse covariance matrix (precision matrix) estimation technique to identify a set of highly correlated discriminative perspectives between radical and counter-radical groups. A ranking system has been developed that utilizes ranked perspectives to map Islamic organizations on a set of socio-cultural, political and behavioral scales based on their web site corpus. Simultaneously, a gold standard ranking of these organizations was created through domain experts and compute expert-to-expert agreements and present experimental results comparing the performance of the QUIC based scaling system to another baseline method for organizations. The QUIC based algorithm not only outperforms the baseline methods, but it is also the only system that consistently performs at area expert-level accuracies for all scales. Also, a multi-scale ideological model has been developed and it investigates the correlates of Islamic extremism in Indonesia, Nigeria and UK. This analysis demonstrate that violence does not correlate strongly with broad Muslim theological or sectarian orientations; it shows that religious diversity intolerance is the only consistent and statistically significant ideological correlate of Islamic extremism in these countries, alongside desire for political change in UK and Indonesia, and social change in Nigeria. Next, dynamic issues and communities tracking system based on NMF(Non-negative Matrix Factorization) co-clustering algorithm has been built to better understand the dynamics of virtual communities. The system used between Iran and Saudi Arabia to build and apply a multi-party agent-based model that can demonstrate the role of wedges and spoilers in a complex environment where coalitions are dynamic. Lastly, a visual intelligence platform for tracking the diffusion of online social movements has been developed called LookingGlass to track the geographical footprint, shifting positions and flows of individuals, topics and perspectives between groups. The algorithm utilize large amounts of text collected from a wide variety of organizations’ media outlets to discover their hotly debated topics, and their discriminative perspectives voiced by opposing camps organized into multiple scales. Discriminating perspectives is utilized to classify and map individual Tweeter’s message content to social movements based on the perspectives expressed in their tweets.
ContributorsKim, Nyunsu (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Hsiao, Sharon (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
An old proverb claims that “two heads are better than one”. Crowdsourcing research and practice have taken this to heart, attempting to show that thousands of heads can be even better. This is not limited to leveraging a crowd’s knowledge, but also their creativity—the ability to generate something not only useful, but also novel. In practice, there are initiatives such as Free and Open Source Software communities developing innovative software. In research, the field of crowdsourced creativity, which attempts to design scalable support mechanisms, is blooming. However, both contexts still present many opportunities for advancement.
In this dissertation, I seek to advance both the knowledge of limitations in current technologies used in practice as well as the mechanisms that can be used for large-scale support. The overall research question I explore is: “How can we support large-scale creative collaboration in distributed online communities?” I first advance existing support techniques by evaluating the impact of active support in brainstorming performance. Furthermore, I leverage existing theoretical models of individual idea generation as well as recommender system techniques to design CrowdMuse, a novel adaptive large-scale idea generation system. CrowdMuse models users in order to adapt itself to each individual. I evaluate the system’s efficacy through two large-scale studies. I also advance knowledge of current large-scale practices by examining common communication channels under the lens of Creativity Support Tools, yielding a list of creativity bottlenecks brought about by the affordances of these channels. Finally, I connect both ends of this dissertation by deploying CrowdMuse in an Open Source online community for two weeks. I evaluate their usage of the system as well as its perceived benefits and issues compared to traditional communication tools.
This dissertation makes the following contributions to the field of large-scale creativity: 1) the design and evaluation of a first-of-its-kind adaptive brainstorming system; 2) the evaluation of the effects of active inspirations compared to simple idea exposure; 3) the development and application of a set of creativity support design heuristics to uncover creativity bottlenecks; and 4) an exploration of large-scale brainstorming systems’ usefulness to online communities.
In this dissertation, I seek to advance both the knowledge of limitations in current technologies used in practice as well as the mechanisms that can be used for large-scale support. The overall research question I explore is: “How can we support large-scale creative collaboration in distributed online communities?” I first advance existing support techniques by evaluating the impact of active support in brainstorming performance. Furthermore, I leverage existing theoretical models of individual idea generation as well as recommender system techniques to design CrowdMuse, a novel adaptive large-scale idea generation system. CrowdMuse models users in order to adapt itself to each individual. I evaluate the system’s efficacy through two large-scale studies. I also advance knowledge of current large-scale practices by examining common communication channels under the lens of Creativity Support Tools, yielding a list of creativity bottlenecks brought about by the affordances of these channels. Finally, I connect both ends of this dissertation by deploying CrowdMuse in an Open Source online community for two weeks. I evaluate their usage of the system as well as its perceived benefits and issues compared to traditional communication tools.
This dissertation makes the following contributions to the field of large-scale creativity: 1) the design and evaluation of a first-of-its-kind adaptive brainstorming system; 2) the evaluation of the effects of active inspirations compared to simple idea exposure; 3) the development and application of a set of creativity support design heuristics to uncover creativity bottlenecks; and 4) an exploration of large-scale brainstorming systems’ usefulness to online communities.
Contributorsda Silva Girotto, Victor Augusto (Author) / Walker, Erin A (Thesis advisor) / Burleson, Winslow (Thesis advisor) / Maciejewski, Ross (Committee member) / Hsiao, Sharon (Committee member) / Bigham, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2019
Description
Visual processing in social media platforms is a key step in gathering and understanding information in the era of Internet and big data. Online data is rich in content, but its processing faces many challenges including: varying scales for objects of interest, unreliable and/or missing labels, the inadequacy of single modal data and difficulty in analyzing high dimensional data. Towards facilitating the processing and understanding of online data, this dissertation primarily focuses on three challenges that I feel are of great practical importance: handling scale differences in computer vision tasks, such as facial component detection and face retrieval, developing efficient classifiers using partially labeled data and noisy data, and employing multi-modal models and feature selection to improve multi-view data analysis. For the first challenge, I propose a scale-insensitive algorithm to expedite and accurately detect facial landmarks. For the second challenge, I propose two algorithms that can be used to learn from partially labeled data and noisy data respectively. For the third challenge, I propose a new framework that incorporates feature selection modules into LDA models.
ContributorsZhou, Xu (Author) / Li, Baoxin (Thesis advisor) / Hsiao, Sharon (Committee member) / Davulcu, Hasan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
The original version of Helix, the one I pitched when first deciding to make a video game
for my thesis, is an action-platformer, with the intent of metroidvania-style progression
and an interconnected world map.
The current version of Helix is a turn based role-playing game, with the intent of roguelike
gameplay and a dark fantasy theme. We will first be exploring the challenges that came
with programming my own game - not quite from scratch, but also without a prebuilt
engine - then transition into game design and how Helix has evolved from its original form
to what we see today.
for my thesis, is an action-platformer, with the intent of metroidvania-style progression
and an interconnected world map.
The current version of Helix is a turn based role-playing game, with the intent of roguelike
gameplay and a dark fantasy theme. We will first be exploring the challenges that came
with programming my own game - not quite from scratch, but also without a prebuilt
engine - then transition into game design and how Helix has evolved from its original form
to what we see today.
ContributorsDiscipulo, Isaiah K (Author) / Meuth, Ryan (Thesis director) / Kobayashi, Yoshihiro (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The objective of this project concentrates on the game Defense of the Ancients 2 (Dota 2). In this game, players are constantly striving to improve their skills, which are fueled by the competitive nature of the game. The design influences the community to engage in this interaction as they play the game cooperatively. This thesis illustrates the importance of player interaction in influencing design as well as how imperative design is in affecting player interaction. These two concepts are not separate, but are deeply entwined. Every action performed within a game has to interact with some element of design. Both determine how games become defined as competitive, casual, or creative. Game designers can benefit from this study as it reinforces the basics of developing a game for players to interact with. However, it is impossible to predict exactly how players will react to a designed element. Designers should remember to tailor the game towards their audience, but also react and change the game depending on how players are using the elements of design. In addition, players should continue to push the boundaries of games to help designers adapt their product to their audience. If there is not constant communication between players and designers, games will not be tailored appropriately. Pushing the limits of a game benefits the players as well as the designers to make a more complete game. Designers do not solely create a game for the players. Rather, players design the game for themselves. Keywords: game design, player interaction, affinity space, emergent behavior, Dota 2
ContributorsLarsen, Austin James (Author) / Gee, James Paul (Thesis director) / Holmes, Jeffrey (Committee member) / Kobayashi, Yoshihiro (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description
This project is a Game Engine for 2D Fighting Games which uses Simple DirectMedia Layer and C++. The Game Engine's goal is to model the conventions the genre has for dynamically handling combat between two characters. The characters can be in a variety of different states that animate certain features while also responding to the environment based on key statuses. There is a playable test game that is the subject of a user study. The Game Engine's capabilities are shown by the test game and the limitations / missing features are discussed.
ContributorsStanton, Nicholas Scott (Author) / Kobayashi, Yoshihiro (Thesis director) / Hansford, Dianne (Committee member) / Computer Science and Engineering Program (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper details the process for designing both a simulation of the board game Jaipur, and an artificial intelligence (AI) agent that can play the game against a human player. When designing an AI for a card game, there are two major problems that can arise. The first is the difficulty of using a search space to analyze every possible set of future moves. Due to the randomized nature of the deck of cards, the search space rapidly leads to an exponentially growing set of potential game states to analyze when one tries to look more than one turn ahead. The second aspect that poses difficulty is the element of uncertainty that exists from opponent feedback. Certain moves are weak to specific opponent reactions, and these are difficult to predict due to hidden information. To circumvent these problems, the AI uses a greedy approach to decision making, attempting to maximize the value of its plays immediately, and not play for future turns. The agent utilizes conditional statements to evaluate the game state and choose a game action that it deems optimal, a heuristic to place an expected value (EV) of the goods it can choose from, and selects the best one based on this evaluation. Initial implementation of the simulation was done using C++ through a terminal application, and then was translated to a graphical interface using Unity and C#.
ContributorsOrr, James Christopher (Author) / Kobayashi, Yoshihiro (Thesis director) / Selgrad, Justin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05