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- All Subjects: Computer Science
Description
Education of any skill based subject, such as mathematics or language, involves a significant amount of repetition and pratice. According to the National Survey of Student Engagements, students spend on average 17 hours per week reviewing and practicing material previously learned in a classroom, with higher performing students showing a tendency to spend more time practicing. As such, learning software has emerged in the past several decades focusing on providing a wide range of examples, practice problems, and situations for users to exercise their skills. Notably, math students have benefited from software that procedurally generates a virtually infinite number of practice problems and their corresponding solutions. This allows for instantaneous feedback and automatic generation of tests and quizzes. Of course, this is only possible because software is capable of generating and verifying a virtually endless supply of sample problems across a wide range of topics within mathematics. While English learning software has progressed in a similar manner, it faces a series of hurdles distinctly different from those of mathematics. In particular, there is a wide range of exception cases present in English grammar. Some words have unique spellings for their plural forms, some words have identical spelling for plural forms, and some words are conjugated differently for only one particular tense or person-of-speech. These issues combined make the problem of generating grammatically correct sentences complicated. To compound to this problem, the grammar rules in English are vast, and often depend on the context in which they are used. Verb-tense agreement (e.g. "I eat" vs "he eats"), and conjugation of irregular verbs (e.g. swim -> swam) are common examples. This thesis presents an algorithm designed to randomly generate a virtually infinite number of practice problems for students of English as a second language. This approach differs from other generation approaches by generating based on a context set by educators, so that problems can be generated in the context of what students are currently learning. The algorithm is validated through a study in which over 35 000 sentences generated by the algorithm are verified by multiple grammar checking algorithms, and a subset of the sentences are validated against 3 education standards by a subject matter expert in the field. The study found that this approach has a significantly reduced grammar error ratio compared to other generation algorithms, and shows potential where context specification is concerned.
ContributorsMoore, Zachary Christian (Author) / Amresh, Ashish (Thesis director) / Nelson, Brian (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This thesis explores the likely impacts of climate change on agricultural production globally and in the state of Arizona, and on agricultural supply chains. It shows increases in severe weather, including hotter temperatures and droughts, will have a negative impact on crop production in the state and on global agricultural supply chains. It also shows the effects on the environment caused by our current cradle-to-grave supply chains. As a partial remedy, this thesis explores the benefits of vertical farming systems and shows how they could be of value to the residents of Arizona.
ContributorsKing, Emily Marie (Author) / Kirby, Andrew (Thesis director) / Carter, Craig (Committee member) / Department of Supply Chain Management (Contributor) / School of Sustainability (Contributor) / School of International Letters and Cultures (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
One of the core components of many video games is their artificial intelligence. Through AI, a game can tell stories, generate challenges, and create encounters for the player to overcome. Even though AI has continued to advance through the implementation of neural networks and machine learning, game AI tends to implement a series of states or decisions instead to give the illusion of intelligence. Despite this limitation, games can still generate a wide range of experiences for the player. The Hybrid Game AI Framework is an AI system that combines the benefits of two commonly used approaches to developing game AI: Behavior Trees and Finite State Machines. Developed in the Unity Game Engine and the C# programming language, this AI Framework represents the research that went into studying modern approaches to game AI and my own attempt at implementing the techniques learned. Object-oriented programming concepts such as inheritance, abstraction, and low coupling are utilized with the intent to create game AI that's easy to implement and expand upon. The final goal was to create a flexible yet structured AI data structure while also minimizing drawbacks by combining Behavior Trees and Finite State Machines.
ContributorsRamirez Cordero, Erick Alberto (Author) / Kobayashi, Yoshihiro (Thesis director) / Nelson, Brian (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis investigates students' learning behaviors through their interaction with an educational technology, Web Programming Grading Assistant. The technology was developed to facilitate the grading of paper-based examinations in large lecture-based classrooms and to provide richer and more meaningful feedback to students. A classroom study was designed and data was gathered from an undergraduate computer-programming course in the fall of 2016. Analysis of the data revealed that there was a negative correlation between time lag of first review attempt and performance. A survey was developed and disseminated that gave insight into how students felt about the technology and what they normally do to study for programming exams. In conclusion, the knowledge gained in this study aids in the quest to better educate students in computer programming in large in-person classrooms.
ContributorsMurphy, Hannah (Author) / Hsiao, Ihan (Thesis director) / Nelson, Brian (Committee member) / School of Computing, Informatics, and Decision Systems Engineering (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This research lays down foundational work in the semantic reconstruction of linguistic politeness in English-to-Japanese machine translation and thereby advances semantic-based automated translation of English into other natural languages. I developed a Java project called the PoliteParser that is intended as a plug-in to existing semantic parsers to determine whether verbs in dialogue in an English corpus should be conjugated into the plain or the polite honorific form when translated into Japanese. The PoliteParser bases this decision off of semantic information about the social relationships between the speaker and the listener, the speaker's personality, and the circumstances of the utterance. Testing undergone during the course of this research demonstrates that the PoliteParser can achieve levels of accuracy 31 percentage points higher than that of statistical translation systems when integrated with a semantic parser and 54 percentage points higher when used with pre-parsed data.
ContributorsGuiou, Jared Tyler (Author) / Baral, Chitta (Thesis director) / Tanno, Koji (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Can a skill taught in a virtual environment be utilized in the physical world? This idea is explored by creating a Virtual Reality game for the HTC Vive to teach users how to play the drums. The game focuses on developing the user's muscle memory, improving the user's ability to play music as they hear it in their head, and refining the user's sense of rhythm. Several different features were included to achieve this such as a score, different levels, a demo feature, and a metronome. The game was tested for its ability to teach and for its overall enjoyability by using a small sample group. Most participants of the sample group noted that they felt as if their sense of rhythm and drumming skill level would improve by playing the game. Through the findings of this project, it can be concluded that while it should not be considered as a complete replacement for traditional instruction, a virtual environment can be successfully used as a learning aid and practicing tool.
ContributorsDinapoli, Allison (Co-author) / Tuznik, Richard (Co-author) / Kobayashi, Yoshihiro (Thesis director) / Nelson, Brian (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Current farming demographics in the United States indicate an aging and overwhelmingly white group of farmers, stimulating the need for engaging a younger and more diverse population. There is an opportunity to engage these populations through farm-based internship and apprenticeship programs, which are immersive programs on small-scale, sustainable farms. These programs are unique in providing hands-on training, housing, meals, and a stipend in return for labor, presenting a pathway to social empowerment. The potential outcomes of increasing diversity and inclusion in farm programs are absent from the research on the benefits of diversity and inclusion in other work environments, such as the corporate setting. This paper presents the results of a study aimed at determining levels of diversity and inclusion in United States farm-based internship programs, and the viability of these programs as an effective opportunity to engage marginalized young people in farming. The study of 13 farm owners and managers across the U.S. found that the participants are focused on fostering education and training, environmental benefits, and a sense of community in their respective programs. All participants either want to establish, or believe they currently have, an inclusive workplace on their farm, but also indicated a barrier to inclusivity in the lack of a diverse applicant pool. Future recommendations for removing that barrier and involving more young, diverse interns include increased outreach and access to these programs, the use of inclusive language, and further research.
ContributorsLascola, Dania (Co-author) / Biel, Braden (Co-author) / Cloutier, Scott (Thesis director) / MacFadyen, Joshua (Committee member) / School of International Letters and Cultures (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming domain. They may choose to utilize these opportunities to self-assess their learning progress and practice their skill. My objective in this thesis is to understand to what extent self-assess process can impact novice programmers learning and what advanced learning technologies can I provide to enhance the learner’s outcome and the progress. In this dissertation, I conducted a series of studies to investigate learning analytics and students’ behaviors in working on self-assessments and reflection opportunities. To enable this objective, I designed a personalized learning platform named QuizIT that provides daily quizzes to support learners in the computer science domain. QuizIT adopts an Open Social Student Model (OSSM) that supports personalized learning and serves as a self-assessment system. It aims to ignite self-regulating behavior and engage students in the self-assessment and reflective procedure. I designed and integrated the personalized practice recommender to the platform to investigate the self-assessment process. I also evaluated the self-assessment behavioral trails as a predictor to the students’ performance. The statistical indicators suggested that the distributed reflections were associated with the learner's performance. I proceeded to address whether distributed reflections enable self-regulating behavior and lead to better learning in CS introductory courses. From the student interactions with the system, I found distinct behavioral patterns that showed early signs of the learners' performance trajectory. The utilization of the personalized recommender improved the student’s engagement and performance in the self-assessment procedure. When I focused on enhancing reflections impact during self-assessment sessions through weekly opportunities, the learners in the CS domain showed better self-regulating learning behavior when utilizing those opportunities. The weekly reflections provided by the learners were able to capture more reflective features than the daily opportunities. Overall, this dissertation demonstrates the effectiveness of the learning technologies, including adaptive recommender and reflection, to support novice programming learners and their self-assessing processes.
ContributorsAlzaid, Mohammed (Author) / Hsiao, Ihan (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / VanLehn, Kurt (Committee member) / Nelson, Brian (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2022
Description
American Sign Language (ASL) is used for Deaf and Hard of Hearing (DHH) individuals to communicate and learn in a classroom setting. In ASL, fingerspelling and gestures are two primary components used for communication. Fingerspelling is commonly used for words that do not have a specifically designated sign or gesture. In technical contexts, such as Computer Science curriculum, there are many technical terms that fall under this category. Most of its jargon does not have standardized ASL gestures; therefore, students, educators, and interpreters alike have been reliant on fingerspelling, which poses challenges for all parties. This study investigates the efficacy of both fingerspelling and gestures with fifteen technical terms that do have standardized gestures. The terms’ fingerspelling and gesture are assessed based on preference, ease of use, ease of learning, and time by research subjects who were selected as DHH individuals familiar with ASL.
The data is collected in a series of video recordings by research subjects as well as a post-participation questionnaire. Each research subject has produced thirty total videos, two videos to fingerspell and gesture each technical term. Afterwards, they completed a post-participation questionnaire in which they indicated their preference and how easy it was to learn and use both fingerspelling and gestures. Additionally, the videos have been analyzed to determine the time difference between fingerspelling and gestures. Analysis reveals that gestures are favored over fingerspelling as they are generally preferred, considered easier to learn and use, and faster. These results underscore the significance for standardized gestures in the Computer Science curriculum for accessible learning that enhances communication and promotes inclusion.
ContributorsKarim, Bushra (Author) / Gupta, Sandeep (Thesis director) / Hossain, Sameena (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05