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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

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
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Description
ASU’s Software Engineering (SER) program adequately prepares students for what happens after they become a developer, but there is no standard for preparing students to secure a job post-graduation in the first place. This project creates and executes a supplemental curriculum to prepare students for the technical interview process. The

ASU’s Software Engineering (SER) program adequately prepares students for what happens after they become a developer, but there is no standard for preparing students to secure a job post-graduation in the first place. This project creates and executes a supplemental curriculum to prepare students for the technical interview process. The trial run of the curriculum was received positively by study participants, who experienced an increase in confidence over the duration of the workshop.
ContributorsSchmidt, Julia J (Author) / Roscoe, Rod (Thesis director) / Bansal, Srividya (Committee member) / Software Engineering (Contributor) / Human Systems Engineering (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

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
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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

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
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Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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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

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
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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

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

In this thesis, several different methods for detecting and removing satellite streaks from astronomic images were evaluated and compared with a new machine learning based approach. Simulated data was generated with a variety of conditions, and the performance of each method was evaluated both quantitatively, using Mean Absolute Error (MAE)

In this thesis, several different methods for detecting and removing satellite streaks from astronomic images were evaluated and compared with a new machine learning based approach. Simulated data was generated with a variety of conditions, and the performance of each method was evaluated both quantitatively, using Mean Absolute Error (MAE) against a ground truth detection mask and processing throughput of the method, as well as qualitatively, examining the situations in which each model performs well and poorly. Detection methods from existing systems Pyradon and ASTRiDE were implemented and tested. A machine learning (ML) image segmentation model was trained on simulated data and used to detect streaks in test data. The ML model performed favorably relative to the traditional methods tested, and demonstrated superior robustness in general. However, the model also exhibited some unpredictable behavior in certain scenarios which should be considered. This demonstrated that machine learning is a viable tool for the detection of satellite streaks in astronomic images, however special care must be taken to prevent and to minimize the effects of unpredictable behavior in such models.

ContributorsJeffries, Charles (Author) / Acuna, Ruben (Thesis director) / Martin, Thomas (Committee member) / Bansal, Ajay (Committee member) / Barrett, The Honors College (Contributor) / Software Engineering (Contributor)
Created2023-05
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Description
The American Heart Association recommended in 1997 the data elements that should be collected from resuscitations in hospitals. (15) Currently, data documentation from resuscitation events in hospitals, termed ‘code blue’ events, utilizes a paper form, which is institution-specific. Problems with data capture and transcription exists, due to the challenges of

The American Heart Association recommended in 1997 the data elements that should be collected from resuscitations in hospitals. (15) Currently, data documentation from resuscitation events in hospitals, termed ‘code blue’ events, utilizes a paper form, which is institution-specific. Problems with data capture and transcription exists, due to the challenges of dynamic documentation of patient, event and outcome variables as the code blue event unfolds.

This thesis is based on the hypothesis that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. The primary goal of this thesis is to create an iOS based application, primarily designed for iPads, for code blue events at the Mayo Clinic Hospital. The secondary goal is to build an open-source software development framework for converting paper-based hospital protocols into digital format.

The tool created in this study enabled data documentation to be completed electronically rather than on paper for resuscitation outcomes. The tool was evaluated for usability with twenty nurses, the end-users, at Mayo Clinic in Phoenix, Arizona. The results showed the preference of users for the iPad application. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure.
ContributorsBokhari, Wasif (Author) / Patel, Vimla L. (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Nelson, Brian (Committee member) / Sen, Ayan (Committee member) / Arizona State University (Publisher)
Created2015
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Description
For this master's thesis, an open learner model is integrated with Quinn, a teachable robotic agent developed at Arizona State University. This system is represented as a feedback system, which aims to improve a student’s understanding of a subject. It also helps to understand the effect of the learner model

For this master's thesis, an open learner model is integrated with Quinn, a teachable robotic agent developed at Arizona State University. This system is represented as a feedback system, which aims to improve a student’s understanding of a subject. It also helps to understand the effect of the learner model when it is represented by performance of the teachable agent. The feedback system represents performance of the teachable agent, and not of a student. Data in the feedback system is thus updated according to a student's understanding of the subject. This provides students an opportunity to enhance their understanding of a subject by analyzing their performance. To test the effectiveness of the feedback system, student understanding in two different conditions is analyzed. In the first condition a feedback report is not provided to the students, while in the second condition the feedback report is provided in the form of the agent’s performance.
ContributorsUpadhyay, Abha (Author) / Walker, Erin (Thesis advisor) / Nelson, Brian (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016