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- Title
- A STUDY OF STUDENT ACHIEVEMENT IN FLORIDA HIGH SCHOOLS RECEIVING DEPARTMENT OF EDUCATION SMALLER LEARNING COMMUNITY GRANTS: 2006-2009.
- Creator
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Armbruster, Michael, Taylor, Rosemarye, University of Central Florida
- Abstract / Description
-
The focus of this research was to analyze the impact of the United States Department of EducationÂÂÂÂ's Smaller Learning Communities (SLC) Grant Program on student achievement in 17 schools in the state of Florida that were issued three-year grants during the school years 2006-2009 as compared to 17 similar schools in the state of Florida that did not receive grant funding. Base-line data for...
Show moreThe focus of this research was to analyze the impact of the United States Department of EducationÂÂÂÂ's Smaller Learning Communities (SLC) Grant Program on student achievement in 17 schools in the state of Florida that were issued three-year grants during the school years 2006-2009 as compared to 17 similar schools in the state of Florida that did not receive grant funding. Base-line data for each of the 34 schools consisted of student performance in 2006, one year prior to SLC schools receiving the grant. Student achievement data from the base-line through the three-year grant period for the 17 grant recipients were compared with that of 17 similar Florida schools that were not grant recipients in 2006. Student data were collected from the Florida Department of Education. The data subjected to analyses were comprised of student achievement on the ninth and tenth grade Florida Comprehensive Assessment Test (FCAT) in the areas of reading and mathematics, the graduation rate, and the dropout rate. The data showed an overall improvement in the SLC schoolsÂÂÂÂ' student achievement based on the six areas analyzed. The data collected were then compared to the 17 similar schools to identify any significant differences in the achievement gains in those schools. Although both the SLC schools and the control schools showed overall improvement, no statistically significant relationship was discovered in the achievement of students in SLC schools versus students in similar schools that did not receive the grant dollars during the defined time periods. The overall trend for all 34 schools was similar improvement in student achievement.
Show less - Date Issued
- 2010
- Identifier
- CFE0003465, ucf:48936
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003465
- Title
- A CONTEXTUAL APPROACH TO LEARNING COLLABORATIVE BEHAVIOR VIA OBSERVATION.
- Creator
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Johnson, Cynthia, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
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This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate...
Show moreThis dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other software agents and creating a behavior function for a single agent. The research described here combines current research in teamwork simulations and learning by observation to effectively train a multi-agent system in effective team behavior. The dissertation describes the background and work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described.
Show less - Date Issued
- 2011
- Identifier
- CFE0003602, ucf:48869
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003602
- Title
- LEARNING FOR THE NEXT GENERATION: PREDICTING THE USAGE OF SYNTHETIC LEARNING ENVIRONMENTS.
- Creator
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Evans, Arthur, Jentsch, Florian, University of Central Florida
- Abstract / Description
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The push to further the use of technology in learning has broadened the attempts of many to find innovated ways to aid the new, technologically savvy generation of learners, in acquiring the knowledge needed for their education and training. A critical component to the success of these initiatives is the proper application of the science of learning (Cannon-Bowers and Bowers, 2009). One technological initiative that can benefit from this application is the use of synthetic learning...
Show moreThe push to further the use of technology in learning has broadened the attempts of many to find innovated ways to aid the new, technologically savvy generation of learners, in acquiring the knowledge needed for their education and training. A critical component to the success of these initiatives is the proper application of the science of learning (Cannon-Bowers and Bowers, 2009). One technological initiative that can benefit from this application is the use of synthetic learning environments (SLEs). SLEs are instructional systems embedded within virtual worlds. These worlds can be simulations of some task, for instance a simulation that may be completed as part of a military training to mimic specific situations, or they could be in the form of a video game, for example, a game designed to maintain the attention of school children while teaching mathematics. The important components to SLEs are a connection to the underlying task being trained and a set of goals for which to strive toward. SLEs have many unique characteristics which separate them from other forms of education. Two of the most salient characteristics are the instructorless nature of SLEs (most of the learning from SLEs happens without instructor interaction) and the fact that in many cases SLEs are actually fun and engaging, thus motivating the learner to participate more and allowing them to experience a more immersive interaction. Incorporating the latter of these characteristics into a model originally introduced by Davis (1989) and adapted by Yi and Hwang (2003) for use with web applications, an expanded model to predict the effects of enjoyment, goal orientation, ease of use, and several other factors on the overall use of SLEs has been created. Adapting the Davis and Yi and Hwang models for the specific use of SLEs provides a basis understanding how each of the critical input variables effect the use and thus effectiveness of learning tools based on SLEs. In particular, performance goal orientation has been added to the existing models to more accurately reflect the performance characteristics present in games. Results of this study have shown that, in fact, performance goal orientation is a significant factor in the SLE Use and Learning model. However, within the model it is important to distinguish that the two varieties of performance goal orientation (prove and avoid) play different roles. Prove performance goal orientation has been shown to have significant relationships with several other critical factors while avoid performance goal orientation is only accounted for in its significant correlation with prove performance goal orientation. With this understanding, training developers can now have a better understanding of where their resources should be spent to promote more efficient and effective learning. The results of this study allow developers to move forward with confidence in the fact that their new learning environments will be effective in a number of realms, not only limited to classroom, business, or military training.
Show less - Date Issued
- 2010
- Identifier
- CFE0003060, ucf:48298
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003060
- Title
- Machine Learning from Casual Conversation.
- Creator
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Mohammed Ali, Awrad, Sukthankar, Gita, Wu, Annie, Boloni, Ladislau, University of Central Florida
- Abstract / Description
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Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base,...
Show moreHuman social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human's conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research that is focused on teaching specific tasks to computer agents. Furthermore, LCC aims to provide an easy way to enhance the knowledge of the system without requiring the involvement of a programmer. This system does not require the user to enter specific information; instead, the user can chat naturally with the agent. LCC identifies the inputs that contain information relevant to its knowledge base in the learning process. LCC's architecture consists of multiple sub-systems combined to perform the task. Its learning component can add new knowledge to existing information in the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. %The test results indicate that the prototype was successful in learning from a conversation. The LCC system functionality was assessed using different evaluation methods. This includes tests performed by the developer, as well as by 130 human test subjects. Thirty of those test subjects interacted directly with the system and completed a survey of 13 questions/statements that asked the user about his/her experience using LCC. A second group of 100 human test subjects evaluated the dialogue logs of a subset of the first group of human testers. The collected results were all found to be acceptable and within the range of our expectations.
Show less - Date Issued
- 2019
- Identifier
- CFE0007503, ucf:52634
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007503
- Title
- Content-based Information Retrieval via Nearest Neighbor Search.
- Creator
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Huang, Yinjie, Georgiopoulos, Michael, Anagnostopoulos, Georgios, Hu, Haiyan, Sukthankar, Gita, Ni, Liqiang, University of Central Florida
- Abstract / Description
-
Content-based information retrieval (CBIR) has attracted significant interest in the past few years. When given a search query, the search engine will compare the query with all the stored information in the database through nearest neighbor search. Finally, the system will return the most similar items. We contribute to the CBIR research the following: firstly, Distance Metric Learning (DML) is studied to improve retrieval accuracy of nearest neighbor search. Additionally, Hash Function...
Show moreContent-based information retrieval (CBIR) has attracted significant interest in the past few years. When given a search query, the search engine will compare the query with all the stored information in the database through nearest neighbor search. Finally, the system will return the most similar items. We contribute to the CBIR research the following: firstly, Distance Metric Learning (DML) is studied to improve retrieval accuracy of nearest neighbor search. Additionally, Hash Function Learning (HFL) is considered to accelerate the retrieval process.On one hand, a new local metric learning framework is proposed - Reduced-Rank Local Metric Learning (R2LML). By considering a conical combination of Mahalanobis metrics, the proposed method is able to better capture information like data's similarity and location. A regularization to suppress the noise and avoid over-fitting is also incorporated into the formulation. Based on the different methods to infer the weights for the local metric, we considered two frameworks: Transductive Reduced-Rank Local Metric Learning (T-R2LML), which utilizes transductive learning, while Efficient Reduced-Rank Local Metric Learning (E-R2LML)employs a simpler and faster approximated method. Besides, we study the convergence property of the proposed block coordinate descent algorithms for both our frameworks. The extensive experiments show the superiority of our approaches.On the other hand, *Supervised Hash Learning (*SHL), which could be used in supervised, semi-supervised and unsupervised learning scenarios, was proposed in the dissertation. By considering several codewords which could be learned from the data, the proposed method naturally derives to several Support Vector Machine (SVM) problems. After providing an efficient training algorithm, we also study the theoretical generalization bound of the new hashing framework. In the final experiments, *SHL outperforms many other popular hash function learning methods. Additionally, in order to cope with large data sets, we also conducted experiments running on big data using a parallel computing software package, namely LIBSKYLARK.
Show less - Date Issued
- 2016
- Identifier
- CFE0006327, ucf:51544
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006327
- Title
- Faculty Professional Development for Improving Hybrid Course Success.
- Creator
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Lawhon, Jennifer, Hopp, Carolyn, Vitale, Thomas, Hines, Rebecca, Phelps, Julie, University of Central Florida
- Abstract / Description
-
The purpose of this Dissertation in Practice was to investigate the inconsistent success rates in hybrid courses at a Florida college. Results from a pilot study and faculty survey revealed a need for a training program specific to hybrid instructors. The researchers created a training program composed of a framework and a professional development course, designed to promote consistency in how instructors create and implement their hybrid courses. The framework consists of six research-based...
Show moreThe purpose of this Dissertation in Practice was to investigate the inconsistent success rates in hybrid courses at a Florida college. Results from a pilot study and faculty survey revealed a need for a training program specific to hybrid instructors. The researchers created a training program composed of a framework and a professional development course, designed to promote consistency in how instructors create and implement their hybrid courses. The framework consists of six research-based standards which aided in the creation of six learning modules for the professional development course. These modules were: course alignment, face-to-face active learning, online resources, formative feedback, assessment guidelines, and course structure. A focus group of faculty members who have taught hybrid courses at the college was used to review the course and framework to assess whether any modifications are required. The focus group discussion revealed that all six elements of the framework are essential to the success of a hybrid course design. The focus group also suggested changes and revisions to the professional development course which should be addressed prior to rolling out the course college-wide.
Show less - Date Issued
- 2017
- Identifier
- CFE0006757, ucf:51861
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006757
- Title
- Faculty Professional Development for Improving Hybrid Course Success.
- Creator
-
Saxman, Amanda, Hopp, Carolyn, Vitale, Thomas, Hines, Rebecca, Phelps, Julie, University of Central Florida
- Abstract / Description
-
The purpose of this Dissertation in Practice was to investigate the inconsistent success rates in hybrid courses at a Florida college. Results from a pilot study and faculty survey revealed a need for a training program specific to hybrid instructors. The researchers created a training program composed of a framework and a professional development course, designed to promote consistency in how instructors create and implement their hybrid courses. The framework consists of six research-based...
Show moreThe purpose of this Dissertation in Practice was to investigate the inconsistent success rates in hybrid courses at a Florida college. Results from a pilot study and faculty survey revealed a need for a training program specific to hybrid instructors. The researchers created a training program composed of a framework and a professional development course, designed to promote consistency in how instructors create and implement their hybrid courses. The framework consists of six research-based standards which aided in the creation of six learning modules for the professional development course. These modules were: course alignment, face-to-face active learning, online resources, formative feedback, assessment guidelines, and course structure. A focus group of faculty members who have taught hybrid courses at the college was used to review the course and framework to assess whether any modifications are required. The focus group discussion revealed that all six elements of the framework are essential to the success of a hybrid course design. The focus group also suggested changes and revisions to the professional development course which should be addressed prior to rolling out the course college-wide.
Show less - Date Issued
- 2017
- Identifier
- CFE0006791, ucf:51819
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006791
- Title
- On Kernel-base Multi-Task Learning.
- Creator
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Li, Cong, Georgiopoulos, Michael, Anagnostopoulos, Georgios, Tappen, Marshall, Hu, Haiyan, Ni, Liqiang, University of Central Florida
- Abstract / Description
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Multi-Task Learning (MTL) has been an active research area in machine learning for two decades. By training multiple relevant tasks simultaneously with information shared across tasks, it is possible to improve the generalization performance of each task, compared to training each individual task independently. During the past decade, most MTL research has been based on the Regularization-Loss framework due to its flexibility in specifying various types of information sharing strategies, the...
Show moreMulti-Task Learning (MTL) has been an active research area in machine learning for two decades. By training multiple relevant tasks simultaneously with information shared across tasks, it is possible to improve the generalization performance of each task, compared to training each individual task independently. During the past decade, most MTL research has been based on the Regularization-Loss framework due to its flexibility in specifying various types of information sharing strategies, the opportunity it offers to yield a kernel-based methods and its capability in promoting sparse feature representations.However, certain limitations exist in both theoretical and practical aspects of Regularization-Loss-based MTL. Theoretically, previous research on generalization bounds in connection to MTL Hypothesis Space (HS)s, where data of all tasks are pre-processed by a (partially) common operator, has been limited in two aspects: First, all previous works assumed linearity of the operator, therefore completely excluding kernel-based MTL HSs, for which the operator is potentially non-linear. Secondly, all previous works, rather unnecessarily, assumed that all the task weights to be constrained within norm-balls, whose radii are equal. The requirement of equal radii leads to significant inflexibility of the relevant HSs, which may cause the generalization performance of the corresponding MTL models to deteriorate. Practically, various algorithms have been developed for kernel-based MTL models, due to different characteristics of the formulations. Most of these algorithms are a burden to develop and end up being quite sophisticated, so that practitioners may face a hard task in interpreting and implementing them, especially when multiple models are involved. This is even more so, when Multi-Task Multiple Kernel Learning (MT-MKL) models are considered. This research largely resolves the above limitations. Theoretically, a pair of new kernel-based HSs are proposed: one for single-kernel MTL, and another one for MT-MKL. Unlike previous works, we allow each task weight to be constrained within a norm-ball, whose radius is learned during training. By deriving and analyzing the generalization bounds of these two HSs, we show that, indeed, such a flexibility leads to much tighter generalization bounds, which often results to significantly better generalization performance. Based on this observation, a pair of new models is developed, one for each case: single-kernel MTL, and another one for MT-MKL. From a practical perspective, we propose a general MT-MKL framework that covers most of the prominent MT-MKL approaches, including our new MT-MKL formulation. Then, a general purpose algorithm is developed to solve the framework, which can also be employed for training all other models subsumed by this framework. A series of experiments is conducted to assess the merits of the proposed mode when trained by the new algorithm. Certain properties of our HSs and formulations are demonstrated, and the advantage of our model in terms of classification accuracy is shown via these experiments.
Show less - Date Issued
- 2014
- Identifier
- CFE0005517, ucf:50321
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005517
- Title
- PREDICTING ANXIETY FROM PARENT AND CHILDHOOD VARIABLES.
- Creator
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Fisak, Brian, Negy, Charles, University of Central Florida
- Abstract / Description
-
The high prevalence rate, significant distress and impairment, and persistence of childhood anxiety disorders highlight the need for continued theoretical conceptualization and research into the developmental pathways associated these disorders. In response to this need, one goal this project was to examination and identify variables associated with the development and/or maintenance of child anxiety disorders. A second goal of this project was to examine the potential role of learning from...
Show moreThe high prevalence rate, significant distress and impairment, and persistence of childhood anxiety disorders highlight the need for continued theoretical conceptualization and research into the developmental pathways associated these disorders. In response to this need, one goal this project was to examination and identify variables associated with the development and/or maintenance of child anxiety disorders. A second goal of this project was to examine the potential role of learning from parents as a risk factor in the development of child anxiety, with a particular emphasis on three learning mechanisms: modeling, information transfer, and reinforcement of anxious behaviors. The third goal of this project was to compare and contrast the developmental predictors of anxiety in White versus Hispanic samples. Data was collected from a sample of mothers in the community with at least one child between the ages of 6 and 12, and an unrelated sample of young adults. Significant predictors of anxiety were identified in both samples, and the hypothesis that anxiety may, in part, be learned from parents was supported in both samples. In addition, results indicated different sets of predictors of anxiety in White versus Hispanic participants. Limitations and implications of the findings are discussed.
Show less - Date Issued
- 2006
- Identifier
- CFE0001261, ucf:46916
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001261
- Title
- ENHANCING VOCABULARY ACQUISITION THROUGH SYNTHETIC LEARNING EXPERIENCES: IMPLEMENTING VIRTUAL FIELD TRIPS INTO CLASSROOMS.
- Creator
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Sanchez, Alicia, Cannon-Bowers, Jan, University of Central Florida
- Abstract / Description
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A Synthetic Learning Environment (SLE) the Virtual Field Trip (VFT) was designed to increase vocabulary acquisition and knowledge by utilizing simulation based technologies and leveraging sound educational findings. Vocabulary acquisition is considered a prerequisite to becoming a good reader and therefore a critical predictor of academic and lifelong success for early learners, however, teachers report that students lack the real world knowledge required for vocabulary knowledge. The VFT...
Show moreA Synthetic Learning Environment (SLE) the Virtual Field Trip (VFT) was designed to increase vocabulary acquisition and knowledge by utilizing simulation based technologies and leveraging sound educational findings. Vocabulary acquisition is considered a prerequisite to becoming a good reader and therefore a critical predictor of academic and lifelong success for early learners, however, teachers report that students lack the real world knowledge required for vocabulary knowledge. The VFT provides a meaningful context for anchored and situated instruction. Second grade students were assigned to either use the VFT or to listen to stories read aloud by a researcher on a video tape. While results did not indicate significant vocabulary acquisition on a series of 3 vocabulary tests; students who used the VFT did use significantly more words in a post exposure writing sample than students in the story group indicating an increase of words known at a level of depth sufficient to warrant their use in a writing sample. Students who used the VFT also reported increased motivation to use SLEs like the VFT for future learning objectives and that VFTs were fun. Findings related to the self-efficacy of students as measures immediately following each vocabulary test did not reveal a significant increase for VFT users. Students using the VFTs did not report learning more words than those students assigned to the story group. Limitations of the current study and directions for future research are discussed.
Show less - Date Issued
- 2006
- Identifier
- CFE0001419, ucf:47059
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001419
- Title
- A COMPARISON OF COMPUTER AND TRADITIONAL FACE-TO-FACE CLASSROOM ORIENTATION FOR BEGINNING CRITICAL CARE NURSES.
- Creator
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Anzalone, Patricia, Sole, Mary Lou, University of Central Florida
- Abstract / Description
-
Purpose: Education of the novice critical care nurse has traditionally been conducted by critical care educators in face-to-face classes in an orientation or internship. A shortage of qualified educators and growth in electronic modes of course delivery has led organizations to explore electronic learning (e-learning) to provide orientation to critical care nursing concepts. Equivalence of e-learning versus traditional critical care orientation has not been studied. The primary aim of this...
Show morePurpose: Education of the novice critical care nurse has traditionally been conducted by critical care educators in face-to-face classes in an orientation or internship. A shortage of qualified educators and growth in electronic modes of course delivery has led organizations to explore electronic learning (e-learning) to provide orientation to critical care nursing concepts. Equivalence of e-learning versus traditional critical care orientation has not been studied. The primary aim of this study was to examine the equivalency of knowledge attainment in the cardiovascular module of the Essentials of Critical Care Orientation (ECCO) e-learning program to traditional face-to-face critical care orientation classes covering the same content. Additional aims were to determine if learning style is associated with a preference for type of learning method, and to determine any difference in learning satisfaction between the two modalities. Methods: The study was conducted using a two-group pretest-posttest experimental design. Forty-one practicing volunteer nurses with no current critical care experience living in southwest Florida were randomly assigned to either the ECCO (n=19) or face-to-face (n=22) group. Those in the face-to-face group attended 20 hours of classroom instruction taught by an expert educator. Those in the ECCO group completed the lessons on line and had an optional 2 hour face-to-face discussion component. Pre-test measures included the Basic Knowledge Assessment Test (BKAT-7), modified ECCO Cardiovascular (CV) Examination, and Kolb Learning Style Inventory (LSI). Post-tests included the BKAT-7, modified CV Examination, and Affective Measures Survey. Results: The majority of subjects were female, married, and educated at the associate degree level. Their mean age was 39.5 + 12 years, and they averaged 9.9 + 11.7 years of nursing experience. The diverging learning style was assessed in 37% of subjects. Classroom instruction was preferred by 61% of participants. No statistical differences were noted between groups on any demographic variables or baseline knowledge. Learning outcomes were compared by repeated measures analysis of variance. Mean scores of subjects in both groups increased statistically on both the BKAT-7 and modified CV Examination (p=<.01); however, no significant differences (p> .05) were found between groups. Preference for online versus classroom instruction was not associated with learning style (X2 = 3.39, p = .34). Satisfaction with learning modality was significantly greater for those in the classroom group (t=4.25, p=.000). Discussion/Implications: This is the first study to evaluate the ECCO orientation program and contributes to the growing body of knowledge exploring e-learning versus traditional education. The results of this study provide evidence that the ECCO critical care education produces learning outcomes at least equivalent to traditional classroom instruction, regardless of the learning style of the student. As participant satisfaction was more favorable toward the classroom learning modality, consideration should be given to providing blended learning if using computer-based orientation programs. Replication of this study with a variety of instructors in varied geographic locations, expanded populations, larger samples, and different subject matter is recommended.
Show less - Date Issued
- 2008
- Identifier
- CFE0002192, ucf:47888
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002192
- Title
- Task Focused Robotic Imitation Learning.
- Creator
-
Abolghasemi, Pooya, Boloni, Ladislau, Sukthankar, Gita, Shah, Mubarak, Willenberg, Bradley, University of Central Florida
- Abstract / Description
-
For many years, successful applications of robotics were the domain of controlled environments, such as industrial assembly lines. Such environments are custom designed for the convenience of the robot and separated from human operators. In recent years, advances in artificial intelligence, in particular, deep learning and computer vision, allowed researchers to successfully demonstrate robots that operate in unstructured environments and directly interact with humans. One of the major...
Show moreFor many years, successful applications of robotics were the domain of controlled environments, such as industrial assembly lines. Such environments are custom designed for the convenience of the robot and separated from human operators. In recent years, advances in artificial intelligence, in particular, deep learning and computer vision, allowed researchers to successfully demonstrate robots that operate in unstructured environments and directly interact with humans. One of the major applications of such robots is in assistive robotics. For instance, a wheelchair mounted robotic arm can help disabled users in the performance of activities of daily living (ADLs) such as feeding and personal grooming. Early systems relied entirely on the control of the human operator, something that is difficult to accomplish by a user with motor and/or cognitive disabilities. In this dissertation, we are describing research results that advance the field of assistive robotics. The overall goal is to improve the ability of the wheelchair / robotic arm assembly to help the user with the performance of the ADLs by requiring only high-level commands from the user. Let us consider an ADL involving the manipulation of an object in the user's home. This task can be naturally decomposed into two components: the movement of the wheelchair in such a way that the manipulator can conveniently grasp the object and the movement of the manipulator itself. This dissertation we provide an approach for addressing the challenge of finding the position appropriate for the required manipulation. We introduce the ease-of-reach score (ERS), a metric that quantifies the preferences for the positioning of the base while taking into consideration the shape and position of obstacles and clutter in the environment. As the brute force computation of ERS is computationally expensive, we propose a machine learning approach to estimate the ERS based on features and characteristics of the obstacles. This dissertation addresses the second component as well, the ability of the robotic arm to manipulate objects. Recent work in end-to-end learning of robotic manipulation had demonstrated that a deep learning-based controller of vision-enabled robotic arms can be thought to manipulate objects from a moderate number of demonstrations. However, the current state of the art systems are limited in robustness to physical and visual disturbances and do not generalize well to new objects. We describe new techniques based on task-focused attention that show significant improvement in the robustness of manipulation and performance in clutter.
Show less - Date Issued
- 2019
- Identifier
- CFE0007771, ucf:52392
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007771
- Title
- LEARNING-CENTERED PROFESSIONAL STAFF DEVELOPMENT: EXAMINING INSTITUTIONAL AND LEARNER RESPONSIBILITIES.
- Creator
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Corderman, Julie, Witta, Eleanor, University of Central Florida
- Abstract / Description
-
The goal of this research was to determine the balance of a collaborative learning relationship between an institution and its employees. A review of the literature examined learning-centered theory to determine the necessary tenets of a learning-centered professional staff development program. In addition, various staff development components were examined to ascertain their role in a learning-centered program. The literature findings guided this research to conduct a study to determine if...
Show moreThe goal of this research was to determine the balance of a collaborative learning relationship between an institution and its employees. A review of the literature examined learning-centered theory to determine the necessary tenets of a learning-centered professional staff development program. In addition, various staff development components were examined to ascertain their role in a learning-centered program. The literature findings guided this research to conduct a study to determine if relationships existed between employees' perception of climate and two variables: (a) employees' locus of control and (b) employees' job satisfaction. Additionally, the three factors were assessed together in a linear regression to determine what percentage of variance could be accounted for by each of the factors. The extent to which the institution had sufficiently set the stage for learning to take place was determined by assessing the institution's climate utilizing the PACE©. Locus of control and job satisfaction were two audience components utilized to determine appropriate program selection. Findings from the correlation procedures revealed a moderate relationship between both the employees' locus of control and their job satisfaction and their perception of the climate. A multiple regression revealed that 43% of an employee's climate perception could be accounted for by locus of control and job satisfaction. Results of this study indicated that locus of control and job satisfaction were two factors that an institution needs to consider with regards to their staff prior to embarking on a staff development program or in re-designing an existing program. In addition, the results indicated the necessity in establishing a baseline climate perception to ascertain if the environment was conducive to staff learning. Lastly, an institution needs to be willing to inquire of its staff as to their needs and preferred learning delivery methods. By examining itself objectively, and engaging workers in a collaborative learning process, an institution can begin to establish the foundation for a learning centered staff development program.
Show less - Date Issued
- 2008
- Identifier
- CFE0002021, ucf:47624
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002021
- Title
- Active Learning with Unreliable Annotations.
- Creator
-
Zhao, Liyue, Sukthankar, Gita, Tappen, Marshall, Georgiopoulos, Michael, Sukthankar, Rahul, University of Central Florida
- Abstract / Description
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With the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to...
Show moreWith the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to provide a large quantity of labels but controlling the quality of labels is hard. Active learning, which requires experts to annotate a subset of the most informative or uncertain data, is very sensitive to the annotation errors. Though these two techniques can be used independently of one another, by using them in combination they can complement each other's weakness. In this thesis, I investigate the development of active learning Support Vector Machines (SVMs) and expand this model to sequential data. Then I discuss the weakness of combining active learning and crowdsourcing, since the active learning is very sensitive to low quality annotations which are unavoidable for labels collected from crowdsourcing. In this thesis, I propose three possible strategies, incremental relabeling, importance-weighted label prediction and active Bayesian Networks. The incremental relabeling strategy requires workers to devote more annotations to uncertain samples, compared to majority voting which allocates different samples the same number of labels. Importance-weighted label prediction employs an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. An active learning version of Bayesian Networks is used to model the difficulty of samples and the expertise of workers simultaneously to evaluate the relative weight of workers' labels during the active learning process. All three strategies apply different techniques with the same expectation -- identifying the optimal solution for applying an active learning model with mixed label quality to crowdsourced data. However, the active Bayesian Networks model, which is the core element of this thesis, provides additional benefits by estimating the expertise of workers during the training phase. As an example application, I also demonstrate the utility of crowdsourcing for human activity recognition problems.
Show less - Date Issued
- 2013
- Identifier
- CFE0004965, ucf:49579
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004965
- Title
- FALCONET: FORCE-FEEDBACK APPROACH FOR LEARNING FROM COACHING AND OBSERVATION USING NATURAL AND EXPERIENTIAL TRAINING.
- Creator
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Stein, Gary, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
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Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine...
Show moreBuilding an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely observable information to construct an agent that behaves similarly to the observed human. Typically, an observational system builds an agent only based on prerecorded observations. This type of system works well with respect to agent creation, but lacks the ability to be trained and updated on-line. To overcome these deficiencies, the proposed system works by adding an augmented force-feedback system of training that senses the agents intentions haptically. Furthermore, because not all possible situations can be observed or directly trained, a third stage of learning from practice is added for the agent to gain additional knowledge for a particular mission. These stages of learning mimic the natural way a human might learn a task by first watching the task being performed, then being coached to improve, and finally practicing to self improve. The hypothesis is that a system that is initially trained using human recorded data (Observational), then tuned and adjusted using force-feedback (Instructional), and then allowed to perform the task in different situations (Experiential) will be better than any individual step or combination of steps.
Show less - Date Issued
- 2009
- Identifier
- CFE0002746, ucf:48157
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002746
- Title
- EFFECTS OF DEADLINE CONDITIONS ON LEARNERS OF DIFFERENT PROCRASTINATION TENDENCIES IN AN ONLINE COURSE.
- Creator
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Wang, Pin, Gunter, Glenda, University of Central Florida
- Abstract / Description
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The purpose of this study was to investigate the effects of three deadline conditions (i.e., frequent-instructor-set-deadline condition, flexible-instructor-set-deadline condition, and self-imposed-deadline condition) on students of different academic procrastination levels (high, medium, and low) in terms of their perceived learning, academic performance, and course satisfaction in an online course. A 3 x 3 factorial quasi-experimental design was adopted for this study. One hundred and...
Show moreThe purpose of this study was to investigate the effects of three deadline conditions (i.e., frequent-instructor-set-deadline condition, flexible-instructor-set-deadline condition, and self-imposed-deadline condition) on students of different academic procrastination levels (high, medium, and low) in terms of their perceived learning, academic performance, and course satisfaction in an online course. A 3 x 3 factorial quasi-experimental design was adopted for this study. One hundred and seventy three students from three classes of different majors voluntarily participated in the study with 50 students majoring in Agriculture, 61 in International Trading, and 62 in Food Manufacturing. The three classes were randomly assigned to three deadline conditions. Data were collected through an online survey and a final exam. This study found that there were significant differences in perceived learning and course satisfaction among high, medium, and low procrastinators, but there was no significant difference in academic performance among students at different procrastination levels. Low and medium procrastinators had significantly higher perceived learning and were significantly more satisfied with the course than high procrastinators. Among the three deadline condition groups, there were no significant differences in perceived learning and course satisfaction, however, the difference in academic performance was significant. The flexible deadline group achieved the best academic performance followed by the frequent and the self-imposed deadline groups. There was no interaction effect between procrastination and deadline conditions on any of the dependent variables. Limitations of the present study, recommendations for future research, and implications for practice are discussed.
Show less - Date Issued
- 2011
- Identifier
- CFE0003872, ucf:48760
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003872
- Title
- A Comparison of the Verbal Transformation Effect in Normal and Learning Disabled Children.
- Creator
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Kissell, Ellen E., Mullin, Thomas A., Social Sciences
- Abstract / Description
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Florida Technological University College of Social Sciences Thesis
- Date Issued
- 1976
- Identifier
- CFR0008176, ucf:53063
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFR0008176
- Title
- An Examination of Post Implementation Adoption of Business Intelligence Technologies and the Role of Training Programs during this Process.
- Creator
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Robertson, Juliana, Gunter, Glenda, Thompson, Kelvin, Vitale, Thomas, Morrow, Patricia Bockelman, Lagasse, Paul, University of Central Florida
- Abstract / Description
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This research study sought to determine if there was any difference in the perception of training modality delivery between participants who attended a face-to-face (F2F) training session or participated in blended training that supported business intelligence (BI) technology adoption. There is minimal information available identifying how training can influence an individual's intention to fully adopt BI technology into daily work processes. Identification of key factors influencing training...
Show moreThis research study sought to determine if there was any difference in the perception of training modality delivery between participants who attended a face-to-face (F2F) training session or participated in blended training that supported business intelligence (BI) technology adoption. There is minimal information available identifying how training can influence an individual's intention to fully adopt BI technology into daily work processes. Identification of key factors influencing training modalities' effect on technology adoption promotes strategies that allow trainers to better facilitate and develop content that can help organizations to integrating BI technologies into their workflow. This study analyzed survey responses that captured the perceptions of end-users who completed training by attending a F2F or blended training and their readiness to utilize the BI technologies post-training. The sample for this study consisted of 62 individuals who completed both the training session survey (F2F or blended) and the client implementation survey; to qualify for this study, all participants completed both surveys; 33 participants attended the F2F training sessions, and 29 participants attended the blended training sessions. Survey responses related to the training session and the training consultant were used to identify differences in perception when comparing the two different groups and their feelings of preparedness to accept responsibility for the technology. While there was an indication that the feeling of preparedness to adopt the BI technology was more heavily influenced by the blended training, it is important to consider methods for improving participant satisfaction in all areas related to blended training. Overall, this study provides the basis for an executive summary indicating the need to implement more effective training strategies, policies, and training processes before and after implementing BI technologies within organizations.
Show less - Date Issued
- 2017
- Identifier
- CFE0006911, ucf:51699
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006911
- Title
- A Framework for Transforming Elementary Literacy Coaches' Professional Learning.
- Creator
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Forsythe, Lenora, Zygouris-Coe, Vassiliki, Hopp, Carolyn, Puig, Enrique, Roberts, Sherron, Zugelder, Gina, University of Central Florida
- Abstract / Description
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Elementary literacy coaches serve as one component in a professional learning system tosupport teacher learning. This dissertation in practice intended to highlight the need for an effective professional learning system for elementary literacy coaches that will enable them to impact teacher and student learning. The pilot study explored needs and perspectives of professional learning opportunities for elementary literacy coaches in a central Florida school district. Findings from the pilot...
Show moreElementary literacy coaches serve as one component in a professional learning system tosupport teacher learning. This dissertation in practice intended to highlight the need for an effective professional learning system for elementary literacy coaches that will enable them to impact teacher and student learning. The pilot study explored needs and perspectives of professional learning opportunities for elementary literacy coaches in a central Florida school district. Findings from the pilot study, along with literature surrounding the topic, resulted in the design of A Framework for Elementary Literacy Coaches' Professional Learning. This Framework utilized components from existing resources to develop access points for literacy coaches' professional learning. Access points included choice in coaching cycles, collaborative learning communities among coaches, and differentiated learning opportunities for literacy coaches to build their repertoire of literacy content knowledge and coaching skills. Theoreticalcontributions of adult learning and the sociocultural learning perspective within the Framework ensured literacy coaches' choice, ownership, and embedded learning opportunities. Suggested use for this dissertation in practice is to inform professional learning practices for in-service and pre-service elementary literacy coaches to ensure continued growth in coaching skills and literacy knowledge.
Show less - Date Issued
- 2016
- Identifier
- CFE0006300, ucf:51610
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006300
- Title
- Learning Internal State Memory Representations from Observation.
- Creator
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Wong, Josiah, Gonzalez, Avelino, Liu, Fei, Wu, Annie, Ontanon, Santiago, Wiegand, Rudolf, University of Central Florida
- Abstract / Description
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Learning from Observation (LfO) is a machine learning paradigm that mimics how people learn in daily life: learning how to do something simply by watching someone else do it. LfO has been used in various applications, from video game agent creation to driving a car, but it has always been limited by the inability of an observer to know what a performing entity chooses to remember as they act in an environment. Various methods have either ignored the effects of memory or otherwise made...
Show moreLearning from Observation (LfO) is a machine learning paradigm that mimics how people learn in daily life: learning how to do something simply by watching someone else do it. LfO has been used in various applications, from video game agent creation to driving a car, but it has always been limited by the inability of an observer to know what a performing entity chooses to remember as they act in an environment. Various methods have either ignored the effects of memory or otherwise made simplistic assumptions about its structure. In this dissertation, we propose a new method, Memory Composition Learning, that captures the influence of a performer's memory in an observed behavior through the creation of an auxiliary memory feature set that explicitly models the aspects of the environment with significance for future decisions, and which can be used with a machine learning technique to provide salient information from memory. It advances the state of the art by automatically learning the internal structure of memory instead of ignoring or predefining it. This research is difficult in that memory modeling is an unsupervised learning problem that we elect to solve solely from unobtrusive observation. This research is significant for LfO in that it will allow learning techniques that otherwise could not use information from memory to use a tailored set of learned memory features that capture salient influences from memory and enable decision-making based on these influences for more effective learning performance. To validate our hypothesis, we implemented a prototype for modeling observed memory influences with our approach and applied it to simulated vacuum cleaner and lawn mower domains. Our investigation revealed that MCL was able to automatically learn memory features that describe the influences on an observed actor's internal state, and which improved learning performance of observed behaviors.
Show less - Date Issued
- 2019
- Identifier
- CFE0007879, ucf:52755
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007879
