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- All Subjects: Educational leadership
- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Description
The purpose of the study is to explore the identity development and organizational culture of a student organization, the National Association of Latino Fraternal Organizations council (NALFO) by implementing a community of practice approach at a large, public university in southwestern United States. The objective is to construct a sustainable camaraderie among the existing Latino fraternal organizations at the university to influence leadership development, work toward a common vision, and a cohesive and systematic approach to collaboration, consequently transforming organizational culture. This study investigates the factors that contribute to and/or inhibit increased communication and collaboration and to describe the experiences of Latino fraternal members who are purposefully engaged in a community of practice. There are 57 fraternal organizations in five umbrella councils at the university, including predominately Caucasian, historically African American, Latino, and Multicultural groups, whose platforms are commonly leadership, scholarship, and philanthropy. This action research examines the experiences of six NALFO members individually and working as a community with the guidance of a mentor (the researcher). The researcher employs use of an anonymous initial and post electronic survey, a participant personal statement, an intentional and purposeful community of practice, a semi-structured individual interview, and focus groups to collect data. Findings suggest that length of membership and fraternal experience influence participant responses; however, the themes remain consistent. Building relationships, perception (by members and outsiders), identity development, organizational management, and challenging perspectives (from outside influences) are factors that influence the organizational culture of the organization. On the post electronic survey all participants indicate that the implementation of an intentional community of practice can benefit the organization by encouraging participation and increasing communication. While participants suggest activities for encouraging member engagement, they determine that actual participation would be dependent on individual motivation.
ContributorsHeredia, Anna-Maria (Author) / Rund, James (Thesis advisor) / Calleroz White, Mistalene (Thesis advisor) / Corey, Frederick (Committee member) / Arizona State University (Publisher)
Created2013
Description
Peer learning is one of the longest established and most intensively researched forms of learning. As a form of peer learning, peer tutoring is characterized by specific role-taking as tutor or tutee with high focus on curriculum content. In the late 18th century, Andrew Bell undoubtedly became the first person in the world to use peer tutoring in a systematic fashion within a school setting. Due to its miraculous success, Bell affirmed that peer tutoring was the new method of practical education and was essential to every academic institution. Early in American education, teachers relied on certain students to teach others (i.e., peer tutoring) but this occurred on an informal, impromptu, as needed basis. This type of peer tutoring lasted well into the 20th century. A recent change in the traditional face of peer tutoring arrangements for U.S. schools has occurred due to more than 30 years of research at four major tutoring centers. Peer tutoring has moved away from an informal and casual approach to a more formal and robust method of teaching and learning. However, at the researcher's high school, peer tutoring was still very casual, informal, and practically non-existent. Consequently, the researcher created a peer tutoring club, and developed, and implemented a peer tutoring program. The researcher conducted a mixed-methods study with design-based research (DBR) as the preferred research design in order to discover what constitutes an ideal peer tutor and an ideal peer tutoring session. The researcher utilized qualitative means to analyze the following data: 1) field notes, 2) impromptu interviews, 3) questionnaires, 4) focus group interviews, and 5) a semi-structured interview. The researcher utilized quantitative means to analyze the following data: 1) sessions tutored survey and 2) archival data (e.g., daily attendance, school records). Analysis of qualitative and quantitative data suggested that the ideal peer tutor was qualified (e.g., desire, character traits, content mastery), trained (e.g., responsibilities, methodologies, procedures), and experienced. Likewise, in addition to having an ideal peer tutor, an ideal peer tutoring session took place in an environment conducive to learning and tutees were prepared and actively participated.
ContributorsJohnson, Brian (Author) / Carlson, David (Thesis advisor) / Barnard, Wendy (Committee member) / Moore, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
The growing population of American Indian students who attend off-reservation school has been under researched. This absence in American Indian education research, their unique needs, and their growing numbers warrant more attention. To address this absence in education research literature, this study captures the experiences of American Indian students in an off-reservation high school. Through Social Reproduction Theory and Cultural Capital Theory this qualitative study makes known the varying ways that American Indian students in off-reservation high schools comply and resist formal schooling. Through interviews and observations of these students, in addition their teachers and administrators, I document and interpret their experiences. The data suggest that American Indian students strongly connect to and use their tribal identities to negotiate school. By recognizing the rules of the school, these students employ different forms of cultural and social capital, specifically the importance of space and forms of communication. Even though their high school has a high population of American Indian students, they continue to experience challenges in academic success through stereotypical assumptions, expected roles, and structural barriers. Illustrating student identity as effects of the social reproduction process clearly demonstrates resistance, compliance, and agency of these students in their high school.
ContributorsBegay, Victor H (Author) / Margolis, Eric (Thesis advisor) / Mccarty, Teresa L. (Committee member) / Appleton, Nicholas (Committee member) / Arizona State University (Publisher)
Created2014
Descriptionno
ContributorsSalabiye-Gishey, Leclare Ellen (Author) / Spencer, Dee A. (Thesis advisor) / Appleton, Nicholas A. (Committee member) / Hotchkiss, Margaret (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
ContributorsViswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Thesis advisor) / T.H CHI, Michelene (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Given the current focus on high-stakes accountability in America's public schools, this study examined teacher evaluation specific to physical education. This study revealed current teacher evaluation practices used in physical education, perceptions of school administrators related to the value of the physical education evaluation process, and the perceptions of the physical education teachers related to the value of the evaluation process. The first phase of this study was an interpretive document analysis study conducted on four separate teacher evaluation systems commonly used within the public school system to evaluate physical education teachers. Those four systems were: Marzanos teacher evaluation model, Danielson framework for teaching (FFT), Rewarding Excellence in Instruction and Leadership (REIL), and Teacher Advancement Program (TAP). A separate evaluation instrument specific to physical education created by the National Association of Sport and Physical Education (NASPE) was used as a comparative evaluation tool. Evidence suggests that two of the four teacher evaluation systems had a high percentage of alignment with the NASPE instrument (TAP 87.5%, FFT 82.5%). The Marzano teacher evaluation model had the least amount of alignment with the NASPE instrument (62.5%). The second phase of this study was a phenomenological approach to understanding administrators' and physical education teachers' perceptions to teacher evaluation specific to physical education. The participants in this study were administrators and physical education teachers from an urban school district. An informal survey and formal semi-structured interviews were used to reveal perceptions of teacher evaluation specific to physical education. Evidence from the administrator's informal survey and formal semi-structured interviews revealed four common themes: (1) "I value PE, but I live in reality" (administrators value physical education, but practice in reality); (2) "good teaching is good teaching"; (3) "I know my limitations, and I want
eed help" (relative to teacher evaluation in PE); and (4) where's the training beef? Evidence from the physical education teacher's informal survey and formal semi-structured interviews revealed three common themes: (a) physical education is valued, but not prioritized; (b) teacher evaluation in physical education is "greatly needed, yet not transparent; (c) physical educators are not confident in their evaluator.
eed help" (relative to teacher evaluation in PE); and (4) where's the training beef? Evidence from the physical education teacher's informal survey and formal semi-structured interviews revealed three common themes: (a) physical education is valued, but not prioritized; (b) teacher evaluation in physical education is "greatly needed, yet not transparent; (c) physical educators are not confident in their evaluator.
ContributorsNorris, Jason (Author) / Van Der Mars, Hans (Thesis advisor) / Beardsley, Audrey (Thesis advisor) / Kulinna, Pamela (Committee member) / Arizona State University (Publisher)
Created2014
Description
In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients.
ContributorsZhou, Jiayu (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans (Committee member) / Li, Baoxin (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many learning models have been proposed for various tasks in visual computing. Popular examples include hidden Markov models and support vector machines. Recently, sparse-representation-based learning methods have attracted a lot of attention in the computer vision field, largely because of their impressive performance in many applications. In the literature, many of such sparse learning methods focus on designing or application of some learning techniques for certain feature space without much explicit consideration on possible interaction between the underlying semantics of the visual data and the employed learning technique. Rich semantic information in most visual data, if properly incorporated into algorithm design, should help achieving improved performance while delivering intuitive interpretation of the algorithmic outcomes. My study addresses the problem of how to explicitly consider the semantic information of the visual data in the sparse learning algorithms. In this work, we identify four problems which are of great importance and broad interest to the community. Specifically, a novel approach is proposed to incorporate label information to learn a dictionary which is not only reconstructive but also discriminative; considering the formation process of face images, a novel image decomposition approach for an ensemble of correlated images is proposed, where a subspace is built from the decomposition and applied to face recognition; based on the observation that, the foreground (or salient) objects are sparse in input domain and the background is sparse in frequency domain, a novel and efficient spatio-temporal saliency detection algorithm is proposed to identify the salient regions in video; and a novel hidden Markov model learning approach is proposed by utilizing a sparse set of pairwise comparisons among the data, which is easier to obtain and more meaningful, consistent than tradition labels, in many scenarios, e.g., evaluating motion skills in surgical simulations. In those four problems, different types of semantic information are modeled and incorporated in designing sparse learning algorithms for the corresponding visual computing tasks. Several real world applications are selected to demonstrate the effectiveness of the proposed methods, including, face recognition, spatio-temporal saliency detection, abnormality detection, spatio-temporal interest point detection, motion analysis and emotion recognition. In those applications, data of different modalities are involved, ranging from audio signal, image to video. Experiments on large scale real world data with comparisons to state-of-art methods confirm the proposed approaches deliver salient advantages, showing adding those semantic information dramatically improve the performances of the general sparse learning methods.
ContributorsZhang, Qiang (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Wang, Yalin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
Research in the learning sciences suggests that students learn better by collaborating with their peers than learning individually. Students working together as a group tend to generate new ideas more frequently and exhibit a higher level of reasoning. In this internet age with the advent of massive open online courses (MOOCs), students across the world are able to access and learn material remotely. This creates a need for tools that support distant or remote collaboration. In order to build such tools we need to understand the basic elements of remote collaboration and how it differs from traditional face-to-face collaboration.
The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.
The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.
ContributorsNelakurthi, Arun Reddy (Author) / Pon-Barry, Heather (Thesis advisor) / VanLehn, Kurt (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014