Matching Items (598)
Filtering by

Clear all filters

151681-Thumbnail Image.png
Description
Using experience, observations, data, current research, and writings in the field of volunteer management, it was determined there was a need to study the effects of leadership/management practices on the productivity outcomes of a volunteer construction workforce. A simple wood bench that would be tiled and painted was designed to

Using experience, observations, data, current research, and writings in the field of volunteer management, it was determined there was a need to study the effects of leadership/management practices on the productivity outcomes of a volunteer construction workforce. A simple wood bench that would be tiled and painted was designed to test the areas of Time, Waste, Quality, Safety, and Satisfaction of different volunteer groups. The challenge was bolstered by giving the teams no power tools and limited available resources. A simple design of experiment model was used to test highs and lows in the three management techniques of Instruction, Help, and Encouragement. Each scenario was tested multiple times. Data was collected, normalized and analyzed using statistical analysis software. A few significant findings were discovered. The first; the research showed that there was no significant correlation between the management practices of the leader and the satisfaction of the volunteers. The second; the research also showed when further analyzed into specific realistic scenarios that the organizations would be better to focus on high amounts of Help and Encouragement in order to maximize the productivity of their volunteer construction workforce. This is significant as it allows NPO's and governments to focus their attention where best suited to produce results. The results were shared and the study was further validated as "significant" by conducting interviews with experts in the construction nonprofit sector.
ContributorsPrigge, Diedrich (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2013
151689-Thumbnail Image.png
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
152189-Thumbnail Image.png
Description
This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’

This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect to the unknown underlying model. In that regard, this study proposes alternative ways to rank teacher effects that are not dependent on a given model by introducing two variable importance measures (VIMs), the node-proportion and the covariate-proportion. These VIMs are novel because they take into account the final configuration of the terminal nodes in the constitutive trees in a random forest. In a simulation study, under a variety of conditions, true rankings of teacher effects are compared with estimated rankings obtained using three sources: the newly proposed VIMs, existing VIMs, and EBLUPs from the assumed linear model specification. The newly proposed VIMs outperform all others in various scenarios where the model was misspecified. The second study develops two novel interaction measures. These measures could be used within but are not restricted to the VAM framework. The distribution-based measure is constructed to identify interactions in a general setting where a model specification is not assumed in advance. In turn, the mean-based measure is built to estimate interactions when the model specification is assumed to be linear. Both measures are unique in their construction; they take into account not only the outcome values, but also the internal structure of the trees in a random forest. In a separate simulation study, under a variety of conditions, the proposed measures are found to identify and estimate second-order interactions.
ContributorsValdivia, Arturo (Author) / Eubank, Randall (Thesis advisor) / Young, Dennis (Committee member) / Reiser, Mark R. (Committee member) / Kao, Ming-Hung (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
151913-Thumbnail Image.png
Description
In this mixed-methods study, I examined the relationship between professional development based on the Common Core State Standards for Mathematics and teacher knowledge, classroom practice, and student learning. Participants were randomly assigned to experimental and control groups. The 50-hour professional development treatment was administered to the treatment group during one

In this mixed-methods study, I examined the relationship between professional development based on the Common Core State Standards for Mathematics and teacher knowledge, classroom practice, and student learning. Participants were randomly assigned to experimental and control groups. The 50-hour professional development treatment was administered to the treatment group during one semester, and then a follow-up replication treatment was administered to the control group during the subsequent semester. Results revealed significant differences in teacher knowledge as a result of the treatment using two instruments. The Learning Mathematics for Teaching scales were used to detect changes in mathematical knowledge for teaching, and an online sorting task was used to detect changes in teachers' knowledge of their standards. Results also indicated differences in classroom practice between pairs of matched teachers selected to participate in classroom observations and interviews. No statistical difference was detected between the groups' student assessment scores using the district's benchmark assessment system. This efficacy study contributes to the literature in two ways. First, it provides an evidence base for a professional development model designed to promote effective implementation of the Common Core State Standards for Mathematics. Second, it addresses ways to impact and measure teachers' knowledge of curriculum in addition to their mathematical content knowledge. The treatment was designed to focus on knowledge of curriculum, but it also successfully impacted teachers' specialized content knowledge, knowledge of content and students, and knowledge of content and teaching.
ContributorsRimbey, Kimberly A (Author) / Middleton, James A. (Thesis advisor) / Sloane, Finbarr (Committee member) / Atkinson, Robert K (Committee member) / Arizona State University (Publisher)
Created2013
152039-Thumbnail Image.png
Description
An integral part of teacher development are teacher observations. Many teachers are observed once or twice a year to evaluate their performance and hold them accountable for meeting standards. Instructional coaches, however, observe and work with teachers to help them reflect on their performance, with the goal of improving their

An integral part of teacher development are teacher observations. Many teachers are observed once or twice a year to evaluate their performance and hold them accountable for meeting standards. Instructional coaches, however, observe and work with teachers to help them reflect on their performance, with the goal of improving their practice. Video-based evidence has long been used in connection with teacher reflection and as the technology necessary to record video has become more readily available, video recordings have found an increasing presence in teacher observations. In addition, more and more schools are turning to mobile technology to help record evidence during teacher observations. Several mobile applications have been developed, which are designed to help instructional coaches, administrators, and teachers make the most of teacher observations. This study looked at the use of the DataCapture mobile application to record video-based evidence in teacher observations as part of an instructional coaching program in a large public school district in the Southwestern United States. Six instructional coaches and two teachers participated in interviews at the end of the study period. Additional data was collected from the DataCapture mobile application and from a survey of instructional coaches conducted by the school district in connection with its Title I programs. Results show that instructional coaches feel that using video-based evidence for teacher reflection is effective in a number of ways. Teachers who have experienced seeing themselves on video also felt that video-based evidence is effective at improving teacher reflection, while teachers who have not yet experienced seeing themselves on video displayed extreme apprehensiveness about being video recorded in the classroom. Instructional coaches felt the DataCapture mobile application was beneficial in teacher evaluation, but there were several issues that impacted the use of the mobile application and video-based evidence, including logistics, time requirements, and administrative support. The discussion focuses on recommendations for successfully using video-based evidence in an instructional coaching context, as well as some suggestions for other researchers attempting to study how video-based evidence impacts teachers' ability to reflect on their own teaching.
ContributorsShewell, Justin Reed (Author) / Bitter, Gary (Thesis advisor) / Dawson, Edwin (Committee member) / Blair, Heidi (Committee member) / Arizona State University (Publisher)
Created2013
151988-Thumbnail Image.png
Description
Growing popularity of alternatively certifying teachers has created challenges for teacher preparation programs. Many non-traditional routes into classroom include no full-time mentor teacher. Absence of a mentor teacher in the classroom leaves teachers with a deficit. This study follows ten teachers on the intern certificate enrolled in both an alternative

Growing popularity of alternatively certifying teachers has created challenges for teacher preparation programs. Many non-traditional routes into classroom include no full-time mentor teacher. Absence of a mentor teacher in the classroom leaves teachers with a deficit. This study follows ten teachers on the intern certificate enrolled in both an alternative certification teacher preparation program and the Teach for America organization as they pursue a master's degree in education and state teaching certification from a large southwestern university. The five randomly chosen for the treatment group and the control group contained 1 male and 4 female teachers, some of whom teach at public schools and others at charter schools. All were secondary education language arts teachers ranging in age from 22- 29. The treatment used in this study is a job-embedded, professional development, software tool designed to help teachers track their classroom practices called MyiLOGS. The purpose of this action research project was to study the effect using MyiLOGS had on six of the nine areas evaluated by a modified version of the Teacher Advancement Program evaluation rubric, alignment with Opportunity To Learn constructs, and the tool's influence on the efficacy of these first year teachers. The data generated from this study indicate that the MyiLOGS tool did have a positive effect on the teachers' TAP evaluation performances. Also, the MyiLOGS tool had a large impact on the teachers' instruction as measured by the constructs of Opportunity to Learn and their teaching self-efficacy. Implications suggested the tool was an asset to these teachers because they tracked their data, became more reflective, and self-sufficient.
ContributorsRoggeman, Pamela (Author) / Puckett, Kathleen (Thesis advisor) / Kurz, Alexander (Committee member) / Mathur, Sarup (Committee member) / Arizona State University (Publisher)
Created2013
151817-Thumbnail Image.png
Description
This action research project engages questions about the relationship of teacher evaluation and teacher learning, joining the national conversation of accountability and teacher quality. It provides a solid philosophical foundation for changes in teacher evaluation and staff development, and analyzes past and current methods and trends in teacher evaluation. Set

This action research project engages questions about the relationship of teacher evaluation and teacher learning, joining the national conversation of accountability and teacher quality. It provides a solid philosophical foundation for changes in teacher evaluation and staff development, and analyzes past and current methods and trends in teacher evaluation. Set in the context of a suburban elementary charter school, the problems of traditional evaluation methods are confronted. The innovation proposed and implemented is Teacher Evaluation for Learning, Accountability, and Recognition (TELAR), a teacher evaluation system designed to support learning and accountability. TELAR includes multiple data points and perspectives, ongoing feedback and support, an evaluation instrument centered on collective values and a shared vision for professional work, and an emphasis on teacher reflection and self-assessment. This mixed-methods study employs both qualitative and quantitative measures to provide an enriched understanding of the current problem and the impact of the change effort. Results suggest that TELAR 1) helps teachers re-define their role as professionals in their own evaluation, positively increasing perceptions of value, 2) promotes a culture of learning through a focus on shared values for professional work, a spirit of support and teamwork, and continuous improvement; and 3) empowers teachers to assess their own practice, self-diagnose areas for growth, and generate goals through a continuous process of feedback, reflection, conversation, and support. Implications for practice and future studies are presented.
ContributorsMusser, Stephanie (Author) / Zambo, Ronald (Thesis advisor) / Jiménez, Rosa (Committee member) / Harrington, Timothy (Committee member) / Arizona State University (Publisher)
Created2013
151822-Thumbnail Image.png
Description
As schools across Arizona worked to meet NCLB's AYP requirement in 2010-2011, they were also labeled and sanctioned by AZ Learns. This phenomenological study focused on six effective high school principals in two Arizona school districts to ascertain how accountability policies impacted the principals' job responsibilities, autonomy, and ability to

As schools across Arizona worked to meet NCLB's AYP requirement in 2010-2011, they were also labeled and sanctioned by AZ Learns. This phenomenological study focused on six effective high school principals in two Arizona school districts to ascertain how accountability policies impacted the principals' job responsibilities, autonomy, and ability to pursue social justice on their campuses. Interviews were conducted in three phases: superintendents, three principals from the superintendents' recommendations of effective school leaders, and three teachers from each school. In addition to analysis of individual principal leadership patterns, comparisons were made across districts, and from school to school within the same district. The goal of the study was to determine if and how principals were able to accomplish their goals for their school. The principals' leadership styles were examined through a Vortex Leadership Framework that posited principals at the center of a vortex of varying leadership roles, interests, and external forces, including accountability, autonomy, and limited resources. Key findings included (a) high school principals' responsibilities now include selling change to their staff, (b) principals' accountability is limited more by district constraints than by state or federal accountability, (c) principals must contend with rigid one-size fits all accountability standards that do not always meet the needs of their students, and (d) principals' autonomy is tied to their resources, including funding for staffing and programs.
ContributorsBatsell, Holly (Author) / Powers, Jeanne M. (Thesis advisor) / Mccarty, Teresa (Committee member) / Davey, Lynn (Committee member) / Arizona State University (Publisher)
Created2013
151326-Thumbnail Image.png
Description
The signing of the No Child Left Behind Act in 2001 created a need for Title 1 principals to conceptualize and operationalize parent engagement. This study examines how three urban principals in Arizona implemented the mandates of the Act as it pertains to parent involvement. The purpose of this qualitative

The signing of the No Child Left Behind Act in 2001 created a need for Title 1 principals to conceptualize and operationalize parent engagement. This study examines how three urban principals in Arizona implemented the mandates of the Act as it pertains to parent involvement. The purpose of this qualitative case study is to examine how principals operationalize and conceptualize parent involvement as they navigate barriers and laws particular to the state of Arizona. This study sought to understand issues surrounding parent involvement in Title 1 schools in Arizona. The beliefs and interview dialogue of the principals as it pertains to parent engagement provided an understanding of how urban principals in Arizona implement the aspects of No Child Left Behind Act that deal with parent involvement. The research study concluded that parents have community cultural wealth that contributes to the success of the students of engaged parents and that cultural responsive leadership assists principals with engaging parents in their schools. The research concludes that a gap exists between how parents and principals perceive and construct parent engagement versus what is prescribed in No Child Left Behind Act.
ContributorsConley, Loraine (Author) / Brayboy, Bryan (Thesis advisor) / Mccarty, Teresa (Committee member) / Scott, Kimberly (Committee member) / Arizona State University (Publisher)
Created2012
151544-Thumbnail Image.png
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse

Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013