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- Genre: Doctoral Dissertation
- Genre: Databases
- Creators: Arizona State University
Description
There has been a push to create and implement school wellness policies. Childhood obesity statistics suggest that schools may have an important role to play in promoting wellness. Childhood obesity has become a significant problem in the United States. The percentage of obese children in the United States has more than doubled since 1970, and up to 33% of the children in the United States are currently overweight. Among the 33% of children who are overweight, 25% are obese, and 14% have type 2 diabetes, previously considered to be a condition found only in adults. This mixed-method study with a string qualitative component study examined three aspects of federally mandated local wellness polices. The study investigated the policies themselves, how the policies are understood in the local school setting, with a particular focus on the impact the policies have had on school meals. The bulk of the research data was generated through 8 in-depth interviews. The interviews were conducted with key stakeholders within 2 elementary school districts in Arizona. In addition, the evaluation of 20 local wellness polices was conducted via a rubric scoring system. The primary component found to be lacking in local wellness policies was the evaluation method. Recommendations for school districts include the establishment of a clear method of measurement.
ContributorsCrawford, Sara S (Author) / Mccarty, Teresa L. (Thesis advisor) / Molnar, Alex (Thesis advisor) / Montoya, Araceli (Committee member) / Arizona State University (Publisher)
Created2011
Description
Parents die during the lives of their children. If the child is an adolescent, that death will impact the student's education immediately or in subsequent years. Findings show the death of a mother does impact the daughter's education. It is imperative educators are willing to work with the student at the time the death occurs as well as in the ensuing months. Seidman's (2006) three-interview format was used as a template for the interviews of 11 women, ranging in age from 19 to 78 and whose mothers died when the women were adolescents. The interviews were primarily conducted in one sitting, transcribed, and then analyzed for common themes that connected to the research on the topic. Those themes include grieving, the role of caring in education, the role of teacher as the second mother, mother-daughter relationships, and the impact of parent death on schooling. These themes from the data cross cut with thematic strands within the study's theoretical framework: the nurturing and empathetic role of the mother, a desire of the daughter not to be different, and the ethics of caring. Findings in this study reveal that the negative impacts of mother loss are felt in diffuse ways, such as a lack of academic or emotional encouragement. Many women discussed the need and availability of support groups including groups at colleges. One practical implication of these findings is schools need to become caring communities in which caring is the norm for all students and teachers, thereby providing all students with needed support in times of crisis. The implications for further research include the impact of the mother death on the education of daughters, how volunteering with an organization related to the cause of the mother's death assists the daughter and types of programs most important to a student's success in post-secondary education. Adolescents are in a time of great change in their lives, and for a daughter, the loss of a mother has an everlasting, life-changing impact.
ContributorsRatti, Theresa Helen McLuskey (Author) / Mccarty, Teresa L (Thesis advisor) / Fischman, Gustavo E. (Committee member) / Powers, Jeanne M. (Committee member) / Arizona State University (Publisher)
Created2011
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
The United States is facing an emerging principal shortage. This study examines an intervention to deliver professional development for assistant principals on their way to becoming principals. The intervention intended to boost their sense of efficacy as if they were principals while creating a supportive community of professionals for ongoing professional learning. The community was designed much like a professional learning community (PLC) with the intent of developing into a community of practice (CoP). The participants were all elementary school assistant principals in a Title I district in a large metropolitan area. The researcher interviewed an expert set of school administrators consisting of superintendents and consultants (and others who have knowledge of what a good principal ought to be) about what characteristics and skills were left wanting in principal applicants. The data from these interviews provided the discussion topics for the intervention. The assistant principals met regularly over the course of a semester and discussed the topics provided by the expert set of school administrators. Participant interaction within the sessions followed conversation protocols. The researcher was also a participant in the group and served as the coordinator. Each session was recorded and transcribed. The researcher used a mixed methods approach to analyze the intervention. Participants were surveyed to measure their efficacy before and after the intervention. The session transcripts were analyzed using open and axial coding. Data showed no statistically significant change in the participants' sense of efficacy. Data also showed the participants became a coalescing community of practice.
ContributorsRichman, Bryan (Author) / Puckett, Kathleen (Thesis advisor) / Smith, Jeffery (Committee member) / Foulger, Teresa (Committee member) / Arizona State University (Publisher)
Created2011
Description
It is commonly accepted that undergraduate degree attainment rates must improve if postsecondary educational institutions are to meet macroeconomic demands. Involvement in co-curricular activities, such as student clubs and organizations, has been shown to increase students' satisfaction with their college experience and the rates by which they might persist. Yet, strategies that college administrators, faculties, and peer leaders may employ to effectively promote co-curricular engagement opportunities to students are not well developed. In turn, I created the Sky Leaders program, a retention-focused intervention designed to promote commuter student involvement in academically-purposeful activities via faculty- and peer-lead mentoring experiences. Working from an interpretivist research paradigm, this quasi-experimental mixed methods action research study was intended to measure the intervention's impact on participants' re-enrollment and reported engagement rates, as well as the effectiveness of its conceptual and logistical aspects. I used enrollment, survey, interview, observation, and focus group data collection instruments to accommodate an integrated data procurement process, which allowed for the consideration of several perspectives related to the same research questions. I analyzed all of the quantitative data captured from the enrollment and survey instruments using descriptive and inferential statistics to explore statistically and practically significant differences between participant groups. As a result, I identified one significant finding that had a perceived positive effect. Expressly, I found the difference between treatment and control participants' reported levels of engagement within co-curricular activities to be statistically and practically significant. Additionally, consistent with Glaser and Strauss' grounded theory approach, I employed open, axial, and selective coding procedures to analyze all of the qualitative data obtained via open-ended survey items, as well as interview, observation, and focus group instruments. After I reviewed and examined the qualitative data corpus, I constructed six themes reflective of the participants' programmatic experiences as well as conceptual and logistical features of the intervention. In doing so, I found that faculty, staff, and peer leaders may efficaciously serve in specific mentoring roles to promote co-curricular engagement opportunities and advance students' institutional academic and social integration, thereby effectively curbing their potential college departure decisions, which often arise out of mal-integrative experiences.
ContributorsSebold, Brent (Author) / Beardsley, Audrey (Thesis advisor) / Serafini, Frank (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2011
Description
Federal education policies call for school district leaders to promote classroom technology integration to prepare students with 21st century skills. However, schools are struggling to integrate technology effectively, with students often reporting that they feel like they need to power down and step back in time technologically when they enter classrooms. The lack of meaningful technology use in classrooms indicates a need for increased teacher preparation. The purpose of this study was to investigate the impact a coaching model of professional development had on school administrators` abilities to increase middle school teachers` technology integration in their classrooms. This study attempted to coach administrators to develop and articulate a vision, cultivate a culture, and model instruction relative to the meaningful use of instructional technology. The study occurred in a middle school. Data for this case study were collected via administrator interviews, the Principal`s Computer Technology Survey, structured observations using the Higher Order Thinking, Engaged Learning, Authentic Learning, Technology Use protocol, field notes, the Technology Integration Matrix, teacher interviews, and a research log. Findings concluded that cultivating change in an organization is a complex process that requires commitment over an extended period of time. The meaningful use of instructional technology remained minimal at the school during fall 2010. My actions as a change agent informed the school`s administrators about the role meaningful use of technology can play in instruction. Limited professional development, administrative vision, and expectations minimized the teachers` meaningful use of instructional technology; competing priorities and limited time minimized the administrators` efforts to improve the meaningful use of instructional technology. Realizing that technology proficient teachers contribute to student success with technology, it may be wise for administrators to incorporate technology-enriched professional development and exercise their leadership abilities to promote meaningful technology use in classrooms.
ContributorsRobertson, Kristen (Author) / Moore, David (Thesis advisor) / Cheatham, Greg (Committee member) / Catalano, Ruth (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Service based software (SBS) systems are software systems consisting of services based on the service oriented architecture (SOA). Each service in SBS systems provides partial functionalities and collaborates with other services as workflows to provide the functionalities required by the systems. These services may be developed and/or owned by different entities and physically distributed across the Internet. Compared with traditional software system components which are usually specifically designed for the target systems and bound tightly, the interfaces of services and their communication protocols are standardized, which allow SBS systems to support late binding, provide better interoperability, better flexibility in dynamic business logics, and higher fault tolerance. The development process of SBS systems can be divided to three major phases: 1) SBS specification, 2) service discovery and matching, and 3) service composition and workflow execution. This dissertation focuses on the second phase, and presents a privacy preserving service discovery and ranking approach for multiple user QoS requirements. This approach helps service providers to register services and service users to search services through public, but untrusted service directories with the protection of their privacy against the service directories. The service directories can match the registered services with service requests, but do not learn any information about them. Our approach also enforces access control on services during the matching process, which prevents unauthorized users from discovering services. After the service directories match a set of services that satisfy the service users' functionality requirements, the service discovery approach presented in this dissertation further considers service users' QoS requirements in two steps. First, this approach optimizes services' QoS by making tradeoff among various QoS aspects with users' QoS requirements and preferences. Second, this approach ranks services based on how well they satisfy users' QoS requirements to help service users select the most suitable service to develop their SBSs.
ContributorsYin, Yin (Author) / Yau, Stephen S. (Thesis advisor) / Candan, Kasim (Committee member) / Dasgupta, Partha (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study presents the results of one of the first attempts to characterize the pore water pressure response of soils subjected to traffic loading under saturated and unsaturated conditions. It is widely known that pore water pressure develops within the soil pores as a response to external stimulus. Also, it has been recognized that the development of pores water pressure contributes to the degradation of the resilient modulus of unbound materials. In the last decades several efforts have been directed to model the effect of air and water pore pressures upon resilient modulus. However, none of them consider dynamic variations in pressures but rather are based on equilibrium values corresponding to initial conditions. The measurement of this response is challenging especially in soils under unsaturated conditions. Models are needed not only to overcome testing limitations but also to understand the dynamic behavior of internal pore pressures that under critical conditions may even lead to failure. A testing program was conducted to characterize the pore water pressure response of a low plasticity fine clayey sand subjected to dynamic loading. The bulk stress, initial matric suction and dwelling time parameters were controlled and their effects were analyzed. The results were used to attempt models capable of predicting the accumulated excess pore pressure at any given time during the traffic loading and unloading phases. Important findings regarding the influence of the controlled variables challenge common beliefs. The accumulated excess pore water pressure was found to be higher for unsaturated soil specimens than for saturated soil specimens. The maximum pore water pressure always increased when the high bulk stress level was applied. Higher dwelling time was found to decelerate the accumulation of pore water pressure. In addition, it was found that the higher the dwelling time, the lower the maximum pore water pressure. It was concluded that upon further research, the proposed models may become a powerful tool not only to overcome testing limitations but also to enhance current design practices and to prevent soil failure due to excessive development of pore water pressure.
ContributorsCary, Carlos (Author) / Zapata, Claudia E (Thesis advisor) / Wiczak, Matthew W (Thesis advisor) / Kaloush, Kamil (Committee member) / Sandra, Houston (Committee member) / Arizona State University (Publisher)
Created2011