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- All Subjects: mobility
- Genre: Academic theses
- Creators: Baker, Dale
Description
The high rate of teacher turnover in the United States has prompted a number of studies into why teachers leave as well as why they stay. The present study aims to add to that knowledge specifically regarding why teachers choose to stay at urban schools. Several reasons teachers in general choose to stay have been identified in previous studies including faith in their students, continuing hope and sense of responsibility, and love among others. The importance of such a study is the possibility of designing programs that reinforce teacher success through understanding the personal and professional reasons teachers choose to stay. Getting teachers to stay is important to the nation's goal of providing equity in science education to all children. Important to this research is an understanding of motivational theories. Already a challenge in the over-busy modern world, the ability to self-motivate and motivate others is of particular importance to teachers in urban schools as well as teachers struggling against restrictive budgets. Studies have shown teachers extrinsically motivated will need external rewards to encourage them while teachers who are intrinsically motivated will have their own internal reasons such as satisfaction in contributing to the future, self-actualization, or the joy of accomplishment. Some studies have suggested that teachers who decide to remain teaching tend to be intrinsic motivators. Unfortunately, the environment in most Western country educational systems presents a challenge to achieving these intrinsic goals. As a result, self-determination theory should play a significant role in shaping educational programs. The following study examined the perspectives of secondary school science teachers, specifically regarding why they opted to remain within the classroom in urban districts. It was conducted utilizing interviews and surveys of teachers working within urban school districts in Arizona and California. The sample consisted of 94 science teachers. More than half of the participants were White females and 36 percent of them had been teaching for more than 15 years. Participation in the study was based on self-selected volunteerism. Survey questions were based on self-determination theory and used Likert scale responses. Follow-up audiotaped interview requested information regarding identity and their social interaction within the urban settings. The survey responses were analyzed using SPSS for descriptive statistics, one-way ANOVA, and linear regression. The results of this study provide insight on what works to motivate science teachers to continue teaching in less than ideal school settings and with such high bureaucratic impediments as standardized testing and school rating systems. It demonstrates that science teachers do seem to be intrinsically motivated and suggests some areas in which this motivation can be fostered. Such results could help in the development of teacher support groups, professional development programs, or other programs designed to assist teachers struggling to deal with the specific problems and needs of inner city school students.
ContributorsAlhashem, Fatimah (Author) / Baker, Dale (Thesis advisor) / Margolis, Eric (Committee member) / Husman, Jenefer (Committee member) / Arizona State University (Publisher)
Created2012
Description
As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
ContributorsOshan, Taylor Matthew (Author) / Fotheringham, A. S. (Thesis advisor) / Farmer, Carson J.Q. (Committee member) / Rey, Sergio S.J. (Committee member) / Nelson, Trisalyn (Committee member) / Arizona State University (Publisher)
Created2017