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- All Subjects: Accountability
- Creators: Donofrio, Robert
Description
ABSTRACT External accountability is embedded in every school system across the United States. This dissertation study focuses on how ten principals negotiate the accountability system placed upon their school by the state of Arizona. The federal accountability policy, No Child Left Behind (NCLB), requires that states use a standardized assessment to document student achievement. Arizona's policy to meet the federal requirements of NCLB is Arizona Learns (AZLearns). AZLearns outlines the formulas for determining which schools are achieving and which schools need to improve. Each school is tagged with a label annually. The labels are Excelling, Highly Performing, Performing Plus, Performing, Underperforming and Failing. The foundation of this study lies in the interpretation, application and negotiation of a school's label by its principal. To investigate the relationship between external accountability and the daily life of a principal, I interviewed ten Arizona elementary school principals. The research questions of this study are: (R1) What effects do external accountability measures have on the development of the organizational capacity of a school? (R2) How do Arizona principals negotiate their school's assigned label in their everyday professional practice? (R3) What are Arizona principals' views of the state accountability process? A qualitative, phenomenological research methodology was used to interview the participants and analyze their stories for common themes. The commonalities that surfaced across the experiences of the principals in response to the labels placed on their school are Accountability, Achievement and Attitude. This study found that Accountability was based on multiple interpretations of policies enforced by the federal government, state or district guidelines and parent or school expectations. Achievement was a result of multiple factors including data collected from test scores, the quality of teachers or instruction and the personal goals of the principals. Attitude was a process embedded in the high stakes testing era, boundaries or conflicts within the location of the school and the personal experiences of the principals. This research is an attempt to share the multiple voices of principals that may lead to alternative meanings or even provoke questions about the labeling system in Arizona schools.
ContributorsMcNeil, David Michael (Author) / Davey, Lynn (Thesis advisor) / Mccarty, Teresa (Committee member) / Donofrio, Robert (Committee member) / Arizona State University (Publisher)
Created2011
Description
Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for clinical and social impact. Several technologies in cancer screening, such as Computer Aided Detection (CAD), have broken the barrier of research into reality through successful outcomes with patient data (Morton, Whaley, Brandt, & Amrami, 2006; Patel et al, 2018). Technologies, such as the IBM Medical Sieve, are growing excitement with the potential for increased impact through the addition of medical record information ("Medical Sieve Radiology Grand Challenge", 2018). As the capabilities of automation increase and become a part of expert-decision-making jobs, however, the careful consideration of its integration into human systems is often overlooked. This paper aims to identify how healthcare professionals and system engineers implementing and interacting with automated decision-making aids in Radiology should take bureaucratic, legal, professional, and political accountability concerns into consideration. This Accountability Framework is modeled after Romzek and Dubnick’s (1987) public administration framework and expanded on through an analysis of literature on accountability definitions and examples in military, healthcare, and research sectors. A cohesive understanding of this framework and the human concerns it raises helps drive the questions that, if fully addressed, create the potential for a successful integration and adoption of AI in radiology and ultimately the care environment.
ContributorsGilmore, Emily Anne (Author) / Chiou, Erin (Thesis director) / Wu, Teresa (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05