Exploring the Highest Levels of Fragmentation: A Case Study of Kowalski School Districts Issuance of Special Directives Despite Opposition from County Teachers and the Centers Shelves
Download Paper
Download Bibtex
Authors
- Rhuaridh Tiarnan
Associated
Related Links
- ACM Digital Library Records
- Video on YouTube (Optional)
- IEEE Xplore
- ThinkMind
- Acquisition of Knowledge
- Arxiv
- Arxra
- Eurographics
Abstract
This paper presents a case study of Kowalski School District's issuance of special directives amidst opposition from county teachers and the centers shelves, in order to explore the highest levels of fragmentation. The study utilizes a qualitative research methodology, including in-depth interviews and document analysis, to analyze the dynamics of power and the conflicts between school district authorities, county teachers, and the centers shelves. The findings reveal the complex and multi-layered nature of fragmentation, which is characterized by power struggles, conflicting interests, and institutional barriers. The study also highlights the role of communication and collaboration in overcoming fragmentation and achieving successful policy implementation. The implications of these findings for policy and practice in school districts are discussed, including the need for increased communication, collaboration, and transparency between school district authorities, county teachers, and the centers shelves, in order to ensure a more cohesive and effective education system. Overall, this paper provides a valuable contribution to the literature on fragmentation in education policy by offering a detailed case study that sheds light on the challenges and opportunities of policy implementation in complex institutional environments.
Citation
Rhuaridh Tiarnan "Exploring the Highest Levels of Fragmentation: A Case Study of Kowalski School Districts Issuance of Special Directives Despite Opposition from County Teachers and the Centers Shelves". IEEE Exploration in Machine Learning, 2016.
Supplemental Material
Preview
Note: This file is about ~5-30 MB in size.
This paper appears in:
Date of Release: 2016
Author(s): Rhuaridh Tiarnan.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications