Building Leadership: The Knowledge of Principals in Creating Collaborative Communities of Professional Learning

Research literature is replete with the importance of collaboration in schools, the lack of its implementation, the centrality of the role of the principal, and the existence of a gap between knowledge and practice--or a "Knowing-Doing Gap." In other words, there is a set of knowledge that principals must know in order to create a collaborative workplace environment for teachers. This study sought to describe what high school principals know about creating such a culture of collaboration. The researcher combed journal articles, studies and professional literature in order to identify what principals must know in order to create a culture of collaboration. The result was ten elements of principal knowledge: Staff involvement in important decisions, Charismatic leadership not being necessary for success, Effective elements of teacher teams, Administrator‘s modeling professional learning, The allocation of resources, Staff meetings focused on student learning, Elements of continuous improvement, and Principles of Adult Learning, Student Learning and Change. From these ten elements, the researcher developed a web-based survey intended to measure nine of those elements (Charismatic leadership was excluded). Principals of accredited high schools in the state of Nebraska were invited to participate in this survey, as high schools are well-known for the isolation that teachers experience--particularly as a result of departmentalization. The results indicate that principals have knowledge of eight of the nine measured elements. The one that they lacked an understanding of was Principles of Student Learning. Given these two findings of what principals do and do not know, the researcher recommends that professional organizations, intermediate service agencies and district-level support staff engage in systematic and systemic initiatives to increase the knowledge of principals in the element of lacking knowledge. Further, given that eight of the nine elements are understood by principals, it would be wise to examine reasons for the implementation gap (Knowing-Doing Gap) and how to overcome it.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.