An analysis of teachers' perceptions of informal science professional development

Science professional development, which is fundamental to science education improvement, has been described as being weak and fragmentary. The purpose of this study was to investigate teachers' perceptions of informal science professional development to gain an in-depth understanding of the essence of the phenomenon and related science-teaching dispositions. Based on the frameworks of phenomenology, constructivism, and adult learning theory, the focus was on understanding how the phenomenon was experienced within the context of teachers' everyday world. ^ Data were collected from eight middle-school teachers purposefully selected because they had participated in informal programs during Project TRIPS (Teaching Revitalized Through Informal Programs in Science), a collaboration between the Miami-Dade school district, government agencies (including NASA), and non-profit organizations (including Audubon of Florida). In addition, the teachers experienced hands-on labs offered through universities (including the University of Arizona), field sites, and other agencies. ^ The study employed Seidman's (1991) three-interview series to collect the data. Several methods were used to enhance the credibility of the research, including using triangulation of the data. The interviews were transcribed, color-coded and organized into six themes that emerged from the data. The themes included: (a) internalized content knowledge, (b) correlated hands-on activities, (c) enhanced science-teaching disposition, (d) networking/camaraderie, (e) change of context, and (f) acknowledgment as professionals. The teachers identified supportive elements and constraints related to each theme. ^ The results indicated that informal programs offering experiential learning opportunities strengthened understanding of content knowledge. Teachers implemented hands-on activities that were explicitly correlated to their curriculum. Programs that were conducted in a relaxed context enhanced teachers' science-teaching dispositions. However, a lack of financial and administrative support, perceived safety risks, insufficient reflection time, and unclear itineraries impeded program implementation. The results illustrated how informal educators can use this cohesive model as they develop programs that address the supports and constraints to teachers' science instruction needs. This, in turn, can aid teachers as they strive to provide effective science instruction to students; notions embedded in reforms. Ultimately, this can affect how learners develop the ability to make informed science decisions that impact the quality of life on a global scale. ^

Exploring the integration of data mining and data visualization

Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be effective, it is important to include the visualization techniques in the mining process and to generate the discovered patterns for a more comprehensive visual view. In this dissertation, four related problems: dimensionality reduction for visualizing high dimensional datasets, visualization-based clustering evaluation, interactive document mining, and multiple clusterings exploration are studied to explore the integration of data mining and data visualization. In particular, we 1) propose an efficient feature selection method (reliefF + mRMR) for preprocessing high dimensional datasets; 2) present DClusterE to integrate cluster validation with user interaction and provide rich visualization tools for users to examine document clustering results from multiple perspectives; 3) design two interactive document summarization systems to involve users efforts and generate customized summaries from 2D sentence layouts; and 4) propose a new framework which organizes the different input clusterings into a hierarchical tree structure and allows for interactive exploration of multiple clustering solutions.