The purpose of this study was to examine topic-specific nature of pedagogical content knowledge (PCK). Two experienced chemistry teachers&rsquo / PCK was examined in electrochemistry and radioactivity. To capture participants&rsquo / PCK, all PCK components were studied. To get deep and rich answers to research questions asked, qualitative methodology was used. Participants were selected through purposeful sampling. Data were gathered through card-sorting activity, Content Representation (CoRe), semi-structured interviews, classroom observations, and field notes. Results revealed that participants had two types of PCK, namely, PCK A for teaching electrochemistry and PCK B for teaching radioactivity. PCK A included content-based and teacher-centered instruction, many links to other topics in chemistry and in physics. The assessment was coherent which included different types of assessment strategies used at the beginning, during, and at the end of teaching. In PCK B, it was less teacher-centered. The link to other topics was limited. Additionally, teachers used fragmented assessment and were less knowledgeable about learners&rsquo / difficulties and misconceptions in radioactivity than they were in electrochemistry. Differences between PCK A and B may be related to nature of the topics. Learners need to have much pre-requisite knowledge both from chemistry and physics to learn electrochemistry. Also, there are more concepts in electrochemistry than there are in radioactivity. It seems that when teachers have to focus on more concepts to teach, they may have a tendency to teach more-teacher centered to save time. Teacher education programs should focus on topic-specific nature of PCK and provide topic-specific training to teachers.

<p>Analyzing the ever-increasing data of unprecedented scale, dimensionality, diversity, and complexity poses considerable challenges to conventional approaches of statistical modeling. Bayesian nonparametrics constitute a promising research direction, in that such techniques can fit the data with a model that can grow with complexity to match the data. In this dissertation we consider nonparametric Bayesian modeling with completely random measures, a family of pure-jump stochastic processes with nonnegative increments. In particular, we study dictionary learning for sparse image representation using the beta process and the dependent hierarchical beta process, and we present the negative binomial process, a novel nonparametric Bayesian prior that unites the seemingly disjoint problems of count and mixture modeling. We show a wide variety of successful applications of our nonparametric Bayesian latent variable models to real problems in science and engineering, including count modeling, text analysis, image processing, compressive sensing, and computer vision.</p> / Dissertation

Esta tesis intenta demostrar cómo los sistemas de Recuperación de Información (RI) y los sistemas de Detección de Sucesos (TDT - Topic Detection and Tracking) mejoran si se añade una componente temporal extraída automáticamente del texto, a la cual denominaremos periodo de suceso. Este atributo representa el espacio de tiempo en el que transcurre el suceso principal relatado en cada documento. Con este propósito la tesis ha cubierto los siguientes objetivos: * Definición de un modelo de tiempo para representar y manipular las referencias temporales que aparecen en un texto. * Desarrollo de una aplicación para la extracción de expresiones temporales lingüísticas y el reconocimiento del intervalo absoluto que referencian según el calendario Gregoriano. * Implementación de un sistema para la extracción automática del periodo de suceso. * Modificación de los actuales sistemas de RI, TDT para incluir la información temporal extraída con las herramientas anteriores.

The purpose of this study was to examine topic-specific nature of pedagogical content knowledge (PCK). Two experienced chemistry teachers&rsquo / PCK was examined in electrochemistry and radioactivity. To capture participants&rsquo / PCK, all PCK components were studied. To get deep and rich answers to research questions asked, qualitative methodology was used. Participants were selected through purposeful sampling. Data were gathered through card-sorting activity, Content Representation (CoRe), semi-structured interviews, classroom observations, and field notes. Results revealed that participants had two types of PCK, namely, PCK A for teaching electrochemistry and PCK B for teaching radioactivity. PCK A included content-based and teacher-centered instruction, many links to other topics in chemistry and in physics. The assessment was coherent which included different types of assessment strategies used at the beginning, during, and at the end of teaching. In PCK B, it was less teacher-centered. The link to other topics was limited. Additionally, teachers used fragmented assessment and were less knowledgeable about learners&rsquo / difficulties and misconceptions in radioactivity than they were in electrochemistry. Differences between PCK A and B may be related to nature of the topics. Learners need to have much pre-requisite knowledge both from chemistry and physics to learn electrochemistry. Also, there are more concepts in electrochemistry than there are in radioactivity. It seems that when teachers have to focus on more concepts to teach, they may have a tendency to teach more-teacher centered to save time. Teacher education programs should focus on topic-specific nature of PCK and provide topic-specific training to teachers.

Thesis (M.N.)--Memorial University of Newfoundland, School of Nursing, 2000. / Typescript. Bibliography: leaves 105-119.

Source: Masters Abstracts International, Volume: 47-06, page: 3551. Advisers: Florence J. Dallo; Raul Caetano. Includes bibliographical references.

Thesis (doctoral)--University of Arizona, 2002. / Includes bibliographical references (p. 464-478).

A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document representation has two major challenges: sparsity and sequentiality. The sparsity often causes high estimation error, and text's sequential nature, interdependency between words, causes even more complication. This thesis presents novel document representations to overcome the two challenges. First, I employ label characteristics to estimate a compact document representation. Because label attributes implicitly describe the geometry of dense subspace that has substantial impact, I can effectively resolve the sparsity issue while only focusing the compact subspace. Second, while modeling a document as a joint or conditional distribution between words and their sequential information, I can efficiently reflect sequential nature of text in my document representations. Lastly, the thesis is concluded with a document representation that employs both labels and sequential information in a unified formulation. The following four criteria are utilized to evaluate the goodness of representations: how close a representation is to its original data, how strongly a representation can be distinguished from each other, how easy to interpret a representation by a human, and how much computational effort is needed for a representation. While pursuing those good representation criteria, I was able to obtain document representations that are closer to the original data, stronger in discrimination, and easier to be understood than traditional document representations. Efficient computation algorithms make the proposed approaches largely scalable. This thesis examines emotion prediction, temporal emotion analysis, modeling documents with edit histories, locally coherent topic modeling, and text categorization tasks for possible applications.

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Some of the theory of nonnegative matrices is first presented. The Perron-Frobenius theorem is highlighted. Some of the important linear algebraic methods of information retrieval are surveyed. Latent Semantic Indexing (LSI), which uses the singular value de-composition is discussed. The Hyper-Text Induced Topic Search (HITS) algorithm is next considered; here the power method for finding dominant eigenvectors is employed. Through the use of a theorem by Sinkohrn and Knopp, a modified HITS method is developed. Lastly, the PageRank algorithm is discussed. Numerical examples and MATLAB programs are also provided.