Utilizando o software GeoGebra como recurso didático para o ensino do movimento oscilatório de pêndulos

Pós-graduação em Educação para a Ciência - FC

Sistema inteligente para a predição de grupo de risco de evasão discente

Pós-graduação em Engenharia Elétrica - FEIS

Estilos e estratégias de aprendizagem do inglês: um estudo de caso no ensino fundamental

Este estudo tem como objetivo investigar os estilos de aprendizagem de aprendentes de uma turma do Ensino Fundamental de uma escola pública municipal de Belém e as estratégias de aprendizagem utilizadas por eles nas aulas de inglês e também fora delas. Além disso, buscamos identificar os objetivos dos aprendentes com relação à aprendizagem de inglês. Foram adotados os princípios da pesquisa qualitativa estudo de caso para coleta e análise de dados. Os instrumentos utilizados foram questionários, auto-relatos verbais e notas de campo. As propostas de Brown (1994), Felder e Soloman (1993) e Oxford (2003) forneceram suporte teórico para uma tentativa de classificação dos estilos de aprendizagem. Escolhemos as teorias de Oxford (1990) para descrever e classificar as estratégias. Para tornar a aprendizagem de uma LE mais eficaz na escola pública, exploramos a possibilidade de propor uma instrução baseada em estratégias integrada ao conteúdo do curso regular. Com esse objetivo, apresentamos dois modelos: Instrução Baseada em Estratégias (IBES) de Cohen (1998) e o Modelo de Treinamento de Estratégias Oxford (1990). Nossos resultados descrevem os estilos e estratégias de aprendizagem dos alunos de uma turma da 8 série do Ensino Fundamental. Aventamos a possibilidade de que, identificadas essas características, professor e alunos podem criar situações de aprendizagem muito mais eficazes.

Materiais audiovisuais para a educação a distância: a contribuição dos estilos de aprendizagem

Pós-graduação em Educação Escolar - FCLAR

A model for the enhancement of autonomy

Este artigo apresenta um modelo para o fomento da autonomia dos aprendentes, mostra alguns resultados alcançados na aplicação desse modelo e discute desafios ainda a serem enfrentados. O modelo comporta a investigação de áreas problemáticas do processo individual de aprendizagem de cada sujeito, a identificação de seus estilos preferenciais de aprender, o uso de ferramentas tecnológicas para melhorar a autonomia na aprendizagem, o desenvolvimento de um leque maior de estratégias de aprendizagem de línguas e a implementação de rotinas de auto-monitoramento e auto-avaliação. Este modelo tem sido aplicado nos últimos três anos com alunos de Letras cursando Licenciaturas em Alemão, Francês ou Inglês. Três ordens de resultados emergem dos dados da pesquisa: primeiramente, o modelo provou sua eficácia em prover um andaime para a aprendizagem autônoma de línguas dos alunos; em segundo lugar, as experiências de aprendizagem autônoma vividas pelos futuros professores de línguas poderão ser espelhadas em suas vidas profissionais futuras com seus próprios alunos; finalmente, os dados emanados dos participantes da pesquisa podem lançar uma luz sobre a variedade de maneiras pelas quais as pessoas aprendem no contexto local.

Estilos de aprendizagem e interfaces online: aporte ao ensino presencial em saúde, na disciplina Interação Universidade-Serviço-Comunidade I da Faculdade de Medicina de Botucatu-UNESP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mathematical Communication, Conceptual Understanding, and Students' Attitudes Toward Mathematics

This action research study of my 8th grade classroom investigated the use of mathematical communication, through oral homework presentations and written journals entries, and its impact on conceptual understanding of mathematics. This change in expectation and its impact on students’ attitudes towards mathematics was also investigated. Challenging my students to communicate mathematics both orally and in writing deepened the students’ understanding of the mathematics. Levels of understanding deepened when a variety of instructional methods were presented and discussed where students could comprehend the ideas that best suited their learning styles. Increased understanding occurred through probing questions causing students to reflect on their learning and reevaluate their reasoning. This transpired when students were expected to write more than one draft to math journals. By making students aware of their understanding through communicating orally and in writing, students realized that true understanding did not come from mere homework completion, but from evaluating and assessing their own and other’s ideas and reasoning. I discovered that when students were challenged to communicate their reasoning both orally and in writing, students enjoyed math more and thought math was more fun. As a result of this research, I will continue to require students to communicate their thinking and reasoning both orally and in writing.

Estilos de aprendizagem e interfaces online: aporte ao ensino presencial em saúde, na disciplina Interação Universidade-Serviço-Comunidade I da Faculdade de Medicina de Botucatu-UNESP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Authoring tools and management information systems: study of a generalizable architecture

The need to effectively manage the documentation covering the entire production process, from the concept phase right through to market realise, constitutes a key issue in the creation of a successful and highly competitive product. For almost forty years the most commonly used strategies to achieve this have followed Product Lifecycle Management (PLM) guidelines. Translated into information management systems at the end of the '90s, this methodology is now widely used by companies operating all over the world in many different sectors. PLM systems and editor programs are the two principal types of software applications used by companies for their process aotomation. Editor programs allow to store in documents the information related to the production chain, while the PLM system stores and shares this information so that it can be used within the company and made it available to partners. Different software tools, which capture and store documents and information automatically in the PLM system, have been developed in recent years. One of them is the ''DirectPLM'' application, which has been developed by the Italian company ''Focus PLM''. It is designed to ensure interoperability between many editors and the Aras Innovator PLM system. In this dissertation we present ''DirectPLM2'', a new version of the previous software application DirectPLM. It has been designed and developed as prototype during the internship by Focus PLM. Its new implementation separates the abstract logic of business from the real commands implementation, previously strongly dependent on Aras Innovator. Thanks to its new design, Focus PLM can easily develop different versions of DirectPLM2, each one devised for a specific PLM system. In fact, the company can focus the development effort only on a specific set of software components which provides specialized functions interacting with that particular PLM system. This allows shorter Time-To-Market and gives the company a significant competitive advantage.

Evaluating Learner Perceptions of Use of Simulations for New nurses – A Collaboration Between the UT SON and the Methodist Hospital

The purpose of this evaluation project was to describe the integration of simulation into a nursing internship program and to help prepare new graduate nurses for patient care. Additionally, learning styles and perceptions of active learning, collaboration among peers, ways of learning, expectation of simulation, satisfaction, self-confidence, and design of simulation were examined. [See PDF for complete abstract]

Report on the practice based culminating experience as teaching assistant for "Pathology and Public Health" class taught at University of Texas School of Public Health, Houston

Objectives. The objectives of this report were to describe current best standards in online education, class competencies, class objectives, class activities and to compare the class competencies, objectives and activities undertaken with the current best practices in online teaching and to provide a list of recommendations based on the most efficacious practices. ^ Methods. Utilizing the key words- online teaching, national standards, quality, online courses, I: (1) conducted a search on Google to find the best standard for quality online courses; the search yielded National Standards for Quality Online Teaching as the gold standard in online course quality; (2) specified class objectives and competencies as well as major activities undertaken as a part of the class. Utilizing the Southern Regional Education Board evaluation checklist for online courses, I: (1) performed an analysis comparing the class activities, objectives, and competencies with the current best standards; (2) utilized the information obtained from the analysis and class experiences to develop recommendations for the most efficacious online teaching practices. ^ Results. The class met the criteria set by the Southern Regional Education Board for evaluating online classes completely in 75%, partially in 16% and did not meet the criteria in 9% cases. The majority of the parameters in which the class did not meet the standards (4 of 5) were due to technological reasons beyond the scope of the class instructor, teaching assistant and instructional design. ^ Discussion. Successful online teaching requires awareness of technology, good communication, methods, collaboration, reflection and flexibility. Creation of an online community, engaging online learners and utilizing different learning styles and assessment methods promote learning. My report proposes that online teaching should actively engage the students and teachers with multiple interactive strategies as evidenced from current best standards of online education and my “hands-on” work experience. ^ Conclusion. The report and the ideas presented are intended to create a foundation for efficacious practice on the online teaching platform. By following many of the efficacious online practices described in the report and adding from their own experiences, online instructors and teaching assistants can contribute to effective online learning. ^

Developing a Framework for Child Welfare Supervision

The roles of the child welfare supervisor in guiding practice and in retaining child welfare workers are well established in the literature. In this article, we discuss a framework for child welfare supervision that was developed and implemented in the state of Iowa with support from the Children’s Bureau through a five-year grant to improve recruitment and retention in public child welfare. The framework supports family centered practice through a parallel process of supervision reflecting these guiding principles: strength-based, competency-based, culturally competent, reflective, individualized to workers’ learning styles and stages of development, and aimed at enhancing worker skill, autonomy, teamwork, and commitment to the organization. We present key elements of the framework, an overview of implementation, and evaluation results regarding knowledge gain, use of skills, and rates of worker retention.

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Uso de redes sociales para mejorar el rendimiento de los alumnos con diferentes estilos de aprendizaje

Debido a los cambios que el Espacio Europeo de Educación Superior introduce al potenciar las horas de trabajo no presencial, se hacen necesarios nuevos mecanismos para posibilitar una mejor comunicación y cooperación en el proceso de aprendizaje. Las redes sociales, como Facebook, pueden suministrar estos mecanismos, pero su uso satisfactorio para la docencia puede verse afectado en gran medida por el estilo de aprendizaje de los alumnos. Este artículo plantea la necesidad de estudiar la influencia de los diferentes estilos de aprendizaje en la docencia no presencial mediante el uso de redes sociales con el fin de incrementar el rendimiento de los alumnos. Cabe destacar que este artículo describe el proyecto “Las redes sociales y su relación con los estilos de aprendizaje” a realizar dentro del programa de Redes de Investigación en Docencia Universitaria del Instituto de Ciencias de la Educación de la Universidad de Alicante.

Examining Special Education Teacher Preparation Programs

Thesis (Master's)--University of Washington, 2016-06