This project is aimed at generating new knowledge and improving our understanding of how Modified for Language Support-Worked Example Pairs (MLS-WEPs) contribute to effective mathematics learning and teaching in an ESOL (English to Speakers of Other Languages) context. The current study investigated a novel instructional approach to help English Language Learners (ELLs) develop better understanding in mathematical reasoning, problem solving, and literacy skills (listening, reading, writing, and speaking) while they are still developing their English language proficiency. The current study followed a wait-list control design, with both the treatment and control groups receiving intervention materials. The intervention materials were administered multiple times with different topics (units) throughout the study. The lessons were audio-recorded when the selected topics were taught. Pretest and posttest were given each time when the selected topics were taught. The data analysis for this study included both qualitative and quantitative analyses. The present study revealed the following results: (1) MLS-WEPs not only enhanced ELLs’ ability to solve mathematical problems, but also improved their written explanation skills and enabled them to transfer such skills to different mathematical concepts; (2) when controlling ELLs’ prior knowledge, the effectiveness of the MLS-WEPs intervention did not vary by their English language proficiency; (3) the MLS-WEPs intervention materials facilitated teachers to provide ELLs with more opportunities to read, write, and speak in mathematics and enabled teachers to ask more and deeper questions. However, worked example comparisons did not appear to motivate the participant teachers to promote equitable participation in mathematics classrooms. These findings provide direct empirical support for the need to reform mathematics teaching and learning in the ESOL context. / Math & Science Education

Att lösa matematiska uppgifter med hjälp av färdiga lösningsförslag kan ses som ett sätt för elever att anstränga sig mindre och därför lära sig mindre. Det finns dock forskning som har visat att undervisning där eleverna huvudsakligen studerar lösningsförslag har en stor effekt på elevernas förmåga att lösa uppgifter. Den här undervisningsmetoden kallas "the worked example effect" och har fått ett genomslag i den svenska skolan den senaste tiden. Metoden grundas på Swellers teori om kognitiv belastning. Enligt hans och andras forskning kan arbetsminnet delas upp i tre olika typer. Dessa tre, intrinsic cognitive load, germane cognitive load samt extraneous cognitive load, är additiva och stödjer lärande olika mycket. Lösningsförslag har visats minska mängden extraneous cognitive load och öka mängden germane cognitive load. Germane cognitive load har i sin tur visats korrelera med hur väl elever lyckas svara på matematiska uppgifter. Den här studien syftar till att undersöka lösningsförslagens effekt på elevers lärande i den svenska gymnasieskolan. I studien deltog tre skolor, varav en enbart i förstudien, i olika omfattning med totalt 93 elever på natur- och teknikprogrammen i andra årskursen på gymnasiet (17-18-åringar). Elevernas resultat på nationella proven i samtliga obligatoriska matematikkurser användes i undersökningen. Även en enkät genomfördes för att samla in mer information om eleverna och hur de studerade. Studien visar på ett tvetydigt resultat. Över 75% av eleverna anser sig ha ändrat sitt sätt att studera på grund av lösningsförslagen, men om och hur deras lärande har förbättrats, mätt i betyg, är oklart. För en av kurserna i matematik visade studien på ett negativt resultat, men för en annan kurs – positivt. Dessa olika resultat uppkom dessutom på olika skolor. En tolkning av detta resultat kan vara att lösningsförslagens effektivitet är beroende på vilken typ av matematik som ingår i kursen där de används. Det spelar även stor roll hur lösningsförslagen använts av eleverna, något som studien inte undersökt. / To solve mathematical tasks with the help of example solutions can be seen as a way for learners to apply less effort and therefore learn less or perhaps learn in undesirable ways. However, existing international research has indicated that classrooms where pupils mainly study worked examples has had a significant effect on their ability to solve tasks. This method is called "the worked example effect" and has gained increasing support over time. It is based on Sweller’s cognitive load theory. According to his and others’ research the working memory can be divided into three different types. These three, intrinsic cognitive load, germane cognitive load and extraneous cognitive load, are additive and support learning differently. Using worked examples has been shown to reduce the amount of extraneous cognitive load and instead increase the amount of germane cognitive load. Germane cognitive load has in turn been shown to correlate to how well pupils are able to solve mathematical tasks. This thesis aims to study the worked example effect in relation to learning outcome in Swedish secondary school. Three schools took part in the study, of which one only participated in a pilot study. A total of 93 pupils participated, all of them in the science program. Results of the national test for the four latest courses were used in this thesis. The pupils were also asked to fill out a survey with the aim to gather information as to how they used the worked examples when studying. The results of the study are ambiguous. More than 75% of the pupils claim to have changed how they study, but whether their learning has improved, measured in grades, is unclear. The study showed a negative result for one course but a positive for another. These different results originated from different schools. The results of the study may indicate that the worked example effect differs depending on which type of math is being taught. Another important factor is how the worked examples are used by the pupils, something this study has not examined.

A Journey from the Wordless is a study of the development of the author’s own professional knowledge.  After an introductory chapter on theory and method, there are four chapters treating of the author`s academic training as an artist (Ch. 2), a presentation and analysis of his own exhibitions (Ch. 3), a chapter on a cooperative effort entitled Pompeii in Time and Space (Ch. 4), and a concluding discussion of the nature of personal knowledge (Ch. 5). Questions are addressed concerning artistic knowledge; how it is built and transferred and how it is developed and transformed. Since the author`s own professional knowledge is the object of the research, special perspectives on personal experience emerges that otherwise would be difficult to articulate. The thesis is also a contribution to the debate surrounding artistic research in the visual arts, focusing on the importance of reflection and analysis in art education and creative art work.

Submitted by Nara Lays Domingues Viana Oliveira (naradv) on 2015-06-23T17:50:28Z No. of bitstreams: 1 marcelo.pdf: 9346349 bytes, checksum: 5ff2ba0d31a71c5302bd894ccd1a4521 (MD5) / Made available in DSpace on 2015-06-23T17:50:28Z (GMT). No. of bitstreams: 1 marcelo.pdf: 9346349 bytes, checksum: 5ff2ba0d31a71c5302bd894ccd1a4521 (MD5) Previous issue date: 2013-06-25 / Nenhuma / A Lei Federal 11.196/2005, conhecida como lei do bem, alcança um volume considerável de incentivos fiscais aos gestores que queiram investir em inovação tecnológica. Apesar desses benefícios estarem disponíveis às empresas, a análise do Relatório de Utilização de Incentivos Fiscais do MCTI, Ministério da Ciência, Tecnologia e Inovação de 2011, referente ao ano base de 2010, em comparação com 2009, denota que é crescente o número de organizações que são atraídas pelas isenções tributárias e aproveitamentos contábeis disponíveis, por volta de 38% (trinta e oito por cento), essa percentagem ainda é bastante acanhada em termos de Brasil, foram 875 (oitocentos e setenta e cinco) requerimentos encaminhados, com relevantes 236 (duzentos e trinta e seis) que não foram contemplados. O trabalho analisou as dificuldades e os ganhos de 10 (dez) empresas no Estado do Rio Grande do Sul, segunda região brasileira mais habilitada no Relatório do MCTI. Como metodologia foi utilizada o fenomenológico hermenêutico, com abordagem qualitativa e a coleta de dados, a partir de roteiro de entrevistas semiestruturadas em sintonia com a legislação nacional. Os resultados indicam que o governo deve melhorar a comunicação dos incentivos fiscais para projetos de inovação junto às empresas, pois existe grande dispersão na forma em que as empresas têm conhecimento sobre o acesso a Lei do Bem; existe grande número de profissionais de diferentes áreas que estão envolvidos, tanto no projeto quanto na interpretação da legislação. Sendo que as normas do programa geram dúvidas nas empresas, o que causa insegurança e faz com que abram mão de benefícios com receio dos projetos não serem aprovados e, consequentemente, glosados pela Receita Federal. Foi constatado também, que embora haja uma diminuição no ganho financeiro da operação com os projetos, pelo impacto das dificuldades legais, as empresas participantes têm outros projetos em andamento, por outras motivações. Por fim, através de exemplo prático se cotejam duas empresas: uma com incidência e outra sem incidência dos benefícios fiscais da Lei do Bem, visando facilitar a compreensão da instrumentalização do programa e dos resultados financeiros, em nível ótimo de aplicação. / Federal Law 11.196/2005, known as the “Good Law”, reaches a considerable amount of tax incentives to managers who want to invest in technological innovation. Despite these benefits are available to companies, the analysis of the 2011 Report on the Use of Fiscal Incentives MCTI, Ministry of Science, Technology and Innovation - for the base year 2010, compared with 2009 - denotes that a growing number of organizations who are attracted by tax breaks and the available financial advantages, around 38% (thirty-eight percent), this percentage is still quite shy in terms of Brazil. From the 875 (eight hundred and seventy-five) requests that were sent, 236 (two hundred and thirty-six) were not granted. The paper analyzes the difficulties and gains ten (10) companies in the State of Rio Grande do Sul, Brazil's second most qualified region according to the Report of the MCTI. The methodology used was the phenomenological hermeneutic method, with a qualitative approach, and collecting data from semi-structured interviews in line with national legislation. The results are that the government should improve the communication of tax incentives for innovation projects with companies, because there is great variation in the way which companies get to know about the Good Law, there is a large number of different professionals involved both in the project and in the interpretation of legislation, and the program rules raise doubts in the companies, which causes insecurity and leads them to relinquish benefits due to the fear that projects will not be approved and consequent disallowance by the IRS. It was also evidenced that although there is a decrease in financial gain with the projects due to the impact of legal difficulties, participating companies have other ongoing projects, but for other reasons. Finally, through practical example, we compare two companies, one with and one without the tax benefits of the Good Law, in order to better understanding how to exploit the program as well its the financial results in an ideal level of application.

In 2013, 35% of the workforce was not engaged, which results in lack of productivity and loss of profitability for small business enterprises (SBEs). The purpose of this qualitative case study was to explore successful strategies that frontline leaders in a 4 generation, family-owned excavating business used to engage their frontline employees. The excavating business was started in 1947 by the father of the current business owners. William Kahn's employee engagement theory was the conceptual framework for this study. Data were collected through a focus group and direct observations of engagement during meetings and frontline areas from a population of 8 frontline leaders from construction work at an excavating business in Stephens City, Virginia. Data from the focus group and direct observations were thematically analyzed and then triangulated to ensure the trustworthiness of the interpretations. The 5 themes that emerged included: investing in sustainability, leading by example, providing clear and open communication, implementing a system of measurement, and developing a professional image. These themes could provide the basis for the area frontline leaders to improve the employee engagement level of their frontline employees. These findings could prompt what has been a missing dialogue of communication that could bridge the employee engagement gap between the area employees and employers. Social change implications of these findings could lead to productivity improvement that could contribute to the survival of SBEs and to the employment status of the community.

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

A Journey from the Wordless is a study of the development of the author’s own professional knowledge.  After an introductory chapter on theory and method, there are four chapters treating of the author`s academic training as an artist (Ch. 2), a presentation and analysis of his own exhibitions (Ch. 3), a chapter on a cooperative effort entitled Pompeii in Time and Space (Ch. 4), and a concluding discussion of the nature of personal knowledge (Ch. 5). Questions are addressed concerning artistic knowledge; how it is built and transferred and how it is developed and transformed. Since the author`s own professional knowledge is the object of the research, special perspectives on personal experience emerges that otherwise would be difficult to articulate. The thesis is also a contribution to the debate surrounding artistic research in the visual arts, focusing on the importance of reflection and analysis in art education and creative art work. / QC 20100824

This thesis proposes machine learning algorithms for processing geometry by example. Each algorithm takes as input a collection of shapes along with exemplar values of target properties related to shape processing tasks. The goal of the algorithms is to output a function that maps from the shape data to the target properties. The learned functions can be applied to novel input shape data in order to synthesize the target properties with style similar to the training examples. Learning such functions is particularly useful for two different types of geometry processing problems. The first type of problems involves learning functions that map to target properties required for shape interpretation and understanding. The second type of problems involves learning functions that map to geometric attributes of animated shapes required for real-time rendering of dynamic scenes. With respect to the first type of problems involving shape interpretation and understanding, I demonstrate learning for shape segmentation and line illustration. For shape segmentation, the algorithms learn functions of shape data in order to perform segmentation and recognition of parts in 3D meshes simultaneously. This is in contrast to existing mesh segmentation methods that attempt segmentation without recognition based only on low-level geometric cues. The proposed method does not require any manual parameter tuning and achieves significant improvements in results over the state-of-the-art. For line illustration, the algorithms learn functions from shape and shading data to hatching properties, given a single exemplar line illustration of a shape. Learning models of such artistic-based properties is extremely challenging, since hatching exhibits significant complexity as a network of overlapping curves of varying orientation, thickness, density, as well as considerable stylistic variation. In contrast to existing algorithms that are hand-tuned or hand-designed from insight and intuition, the proposed technique offers a largely automated and potentially natural workflow for artists. With respect to the second type of problems involving fast computations of geometric attributes in dynamic scenes, I demonstrate algorithms for learning functions of shape animation parameters that specifically aim at taking advantage of the spatial and temporal coherence in the attribute data. As a result, the learned mappings can be evaluated very efficiently during runtime. This is especially useful when traditional geometric computations are too expensive to re-estimate the shape attributes at each frame. I apply such algorithms to efficiently compute curvature and high-order derivatives of animated surfaces. As a result, curvature-dependent tasks, such as line drawing, which could be previously performed only offline for animated scenes, can now be executed in real-time on modern CPU hardware.

This thesis proposes machine learning algorithms for processing geometry by example. Each algorithm takes as input a collection of shapes along with exemplar values of target properties related to shape processing tasks. The goal of the algorithms is to output a function that maps from the shape data to the target properties. The learned functions can be applied to novel input shape data in order to synthesize the target properties with style similar to the training examples. Learning such functions is particularly useful for two different types of geometry processing problems. The first type of problems involves learning functions that map to target properties required for shape interpretation and understanding. The second type of problems involves learning functions that map to geometric attributes of animated shapes required for real-time rendering of dynamic scenes. With respect to the first type of problems involving shape interpretation and understanding, I demonstrate learning for shape segmentation and line illustration. For shape segmentation, the algorithms learn functions of shape data in order to perform segmentation and recognition of parts in 3D meshes simultaneously. This is in contrast to existing mesh segmentation methods that attempt segmentation without recognition based only on low-level geometric cues. The proposed method does not require any manual parameter tuning and achieves significant improvements in results over the state-of-the-art. For line illustration, the algorithms learn functions from shape and shading data to hatching properties, given a single exemplar line illustration of a shape. Learning models of such artistic-based properties is extremely challenging, since hatching exhibits significant complexity as a network of overlapping curves of varying orientation, thickness, density, as well as considerable stylistic variation. In contrast to existing algorithms that are hand-tuned or hand-designed from insight and intuition, the proposed technique offers a largely automated and potentially natural workflow for artists. With respect to the second type of problems involving fast computations of geometric attributes in dynamic scenes, I demonstrate algorithms for learning functions of shape animation parameters that specifically aim at taking advantage of the spatial and temporal coherence in the attribute data. As a result, the learned mappings can be evaluated very efficiently during runtime. This is especially useful when traditional geometric computations are too expensive to re-estimate the shape attributes at each frame. I apply such algorithms to efficiently compute curvature and high-order derivatives of animated surfaces. As a result, curvature-dependent tasks, such as line drawing, which could be previously performed only offline for animated scenes, can now be executed in real-time on modern CPU hardware.

International Atomic Energy Agency (IAEA) safeguards are one method of proliferation resistance which is applied at most nuclear facilities worldwide. IAEA safeguards act to prevent the diversion of nuclear materials from a facility through the deterrence of detection. However, even with IAEA safeguards present at a facility, the country where the facility is located may still attempt to proliferate nuclear material by exploiting weaknesses in the safeguards system. The IAEA's mission is to detect the diversion of nuclear materials as soon as possible and ideally before it can be weaponized. Modern IAEA safeguards utilize unattended monitoring systems (UMS) to perform nuclear material accountancy and maintain the continuity of knowledge with regards to the position of nuclear material at a facility. This research focuses on evaluating the reliability of unattended monitoring systems and integrating the probabilistic failure of these systems into the comprehensive probabilistic proliferation resistance model of a facility. To accomplish this, this research applies reliability engineering analysis methods to probabilistic proliferation resistance modeling. This approach is demonstrated through the analysis of a safeguards design for the Example Sodium Fast Reactor Fuel Cycle Facility (ESFR FCF). The ESFR FCF UMS were analyzed to demonstrate the analysis and design processes that an analyst or designer would go through when evaluating/designing the proliferation resistance component of a safeguards system. When comparing the mean time to failure (MTTF) for the system without redundancies versus one with redundancies, it is apparent that redundancies are necessary to achieve a design without routine failures. A reliability engineering approach to probabilistic safeguards system analysis and design can be used to reach meaningful conclusions regarding the proliferation resistance of a UMS. The methods developed in this research provide analysts and designers alike a process to follow to evaluate the reliability of a UMS.