779 resultados para Early Years Learning Framework


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A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.

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This paper presents a project for providing the students of Structural Engineering with the flexibility to learn outside classroom schedules. The goal is a framework for adaptive E-learning based on a repository of open educational courseware with a set of basic Structural Engineering concepts and fundamentals. These are paramount for students to expand their technical knowledge and skills in structural analysis and design of tall buildings, arch-type structures as well as bridges. Thus, concepts related to structural behaviour such as linearity, compatibility, stiffness and influence lines have traditionally been elusive for students. The objective is to facilitate the student a teachinglearning process to acquire the necessary intuitive knowledge, cognitive skills and the basis for further technological modules and professional development in this area. As a side effect, the system is expected to help the students improve their preparation for exams on the subject. In this project, a web-based open-source system for studying influence lines on continuous beams is presented. It encompasses a collection of interactive user-friendly applications accessible via Web, written in JavaScript under JQuery and Dygraph Libraries, taking advantage of their efficiency and graphic capabilities. It is performed in both Spanish and English languages. The student is enabled to set the geometric, topologic, boundary and mechanic layout of a continuous beam. While changing the loading and the support conditions, the changes in the beam response prompt on the screen, so that the effects of the several issues involved in structural analysis become apparent. This open interaction with the user allows the student to simulate and virtually infer the structural response. Different levels of complexity can be handled, whereas an ongoing help is at hand for any of them. Students can freely boost their experiential learning on this subject at their own pace, in order to further share, process, generalize and apply the relevant essential concepts of Structural Engineering analysis. Besides, this collection is being added to the "Virtual Lab of Continuum Mechanics" of the UPM, launched in 2013 (http://serviciosgate.upm.es/laboratoriosvirtuales/laboratorios/medios-continuos-en-construcci%C3%B3n)

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The purpose of this paper is to introduce a framework for applying positive psychology in elementary classrooms. The target age group is children in grades K-3 (ages 5 to 8) because this age group can benefit the most from an early introduction to strategies that promote positive development (Cowne & Hightower, 1989; White, 1996). The following sections will: (a) introduce constructs of positive psychology; (b) present developmental data on how these constructs can be applied to children ages 5 to 8 years; (c) present ideas for incorporating positive psychology practice into K-3 classrooms; (d) present strategies for incorporating positive psychology with multicultural considerations; and (e) present ideas on how to implement strategies based on positive psychology that are compatible with grade level standards and sociopolitical teaching expectations.

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In this paper we introduce a probabilistic approach to support visual supervision and gesture recognition. Task knowledge is both of geometric and visual nature and it is encoded in parametric eigenspaces. Learning processes for compute modal subspaces (eigenspaces) are the core of tracking and recognition of gestures and tasks. We describe the overall architecture of the system and detail learning processes and gesture design. Finally we show experimental results of tracking and recognition in block-world like assembling tasks and in general human gestures.

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In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

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The present study examined the predictive effects of gender, intellectual ability, self-concept, motivation, learning strategies, popularity and parent involvement on academic achievement. Hiearchical regression analysis were performed with six steps in which each variable was included, among a sample of 1398 high school students (mean age = 12.5; standard deviation = .67) of eight education centers from the province of Alicante (Spain). The results revealed significant predictive effects of all of the variables, explaining 59.1% of the total variance.

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It is almost 20 years since a series of conferences known as CULT (Corpus Use and Learning to Translate) started. The first and second took place in Bertinoro, Italy, back in 1997 and 2000, respectively. The third was held in 2004 in Barcelona, and the fourth in 2015 in Alicante. Each was organized by a few enthusiastic lecturers and scholars who also happened to be corpus lovers. Guy Aston, Silvia Bernardini, Dominic Stewart and Federico Zanettin, from the Universitá di Bologna; Allison Beeby, Patricia Rodríguez-Inés and Pilar Sánchez-Gijón, from the Universitat Autònoma de Barcelona; and Daniel Gallego-Hernández, from the Universidad de Alicante, organized CULT conferences in the belief that spreading the word about the usefulness of corpora for teaching and professional translation purposes would have positive results.

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This dissertation includes two studies. Study 1 is a qualitative case study that describes enactment of the main components of a high fidelity Full-Day Early Learning Kindergarten (FDELK) classroom, specifically play-based learning and teacher-ECE collaboration. Study 2 is a quantitative analysis that investigates how effectively the FDELK program promotes school readiness skills, namely self-regulation, literacy, and numeracy, in Kindergarteners. To describe the main components of an FDELK classroom in Study 1, a sub-sample of four high fidelity case study schools were selected from a larger case study sample. Interview data from these schools’ administrators, educators, parents, and community stakeholders were used to describe how the main components of the FDELK program enabled educators to meet the individual needs of students and promote students’ SR development. In Study 2, hierarchical regression analyses of 32,207 students’ self-regulation, literacy, and numeracy outcomes using 2012 Ontario Early Development Instrument (EDI) data revealed essentially no benefit for students participating in the FDELK program when compared to peers in Half-Day or Alternate-Day Kindergarten programs. Being older and female predicted more positive SR and literacy outcomes. Age and gender accounted for limited variance in numeracy outcomes. Results from both studies suggest that the Ontario Ministry of Education should take steps to improve the quality of the FDELK program by incorporating evidence-based guidelines and goals for play, reducing Kindergarten class sizes to more effectively scaffold learning, and revising curriculum expectations to include a greater focus on SR, literacy, and numeracy skills.