907 resultados para early years learning framework (EYLF)


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This research investigated the implementation of the Early Years Literacy Program in Victoria, Australia. Victoria was chosen for its literacy quality program. Participants interviewed were teachers, literacy coordinators and school principals. The data was transcribed and cross analysed. The implication to the Indonesian context will be possible but problematic.

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This is the October 2013 Interim Report as part of the 'Early Years Education in the Primary Years Programme (PYP): Implementation Strategies and Programme Outcomes' Project submitted to the International Baccalaureate Organisation. This project is investigating implementation strategies and outcomes in Early Years Education in the Primary Years Programme of the IB, through a mixed-methods approach using both quantitative and qualitative data. It is based on intensive case studies of five Early Years Stage programmes within the IB Primary Years Programme, at three sites in Australia and two sites in Singapore.

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This article discusses the design of social networking sites created through a PhD action research study. Social and participatory media was used as an active, flexible and motivating learning management system. The study investigated ways in which a social learning framework could be designed for students aged 13 to 16 and aimed to encourage student knowledge growth through peer-to-peer interaction while supporting both formal and informal learning. New literacies and multimodality were infused into the design. It was found that the practitioner-researcher’s cycles of planning, acting, observing and reflecting, action research, provided a mechanism for scaffolding the redesign of curriculum content and instruction. Social media in education can be dynamic, interactive and appreciated (SMEDIA) by the students.

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Social and participatory media offer a plethora of ways for students to communicate, collaborate, and learn in schools. Using a social learning approach, Casey (2013a) investigated ways that social media could be integrated into Australian public high school classrooms to enhance student learning. In the process, she developed a social learning framework as discussed in Casey (2013b). Similarly, Davidson-Shivers and Hulon (2013; Hulon & Daidson-Shivers, 2013) suggest ways to employ ID principles to prepare college instructors and pre-service teachers to integrate technology into classrooms. Prior to that, Davidson-Shivers with Rasmussen (2006) developed an instructional design (ID) model for creating effective Web-based learning environments. Through collaboration, Casey and Davidson-Shivers consider a wide range of social learning and instructional design principles and approaches to help develop frameworks for new media integration that can work within varying levels of education.

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This book has been written for all leaders and aspiring leaders with responsibility for improving the quality of early years settings. It brings together current research and effective practice to provide you with the knowledge, understanding and skills you need to motivate and get the best from your team, identify and develop your personal leadership style and clarify your vision for quality.

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Using a child's expression that illustrates his mental image of constituted relations of living things, the author conceptualizes relationality, an interrelated view of being, and its importance for early childhood education. The difference between relation and interaction, and the significance of inter-human relationship are discussed as significant aspects for early childhood teachers to understand as they work with children. In particular, this paper seeks to provide insights into the potential contribution of relationality towards early childhood teaching practice.

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THIS STUDY INVESTIGATED THE teaching and learning approaches of three Victorian early childhood kindergarten teachers to science education, and how they used the Victorian Early Years Learning and Development Framework (VEYLDF) to support them in the development of science in their curriculum. A qualitative, collective case study was designed to investigate how the participants introduced and explored science in their curriculum through two face-to-face individual, semi-structured interviews, separated by a week during which they completed a reflective journal focusing on science in their curriculum. The findings revealed teachers’ own negative school experiences of science education and an overall lack of confidence in their current science knowledge impacts on science in their curriculum. Conversely the findings also revealed instances of science learning anddiscovery, as well as a desire by the early childhood teachers to enhance science education in their curriculum

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The aim of this article is to examine current national early years’ policy reform, which emphasises the importance of service integration, national quality standards and a quality knowledge base for educators concerning the provision of early childhood education and care. Using Queensland, Australia, as an example, a policy discourse analysis identifies two problematics of implementing current national policy – the early childhood education and care problematic and the integration problematic. The article argues that speedy implementation of a national policy in order to meet national targets has unintended consequences for the knowledge base of educators and the possibility of collaboration within service provision. Although government commitment in this area is evident, these consequences and the current difficulties surrounding integration are the result of the lack of a specific integration strategy, and government investment focussed on the development of an integrated workforce.

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© 2014, © 2014 Australian Association of Social Workers. Abstract: Mapping and evaluating a student's progress on placement is a core element of social work education but there has been scant attention to indicate how to effectively create and assess student learning and performance. This paper outlines a project undertaken by the Combined Schools of Social Work to develop a common learning and assessment tool that is being used by all social work schools in Victoria. The paper describes how the Common Assessment Tool (CAT) was developed, drawing on the Australian Association of Social Work Practice Standards, leading to seven key learning areas that form the basis of the assessment of a student's readiness for practice. An evaluation of the usefulness of the CAT was completed by field educators, liaison staff, and students, which confirmed that the CAT was a useful framework for evaluating students' learning goals. The feedback also identified a number of problematic features that were addressed in a revised CAT and rating scale.

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Learning from small number of examples is a challenging problem in machine learning. An effective way to improve the performance is through exploiting knowledge from other related tasks. Multi-task learning (MTL) is one such useful paradigm that aims to improve the performance through jointly modeling multiple related tasks. Although there exist numerous classification or regression models in machine learning literature, most of the MTL models are built around ridge or logistic regression. There exist some limited works, which propose multi-task extension of techniques such as support vector machine, Gaussian processes. However, all these MTL models are tied to specific classification or regression algorithms and there is no single MTL algorithm that can be used at a meta level for any given learning algorithm. Addressing this problem, we propose a generic, model-agnostic joint modeling framework that can take any classification or regression algorithm of a practitioner’s choice (standard or custom-built) and build its MTL variant. The key observation that drives our framework is that due to small number of examples, the estimates of task parameters are usually poor, and we show that this leads to an under-estimation of task relatedness between any two tasks with high probability. We derive an algorithm that brings the tasks closer to their true relatedness by improving the estimates of task parameters. This is achieved by appropriate sharing of data across tasks. We provide the detail theoretical underpinning of the algorithm. Through our experiments with both synthetic and real datasets, we demonstrate that the multi-task variants of several classifiers/regressors (logistic regression, support vector machine, K-nearest neighbor, Random Forest, ridge regression, support vector regression) convincingly outperform their single-task counterparts. We also show that the proposed model performs comparable or better than many state-of-the-art MTL and transfer learning baselines.