School vision screening, ages 5-16 years:The evidence-base for content, provision and efficacy

The optometric profession in the UK has a major role in the detection, assessment and management of ocular anomalies in children between 5 and 16 years of age. The role complements a variety of associated screening services provided across several health care sectors. The review examines the evidence-base for the content, provision and efficacy of these screening services in terms of the prevalence of anomalies such as refractive error, amblyopia, binocular vision and colour vision and considers the consequences of their curtailment. Vision screening must focus on pre-school children if the aim of the screening is to detect and treat conditions that may lead to amblyopia, whereas if the aim is to detect and correct significant refractive errors (not likely to lead to amblyopia) then it would be expedient for the optometric profession to act as the major provider of refractive (and colour vision) screening at 5-6 years of age. Myopia is the refractive error most likely to develop during primary school presenting typically between 8 and 12 years of age, thus screening at entry to secondary school is warranted. Given the inevitable restriction on resources for health care, establishing screening at 5 and 11 years of age, with exclusion of any subsequent screening, is the preferred option. © 2004 The College of Optometrists.

Something to talk about: Content and Language Integrated Learning in Modern Languages in British Higher Education. A case study of German at Aston University

This thesis investigates Content and Language Integrated Learning (CLIL) in German undergraduate programmes in the UK. At its core is a study of how one German department integrates the teaching of language and content in its undergraduate programmes and how instructors and students experience this approach. This micro-context is embedded in the wider macro-context of UK Higher Education and subject to outside forces - be they political, economic, socio-cultural - whose effects will manifest in more or less obvious ways. Data was collected via an online survey of Heads of German at British universities to determine the status quo of CLIL in UK Higher Education and to investigate how certain institutional parameters determine the introduction of CLIL in Higher Education. This project employs a mixed-method case study approach and is based on student questionnaires and semi-structured interview with German teaching staff. The study brings to light a number of significant aspects. For example, contrary to popular belief, content provision in the L2 is rather common at British universities, which is currently not reflected in the research. Student data indicates that German students perceive clear advantages in the university’s approach to CLIL. They consider German-taught content classes challenging yet beneficial for their language development. Staff interviews have yielded intriguing information about perceived advantages and disadvantages of CLIL, about its implications for classroom practice, and about instructors’ attitude towards teacher training, which echo findings from similar investigations in European contexts. Finally, the results of the macro-analysis and the case study are compared and contrasted with findings from European research on ICLHE/CLIL to determine differences and similarities with the British context, a set of recommendations is made regarding CLIL practice at the case study institution, and some implications these indings may have for the future of CLIL in British higher education are discussed.

Capabilities and roles of enterprise wikis in organizational communication

How can technical communicators in organizations benefit from wiki technology? This article alerts technical communicators to the possibilities of wiki-based collaborative content creation. It analyzes 32 articles on the use of corporate wikis, and compares them to three media choice theories: media richness theory, theory of media synchronicity, and common ground theory.

Facilitating intuitive-guided learning in a serious game through integration with a learning content management system

Increased global uptake of entertainment gaming has the potential to lead to high expectations of engagement and interactivity from users of technology-enhanced learning environments. Blended approaches to implementing game-based learning as part of distance or technology-enhanced education have led to demonstrations of the benefits they might bring, allowing learners to interact with immersive technologies as part of a broader, structured learning experience. In this article, we explore how the integration of a serious game can be extended to a learning content management system (LCMS) to support a blended and holistic approach, described as an 'intuitive-guided' method. Through a case study within the EU-Funded Adaptive Learning via Intuitive/Interactive, Collaborative and Emotional Systems (ALICE) project, a technical integration of a gaming engine with a proprietary LCMS is demonstrated, building upon earlier work and demonstrating how this approach might be realized. In particular, how this method can support an intuitive-guided approach to learning is considered, whereby the learner is given the potential to explore a non-linear environment whilst scaffolding and blending provide guidance ensuring targeted learning objectives are met. Through an evaluation of the developed prototype with 32 students aged 14-16 across two Italian schools, a varied response from learners is observed, coupled with a positive reception from tutors. The study demonstrates that challenges remain in providing high-fidelity content in a classroom environment, particularly as an increasing gap in technology availability between leisure and school times emerges.

Online grocery provision resistance:understanding urban (non-)collaboration and ambiguous supply chain environments

This chapter investigates the resistance by institutional actors in ambiguous supply chain environments for online grocery provision. Recent studies have shown that significant shifts in urban geographies are increasing consumers' expectations of online retail provision. However, at the same time there is also growing evidence that the collaborative practice in online grocery provision within the urban supply chains is resisted. That these trends are found despite growing demand of online provision highlights both the difficulty of bringing geographically dispersed supply partners together and the problems associated with operating within and across ambiguous environments. Drawing upon twenty-nine in-depth interviews with a range of institutional actors, including retail, logistics, and urban planning experts within an urban metropolis in an emerging market, we detail the different ways that collaboration is resisted in online retail provision. Several different patterns of resistance were identified in (non-) collaboration notably, ideological, functional, regulatory and spatial. © 2011, IGI Global. C.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.