浅谈英语学习动机对英语教学的启示

本文结合自身的教学实践与经验,探讨英语学习动机与英语学习效果之间的关系。     

University-Community Partnership Centers

Abstract

In academic settings, community research, and the service that goes along with it, is often not valued as much as other methods of research. The more qualitative and labor intensive nature of applied research often raises concerns about whether pre-tenured faculty can publish the sufficient quantity and quality of work necessary to achieve tenure. This paper describes successful collaborations through a university-based Community Partnership Center with members of community-based organizations in low-income inner-city neighborhoods, social work students, and faculty. Two case examples illustrate the co-authors' involvement with the Center as pre-tenured faculty. The article outlines the challenges and benefits of involvement with an established center for university-community partnerships. With careful planning and coordination, such centers can be excellent vehicles through which to achieve important mutual benefits for community-based organizations, student learning, and faculty responsibilities in research, teaching and service.     

Policy Diffusion and Policy Transfer in Comparative Welfare State Research

For many years, comparative welfare state research has followed a ‘methodological nationalism’ in the sense that countries were treated as independent units. Yet the recent ‘spatial turn’ in comparative politics has also influenced welfare state research. For some years now, the field has been witnessing a growing interest in questions about interdependencies and policy diffusion between countries. In this article, we provide a structured overview of the state of the art in the policy diffusion and transfer literature that deals specifically with social policy. We present and critically evaluate existing theoretical concepts and quantitative and qualitative methodological approaches that enable the analysis of interdependencies between countries. Moreover, we summarize the empirical findings of quantitative and qualitative studies on the diffusion and transfer of social policy, from some pioneering studies to the latest findings. Against this background we point out what we believe to be promising avenues for future research. We focus on five areas: theoretical work on the mechanisms underlying diffusion and transfer; methodological approaches; the impact of domestic institutions and policy characteristics on social policy diffusion and transfer; programme‐specific dynamics; and the systematic combination of horizontal and vertical interdependencies.     

高校校有企业改革思考

高校校有企业亟待改革 ,文章以党的十五大精神为指导 ,从校企改革的实际出发 ,对校企面临的问题、思想观念、改革的思路以及校企改革中应采取的措施等方面的问题进行了论述 ,旨在进一步推进校有企业改革的深入发展     

高校空巢老人问题研究

通过对某高校退休职工空巢老人的情况调研，揭示了目前高校空巢老人的基本情况，指出了对生活中体力活力不从心、遇到困惑问题没人商量、对生病的恐惧、无法排解思想上的空虚和孤独是高校空巢老人存在的主要困难与问题。提出了整合学校的资源，建立信息服务、社会服务、互助服务、救助服务等完备的服务体系，建立大学生志愿服务队伍、老年志愿服务队伍等对空巢老人提供具体服务，加强人文关怀以及努力营造全校性的尊老敬老的校园文化氛固等做好高校空巢老人帮扶工作的有效途径。     

高校学生工作要代表学生的根本利益

揭示高校学生工作代表学生根本利益的内涵 ,提出了高校学生工作代表学生根本利益的有效途径 :建立健全一套有效的规章制度 ;用“三个代表”重要思想教育学生 ;着重培养学生的实践能力和创新能力 ;致力于校园文化的软件建设 ;树立新型的“服务观”。     

从"中体西用"到改革开放--中国现代化历程的回顾与思考

自十九世纪中叶起,探索救国与强国之路,是摆在中国人面前的一个重大课题。本文从思想认识演化的角度,纵线条观察分析了从现代洋务运动到今天改革开放,中国人对现代化的认识过程,以期得到一些有益的历史借鉴和思想启迪。     

学习型企业知识创新的模式与推进因素

学习型企业的本质就是共同知识的创新。学习型企业的知识创新有组织化、外在化、组合化和内在化四种基本形式,要经历一个“自中向上再向下”的过程,它明显优于“自上而下”或“自下而上”的传统模式。推进学习型企业知识创新的主要因素有共同知识的发展远景、知识资产管理、员工自主性、不稳定状态、创造性无序、冗余信息、员工之间的关爱与信任。     

生产性企业组织学习最优控制模型及其理论分析

运用最优控制理论研究生产性企业组织学习活动的动态最优决策问题.以企业的概念性学习投资率和操作性学习投资率为决策变量,累积知识量、生产率、单位成本和废品率等为状态变量,计划期内的总利润为指标函数,建立了一个最优控制模型,其特点在于规范地描绘概念性学习和操作性学习对企业累积知识量、生产率、单位成本、质量以及企业利润的动态影响.根据生产性企业组织学习和生产经营的实际情况,提出了一些定量化表达的假设和定义.在假设和定义的基础上利用最大值原理分析了所建立的模型,获得了关于动态最优概念性学习投资策略和操作性学习投资策略性质的一些结论,将这些结论与实际问题相结合,指出了在生产性企业组织学习实践上的含义.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.