试论文化创意产业化背景下的艺术设计教育发展方向

创意产业的发展无不得力于创意人才的教育与培养,大批量创意人才的教育与培养将会是我国未来创意产业获得大发展的前提。这对于培养文化创意产业人才的艺术设计教育事业来说是机遇,也是挑战。本文将从文化创意产业化的背景形势,来思考我国艺术设计教育的现状和我国艺术设计教育应面临的转变。     

基于活动的网络课程设计框架及其应用研究

与美国MIT开展的OCW运动相对应,中国从21世纪起在E-Learning资源建设方面走了一条网络课程开发的道路.在网络课程开发中,人们往往注重教学内容的设计和展示,而对如何设计和支持教与学的活动较为忽略.本文重新审视网络课程的结构框架,从学习活动的视角出发,提出了一个以活动为中心的"角色-活动-环境"(简称R-A-E)网络课程设计框架,并依据这个框架介绍如何围绕教学需求、针对课程特点设计网络课程.     

设计性物理实验的探索与实践

结合辽宁工学院物理实验教学改革实践,从设计性物理实验的基础要求、层次设计、教学组织方式等方面初步探讨如何开展设计性物理实验。     

"包装结构设计"课程教学的实践与探讨

文章讨论了“包装结构设计”课程的特点及传统教学存在的问题,然后在教学内容和教学方法上提出了若干改革建议。     

体验设计与故事主题

在情感化、用户中心设计日趋深入人心时,体验设计走进了人们的视野。这种以故事化主导设计、引领消费者感受的"新"设计,常常有一个鲜明的主题。文章探讨的是在产品设计中,如何给产品起个让人怦然心动的主题,从而让人从产品中获得独特的文化体验。     

Invariant co-ordinate selection

Summary.  A general method for exploring multivariate data by comparing different estimates of multivariate scatter is presented. The method is based on the eigenvalue–eigenvector decomposition of one scatter matrix relative to another. In particular, it is shown that the eigenvectors can be used to generate an affine invariant co-ordinate system for the multivariate data. Consequently, we view this method as a method for invariant co-ordinate selection . By plotting the data with respect to this new invariant co-ordinate system, various data structures can be revealed. For example, under certain independent components models, it is shown that the invariant co- ordinates correspond to the independent components. Another example pertains to mixtures of elliptical distributions. In this case, it is shown that a subset of the invariant co-ordinates corresponds to Fisher's linear discriminant subspace, even though the class identifications of the data points are unknown. Some illustrative examples are given.     

Bayesian credible intervals for response surface optima

In response surface methodology, one is usually interested in estimating the optimal conditions based on a small number of experimental runs which are designed to optimally sample the experimental space. Typically, regression models are constructed from the experimental data and interrogated in order to provide a point estimate of the independent variable settings predicted to optimize the response. Unfortunately, these point estimates are rarely accompanied with uncertainty intervals. Though classical frequentist confidence intervals can be constructed for unconstrained quadratic models, higher order, constrained or nonlinear models are often encountered in practice. Existing techniques for constructing uncertainty estimates in such situations have not been implemented widely, due in part to the need to set adjustable parameters or because of limited or difficult applicability to constrained or nonlinear problems. To address these limitations a Bayesian method of determining credible intervals for response surface optima was developed. The approach shows good coverage probabilities on two test problems, is straightforward to implement and is readily applicable to the kind of constrained and/or nonlinear problems that frequently appear in practice.     

Power and perceptions in participatory monitoring and evaluation

This paper examines the assumptions that commonly underpin the design of participatory monitoring and evaluation (PM&E) within development programmes through the examination of a case study of a large rural development programme in Uganda. This case study reveals a mismatch between programme assumptions and participant perceptions, which stymied the implementation of PM&E. In this case, PM&E was based on the assumptions that programme and participant goals were compatible, participants were willing to volunteer and engage themselves for the greater good of their communities, and information sharing and communication was fairly free and open. However, farmers within the programme felt that the bureaucratic and accountability requirements of the programme were not their concern, and were acutely aware of power differences between farmers and programme officials, and between farmers of varying status. The key lesson to be drawn from this case is the need for a heightened awareness of power dynamics and political factors in the design of PM&E.     

USING CONJOINT ANALYSIS TO PREDICT TRIAL AND REPEAT-PURCHASE PATTERNS OF NEW FREQUENTLY PURCHASED PRODUCTS*

A new approach is proposed to evaluate new-product opportunities. This approach uses the distribution of brand-purchase probability of the new product over a population of potential customers and the outputs from conjoint analysis. The heterogeneous distribution of brand-purchase probability is expressed by a beta binomial brand-choice model compounded with a negative binomial product-class purchase-incidence model. The resulting model provides a way to predict trial and repeat-purchase patterns of new-product concepts. The paper discusses the development of the model. It also discusses issues of measurement, estimation, testing, and implementation of the proposed approach based on actual empirical data.     

Extended scaled prediction variance optimality for modified central composite design

Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV.