Discrete Choice Modeling in Education: An Innovative Method to Assess Teaching Practices

Self‐reported measures are an easy, time‐efficient, and low‐cost way to collect research data compared to other assessment methods. They are, however, characterized by several limitations regarding the quality and the clarity of the data they produce, especially when they are implemented in education. The main purpose of the current study was to use a method called discrete choice modeling (DCM) in education, in order to develop a self‐reported instrument that will reduce the bias for socially desirable responses and to assess teachers’ practices in physical activity. DCM method has the ability to overcome the respondents’ tendency to answer in a socially desirable way. A nationally representative sample of 531 Greek early educators participated in this study and were administered a self‐assessment questionnaire based on DCM. Results showed that the DCM based newly developed instrument manages to assess effectively educators’ practices and provided evidence of the applicability of the method in education. Further implications and future recommendations regarding the effective application of DCM in education are discussed.     

开展数学建模教学促进基础教育改革

随着基础教育课程改革的深入,在中小学开展数学建模教学已经成为一项重要内容。本文试图从数学建模教学的意义、教学环节等几方面揭示数学建模教学对于基础教育改革的重要性。     

体育多种预测方法的比较研究

运用3种不同的方法所建立的多元回归预测模型,其回归预测值的准确度均达到了99.26%以上,其回归的预测值与实际值的方差分析检验均呈无差异性,P》0.50水平.说明3种不同的方法所建立的预测模型的预测值均具有显著性意义.在3种建模方法中,均有评价作用,且其评价目的也不尽相同.在体育运动实践中可依据研究的目的,选择建模方法,以提高体育预测研究的效益.     

二次文献发表延时过程的数学模拟及其分析

二次文献发表延时过程模型的建立 ,为研究利用二次文献查新的漏检问题提供了定量化工具。文献查新 ,不仅要检索二次文献数据库 ,还要检索学科范围内近期的核心期刊和相关期刊的一次文献。它也为二次文献检索数据库建立了定量化研究方法。图 2。参考文献 1。     

Perspectives on inclusive education: learning from each other

Many courses of study are currently available that address inclusive education and, increasingly, distance education is seen as a flexible and appropriately inclusive way to deliver such courses. In this article a team of colleagues, Kieron Sheehy, Jonathan Rix, Melanie Nind and Katy Simmons, discusses the development of an Open University course, E243 Inclusive Education: Learning from Each Other, which was launched in February 2004. The team reflects on their own process of learning from each other and from the rich network of people involved, mirroring some of the course themes in their own journeys. Collaborative learning became a key theme within the course, both as a method for teaching within the ‘inclusive classroom’ and also as the process for producing a course in a contested and challenging area. This experience allowed the team to change and develop their own perspectives on important issues and, it is suggested, allowed the course to include ways in which students could be supported in having the same opportunity.     

基于点云数据的复杂型面实体建模研究

文章针对复杂型面产品的特点,从工业产品设计的实际应用出发,提出将正向设计的尺寸和定位关系设计方法和逆向设计的复杂曲面设计方法有效结合起来,基于点云数据实现产品的实体模型重建。以摩托车覆盖件为例,利用逆向工程软件从点云数据中提取和重构特征曲线与曲面,然后将这些几何元素信息在正向设计系统中结合特征建模方法创建整体三维CAD模型。     

基于属性值偏好矩阵的协同过滤推荐算法

传统的协同过滤推荐算法面临用户评分数据稀疏性和冷启动问题的挑战.针对上述问题,提出了基于属性值偏好矩阵的协同过滤推荐算法,首先采用奇异值分解(SVD)对用户-项目评分矩阵降维得到目标用户的初始邻居用户集,生成新的用户-项目评分矩阵;然后将用户评分映射到相应的项目属性值上,生成每个用户的属性值偏好矩阵,并基于属性值偏好矩阵进行用户相似性度量,从而缓解了评分数据稀疏性;将新项目的属性值与用户的属性值偏好矩阵进行匹配,从而找出匹配度最高的前N个用户作为新项目的推荐受众.实验结果表明了该算法的有效性.     

基于组合加权评分的Item-based协同过滤算法

针对Item-based协同过滤算法中用户评分数据稀疏性严重影响推荐质量的问题,提出一种基于组合加权评分的Item-based协同过滤算法,以用户评分项并集作为用户相似性计算基础,并提出一种组合加权评分方法来对并集中的未评分项进行计算和填补,从而降低了数据稀疏性。实验结果表明该算法能有效提高推荐质量。     

现代图书馆的协同服务

在简述现代图书馆的特征和服务模式的基础上，论述了现代图书馆的各类协同服务，并从三个方面简要描述了协同服务的行为评价。     

Two-stage statistical language models for text database selection

As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.