网格资源共享服务的层次结构模型研究

在提出网格环境下资源组织结构模型的基础上，构建网格资源服务模型。模型分为3个层次，分别为资源与用户层、服务域层及虚拟组织层。在探讨此模型的功能及特点后，指出今后的研究方向与重点。     

一种改进的文档层次分类方法

在文本分类的类别数量庞大的情况下，层次分类是一种有效的分类途径。但是，常用的层次分类法容易产生“阻滞”的问题。为了解决该问题，提出一种改进型层次分类方法，即限制投票法，并运用Rocchio分类器在基础教育学科资源环境下进行了实验。结果表明，该方法能够降低阻滞，具有较好的分类效果。     

分层教学法在高职学前教育专业舞蹈教学中的运用研究

舞蹈是高职学前教育专业一门重要的基础课程,它不仅要求学生掌握一定的舞蹈理论知识,而且要求学生有极强的实践操作技能,在教学上既重要又有较大的难度,而目前高职学前教育专业的学生主要来自普高和职高,他们的舞蹈基础差别很大,给教学增加了更大的困难,为了真正实现高职学前教育专业舞蹈教学的目标,使不同层次的学生都能在原有的基础上有较大的提高,为幼教事业培养优秀的舞蹈人才,必须实行分层教学。即：根据不同的教学对象,制定不同的教学目标和考核标准,选择不同的教学内容和教学方法。     

Prediction of College Major Persistence Based on Vocational Interests, Academic Preparation, and First-Year Academic Performance

We hypothesized that college major persistence would be predicted by first-year academic performance and an interest-major composite score that is derived from a student’s entering major and two work task scores. Using a large data set representing 25 four-year institutions and nearly 50,000 students, we randomly split the sample into an estimation sample and a validation sample. Using the estimation sample, we found major-specific coefficients corresponding to the two work task scores that optimized the prediction of major persistence. Then, we applied the estimated coefficients to the validation sample to form an interest-major composite score representing the likelihood of persisting in entering major. Using the validation sample, we then tested a theoretical model for major persistence that incorporated academic preparation, the interest-major composite score, and first-year academic performance. The results suggest that (1) interest-major fit and first-year academic performance work to independently predict whether a student will stay in their entering major and (2) the relative importance of two work task scores in predicting major persistence depends on the entering major. The results support Holland’s theory of person-environment fit and suggest that academic performance and interest-major fit are key constructs for understanding major persistence behavior.     

Use of hierarchical linear modeling and curriculum-based measurement for assessing academic growth and instructional factors for students with learning difficulties

The main purpose of this paper is to demonstrate how to apply the Hierarchical Linear Modeling (HLM) technique to multi-wave Curriculum-Based Measurement (CBM) measures in modeling academic growth and assessing its relations to student- and instruction-related variables. HLM has advantages over other statistical methods (e.g., repeated measures ANOVA, Structural Equation Modeling) in modeling academic growth. The advantages include allowing more flexible research designs in collecting multiple data points and estimating growth rates and their relations to correlates in more reliable, accurate ways. CBM, as a multi-wave progressmonitoring system, also has distinctive psychometric features that facilitate longitudinal research on academic skill development. These features include provision of multiple data points within short time periods, good validity and reliability, and sensitivity for detecting small degrees of change. Finally, research questions related to assessing the academic growth of students with learning difficulties and using assessment results to improve educational practices for them are discussed     

基于分治递归的层次聚类算法实现

层次聚类算法是一类重要的聚类分析方法。传统的层次聚类算法的时间为O（n2）空间复杂度很大，这使得聚类分析在大型数据集上的应用受到限制。该文提出一种基于分治递推改进算法，该算法将大大减少算法的时间复杂度和空间复杂度。     

Student Hits in an Internet‐Supported Course: How Can Instructors Use Them and What Do They Mean?

The world of education is changing as Web‐based technology and courseware are increasingly used for delivery of course material. In this environment, instructors may need new measures for determining student involvement, and ultimately student performance. This study examines whether hits to a Web site have any value for predicting student performance in a traditional course supported by Web activities. Total Hits at the end of the semester was used as one measure. Hit Consistency, determined by assigning a 0 when no hits occurred between class meetings and by assigning a 1 when one or more hits occurred between class meetings, was another. Hit Consistency was significantly correlated with course average (r= .37, p < .001) for 108 students in two course sections. Hit Consistency started to show a significant relationship with course average by the third week (or class). Total Hits was not found to significantly correlate with course average (r= .08, p > .05) at the end of the semester or during any week. These results suggest that students who consistently access a Web site will perform better than those who do not. When Hit Consistency and Total Hits were entered as independent variables into a stepwise regression with course average as the dependent variable, the model was enhanced by the addition of Total Hits after Hit Consistency was entered (R= .43, p < .001) . Hierarchical regression analysis in which cumulative grade point average was entered as the first controlling variable suggested that online access may go beyond the predictive value of achievement alone for predicting course performance with Hit Consistency appearing to be the dominant causal variable.     

初中数学分层教学初探

数学是初中阶段主要教学科目之一,初中生学好数学具有非常重要的意义。对于初中数学教学来说,传统的教学方法已经不能满足学生的需求,更不利于教学效果的提高。鉴于学生的差异性,教师要组织开展分层教学,让每个学生都能获得知识,促进教学效果的提高。本文主要分析了初中数学课堂上应用分层教学的策略。     

模糊数学理论在绿色制造模式下供应商选择与评价中的应用

针对绿色制造模式下多个供应商的选择问题,采用属性层次模型法与模糊综合评判法组合的综合评判法,对绿色制造模式下供应商的评价与选择进行了实证分析评价,从质量、成本、交货能力、技术能力、服务和环境6个方面建立了绿色制造模式下供应商评价指标体系。分析结果表明：属性层次模型法与模糊综合评价法组合的综合评价方法,对于绿色制造模式下供应商的选择有较好的适用性。     

多层线性模型的原理及应用

本文对多层线性模型 (HierarchicalLinearModels ,HLM)的理论缘起、应用范围以及其应用原理进行了阐述 ,在指出经典统计技术处理多层数据结构上的局限的同时 ,表明了多层线性模型在这方面的优越性。本文最后对多层线性模型的效果及局限性进行了简要分析。