基于数据立方体的网络教学评价模型

网络教学评价是网络教学的一个重要环节。提出了基于数据立方体的网络教学评价模型，以学生、教师为评价对象．构建相应的数据立方体。学生学习行为数据立方体及教师教学行为数据立方体均由六个维度构成，以访问时间作为主要的度量值。并讨论了模型中涉及的关键技术。     

网络教学中的评价系统的设计与研究

网络教学评价的目的是有效地督促学生自学,发展学生各方面的能力。因此,明确网络教学的评价要素以及如何实现评价,对网络教学地不断完善有着突出的作用。本文以建构网络教学下的学员、教师以及教学资源作为评价要素并给出相应的网络教学评价模型,并介绍了一些可实现的网络教学评价系统。     

数据立方体格的图结构特性研究

数据立方体是数据仓库的核心数据模型,其对应的数据立方体格因保留上卷下钻的语义关系而有利于查询和导航。目前对数据立方体内部结构特性尚未展开系统的研究。为此,将数据立方体格视为图数据,研究数据立方体格的结构特性和解析模型。分析结果表明,不同于随机网络和复杂网络的模型,数据立方体格在度分布、聚集系数、平均最短路径等方面具有不同的结构特性。根据上述特性进一步建立数据立方体格的解析模型。     

基于OLAP技术的教学诊断与评价模型

结合首师大虚拟学习社区网络教学支撑平台，提出了一个基于联机分析技术（OLAP）的教学诊断与评价模型，文中使用DMQL语言给出了一个由学生，知识点，时间和认知技能4个维度构成的数据立方体，以及利用OLAP的上卷，下钻、切块和切片等技术实现对该数据方体进行数据挖掘的解决方案，提出的基于OLAP技术的数据挖掘方案，可以在教学诊断与评价中解决诸如判断学习者学习难点，了解学习者集成或个人的学习特征，以及获得学习者认知过程等问题，为实现因材施教的个性化教学和以学生为中心的自主学习提供支持与服务。     

基于离散泊松混合模型的教学评价数据建模

分析学生在教学评价系统中对于教师的评价数据有助于教师了解学生对授课教师的真实态度,总结教学经验,改进后续的教学方式,提高教学质量。但是进行教学评价时,学生中可能会出现随意评价或者恶意评价等问题,导致评价数据中包含大量噪声,造成反馈数据的不理想。因此,提出了一种离散泊松混合模型来对包含噪声的学生的评价数据进行建模,将混合模型中的每一个离散泊松分量对应一类具有相似评价模式的学生,借由离散泊松分布中的模型参数来表示对应评价模式中的评价分数。通过构建对数似然函数来衡量混合模型和评价数据的拟合程度,采用梯度下降的方法求解拟合程度最高的模型参数,找到学生对于教师的真实评价,保证教学评价系统中师生间的有效沟通。大量实验结果表明,模型能够快速准确地从含有噪声的评价数据中识别出具有不同评价模式的学生,掌握学生对于教师的真实评价情况。     

教学评价与知识发现系统的设计

提出了一个基于联机分析技术（OLAP）的教学评价与知识发现,给出了由学生,知识点和类别等构成的六个维度的数据立方体以及利用OLAP技术和关联规则对该数据立方体进行数据挖掘的解决方案。利用上述方法对学生的考试系统进行挖掘,得出有用的结论,从而指导学校的教学工作。     

大数据在教育行为变革中的应用研究

教育行为变革牵涉到学生学习、教师教学、教育管理等行为变革。其目的在于提高学生学习效率和学校教学、 管理质量。教学变革与教育管理变革往往基于学生学习行为基础数据。随着信息技术的发展,利用大数据技术对学生学习行 为、教师教学反馈评价、教育管理等领域产生的海量数据进行有效获取、提炼、加工并以有效视图显示给教师及教育教学管理 者,为教育行为变革提供有效数据,从而确保教育教学及管理变革起点正确。     

路径-维度GraphOLAP大规模多维网络并行分析框架

现实生活中大量数据都可以使用多维网络进行建模,如何更好地对多维网络进行分析至今仍是研究人员关注的重点.OLAP（联机分析处理）技术已被证实是对多维关系数据进行分析的有效工具,但应用OLAP技术管理和分析多维网络数据以支持有效决策仍旧是一项巨大的挑战.本文设计并提出了一种新的图立方体模型：路径-维度立方体,并针对提出的立方体模型将物化过程划分为关系路径物化与关联维度物化两部分,分别提出了物化策略并基于Spark框架设计了相关算法;在此基础上,我们针对网络数据设计并细化了相关的GraphOLAP（图联机分析处理）操作,丰富了框架的分析角度,提高了对多维网络的分析能力;最后,在Spark上实现了相关算法,通过对多个真实应用场景中的数据构建多维网络,在分析框架上进行了分析,实验表明我们提出的图立方体模型和物化算法具有一定有效性和可扩展性.     

数据挖掘在E-learning教育系统中的应用研究

在分析了当前众多E-learning系统存在的问题后,提出了将数据挖掘技术引入到E-learning中。提出了一种基于代理的系统架构模型,在此基础上介绍了以学生学习评价数据为实体的多维数据立方体模型的方法,并用Microsoft SQL Server 2005中提供的联机分析处理分析服务工具实现了此方法。     

以学生为中心的高职课堂教学评价指标体系研究

课堂教学质量评价是提高课堂教学质量的重要保障,随着以学生为中心的新的教学模式的不断出现,构建以学生为中心的课堂教学质量评价体系是十分必要的.结合高职院校课堂教学改革实践,从教师和学生两个维度出发,在传统的"以教论教"的基础上提出一种TLI模型,该模型指出以学生为中心的课堂教学质量评价应该包含教师的教学行为、学生的学习行...     

A Data Cube Model for Prediction-Based Web Prefetching

Reducing the web latency is one of the primary concerns of Internet research. Web caching and web prefetching are two effective techniques to latency reduction. A primary method for intelligent prefetching is to rank potential web documents based on prediction models that are trained on the past web server and proxy server log data, and to prefetch the highly ranked objects. For this method to work well, the prediction model must be updated constantly, and different queries must be answered efficiently. In this paper we present a data-cube model to represent Web access sessions for data mining for supporting the prediction model construction. The cube model organizes session data into three dimensions. With the data cube in place, we apply efficient data mining algorithms for clustering and correlation analysis. As a result of the analysis, the web page clusters can then be used to guide the prefetching system. In this paper, we propose an integrated web-caching and web-prefetching model, where the issues of prefetching aggressiveness, replacement policy and increased network traffic are addressed together in an integrated framework. The core of our integrated solution is a prediction model based on statistical correlation between web objects. This model can be frequently updated by querying the data cube of web server logs. This integrated data cube and prediction based prefetching framework represents a first such effort in our knowledge.     

基于数据仓库的Web日志挖掘技术研究

Web日志挖掘是目前Web挖掘研究的一个重点.针对Web日志挖掘中存在的问题,给出了基于数据仓库技术的Web日志挖掘方案,就数据预处理、数据立方体设计及数据挖掘技术的应用进行了较为深入的探讨.并以一个Web站点日志为例,详细阐述了Web日志数据预处理、Web日志立方体设计以及数据挖掘算法的实现过程,并实现了一个Web日志多维数据集,能够有效解决Web日志分析中的难题.     

基于XML数据立方的面向对象扩展

本文是对基于XML的数据立方数据模型的面向对象的实现，通过对数据仓库技术，面向对象技术和XML技术的结合的探讨，扩展了XML Schema，从而为基于WEB数据仓库的应用提供了一种新的表示和实现方法，解决了数据仓库中模式演化所带来的重组问题，通过与面向对象技术的结合使用，把数据仓库变成为数据立方和OLAP方法的仓库，这种数据仓库的实现方案，保证了数据仓库系统的稳定性，灵活性和可扩展性，适应了新一代WEB应用的需要。     

Intervention and strategy analysis for web group-learning

Abstract Owing to the lack of face-to-face interactions, students using a web-based learning system are likely to study alone and with relatively little classmate support and pressure. Teachers in a web-based learning system may apply a group-learning model to overcome this problem. Teachers first need to organise, manage and monitor the group learning and they must take appropriate actions based on teaching strategies to improve the learning achievements of the students. To perform these tasks effectively, teachers must obtain relevant information by analysing the huge volume of web-access logs or by monitoring web interactions. This paper presents novel methodologies for developing instruments to assist teachers in performing intervention and strategy analysis. The proposed methodologies apply data mining tools provided by existing database management systems. Database techniques, including the multi-dimensional cube, are then applied to make student web logs meaningful and helpful to teachers in managing group learning. The associate rule mining tool is finally employed to assist teachers in analysing their pedagogical strategies. These tools relieve teachers of tedious data collection and analysis, allowing them to focus on managing the groups to promote students' learning achievement.     

微机接口技术课程教学改革探讨

针对“微机接口技术”课程,以提高学生的综合能力、加强素质教育为主线,根据教学要求和课程特点,在教学内容、教学方法和手段等方面进行了探索,提出了课程建设的目标、主要内容、教学模式和实践性教学环节的设计,开发了网上课程辅助教学系统。利用虚拟实验,可以达到真实实验的效果,实验可以随时随地进行,从而提高了学习效率并降低了设备成本。     

基于立方体染色的排课表模型

根据立方体染色思想,建立排课表模型,引入关联锁原理,将排课表约束条件转化为立方体点线面关系,增强该模型的收敛性,从而更好地解决排课问题。分析结果表明,在教师、课程、学生等信息已知的情况下,该模型可给出教学资源消耗的最小值,达到合理利用教学资源的目的。     

Web Communities: Models and Algorithms

In the last few years, a lot of research has been devoted to developing new techniques for improving the recall and the precision of current web search engines. Few works deal with the interesting problem of identifying the communities to which pages belong. Most of the previous approaches try to cluster data by means of spectral techniques or by means of traditional hierarchical algorithms. The main problem with these techniques is that they ignore the relevant fact that web communities are social networks with distinctive statistical properties.In this paper we analyze web communities on the basis of the evolution of an initial set of hubs and authoritative pages. The evolution law captures the behaviour of page authors with respect to the popularity of existing pages for the topics of interest. Assuming such a model, we have found interesting properties of web communities. On the basis of these properties we have proposed a technique for computing relevant properties for specific topics. Several experiments confirmed the validity of both the model and identification method.     

Ranking Pages by Topology and Popularity within Web Sites

We compare two link analysis ranking methods of web pages in a site. The first, called Site Rank, is an adaptation of PageRank to the granularity of a web site and the second, called Popularity Rank, is based on the frequencies of user clicks on the outlinks in a page that are captured by navigation sessions of users through the web site. We ran experiments on artificially created web sites of different sizes and on two real data sets, employing the relative entropy to compare the distributions of the two ranking methods. For the real data sets we also employ a nonparametric measure, called Spearman's footrule, which we use to compare the top-ten web pages ranked by the two methods. Our main result is that the distributions of the Popularity Rank and Site Rank are surprisingly close to each other, implying that the topology of a web site is very instrumental in guiding users through the site. Thus, in practice, the Site Rank provides a reasonable first order approximation of the aggregate behaviour of users within a web site given by the Popularity Rank.     

任务驱动教学法在“网页编程基础”教学中的应用

本文研究讨论了如何利用任务驱动教学法在授课过程中结合有针对性的教学任务培养非计算机专业学生的网页开发能力。针对网页编程的课程特点,文章将其教学过程划分为三个阶段,并给出了一个贯穿整个教学过程的任务实例。并阐述了该教学模式中的任务设计,任务实施的过程,验证了该教学模式的可行性。     

Cardinality pruning and language model heuristics for hierarchical phrase-based translation

In this article we present two novel enhancements for the cube pruning and cube growing algorithms, two of the most widely applied methods when using the hierarchical approach to statistical machine translation. Cube pruning is the de facto standard search algorithm for the hierarchical model. We propose to adapt concepts of the source cardinality synchronous search organization as used for standard phrase-based translation to the characteristics of cube pruning. In this way we will be able to improve the performance of the generation process and reduce the average translation time per sentence to approximately one quarter. We will also investigate the cube growing algorithm, a reformulation of cube pruning with on-demand computation. This algorithm depends on a heuristic for the language model, but this issue is barely discussed in the original work. We analyze the behaviour of this heuristic and propose a new one which greatly reduces memory consumption without costs in runtime or translation performance. Results are reported on the German–English Europarl corpus.