关联规则在招生决策中的应用分析

随着高校招生系统的广泛使用,系统中积累近年来大量的生源信息和招生信息,如何使得这些看似垃圾的数据成为对高校招生决策的重要信息,该文应用数据挖掘技术中管理规则的Apriori算法,以招生系统中的信息为研究对象,挖掘影响学生报到的内在因素,发现与报到率关联的有用信息,从而降低招生宣传的成本,为高校的招生决策者提供指导和有效的数据支持。     

关联规则在招生决策中的应用分析

随着高校招生系统的广泛使用,系统中积累近年来大量的生源信息和招生信息,如何使得这些看似垃圾的数据成为对高校招生决策的重要信息,该文应用数据挖掘技术中管理规则的Apriori算法,以招生系统中的信息为研究对象,挖掘影响学生报到的内在因素,发现与报到率关联的有用信息,从而降低招生宣传的成本,为高校的招生决策者提供指导和有效的数据支持。     

高校新生报名管理系统设计

随着信息化产业的发展,高校在信息化应用上走在了时代的前列,为入校新生的报到提供了方便和快捷的通道,新生的报到系统主要表现在有人工管理核对方式改变为电脑自动化管理的方式。为了实现高新学生管理的自动化,编写了高校新生报名管理系统。本系统是一个具有现代化管理的报到软件,从专业角度出发,主要突出与全国高考招生录取系统数据库相衔接,新生来校办理报名手续简单,报到率统计准确等。方便来年学校专来计划的分配。     

用规则抽取句子中事件信息

信息抽取是数据挖掘的重要课题.目前的研究主要通过机器学习的方法对信息进行抽取.但是机器学习对训练数据的质量要求高,学习过程中参数设置复杂.而利用事先构建好的规则可以简单有效的从文本中提取事件信息.提出一种基于抽取规则对句子中的事件信息进行抽取的方法,摆脱了繁杂的机器学习过程.该方法利用本体对动词与事件角色匹配规则、事件角色抽取规则、时间信息抽取规则和地点信息抽取规则进行定义,用OWL对这些抽取规则进行了描述,然后应用这些规则抽取句子中的动词词义信息、事件角色信息、时间信息和地点信息,并用本文提出的一种新评测指标对事件信息进行评测.实验表明该方法从句子中抽取事件信息是有效的.     

基于粗糙集和信息增益的决策规则生成

信息增益技术可以删除信息量低的属性,决策规则的相关系数分析能对决策规则的准确度和覆盖度进行描述.提出了利用信息增益分析技术对决策表属性进行相关分析,然后进行属性和属性值约简,去除决策表中与决策无关的冗余信息,得出决策规则后,运用相关系数衡量决策规则的精确度.信息增益技术为决策规则的生成提供了一个有效的途径.     

基于粗糙集理论的增量式规则获取

基于粗糙集理论提出了一种新的规则提取算法：基于粗糙集和搜索树的规则提取算法。该算法是以现有规则集中的信息为启发信息,通过对解空间进行宽度优先启发式搜索,产生新规则。以该算法为基础,产生关于新增对象的规则,并对现有规则进行更新。     

Web services技术在学籍管理信息系统中的应用

把Webservices技术的优点运用到学生信息查询模块,当学生应修课程类别、应修学分发生改变或当新生入学培养方案发生变化时,教务人员只需发布一条修改信息,学籍管理系统就可以通过Webservices直接调用相应的规则。这样不仅可以简化系统管理人员的工作量．同时也增强了系统本身的灵活性与健壮性。     

量化关联规则在高校就业信息数据中的应用

针对就业信息数据中存在着大量的量化属性和分类属性等现象,提出了一种基于k-means的量化关联规则挖掘方法。该方法利用聚类算法k-means对量化属性进行合理分区,将量化属性转化为布尔型;利用改进的布尔关联规则方法对此进行关联规则挖掘,找出学生的受教育属性和就业属性之间的关联性;对挖掘出的规则进行分析和运用。就业信息数据实验证明,文中所提方法对就业信息进行挖掘是有效的、可行的。     

关联规则在独立学院招生决策中的应用

关联规则(Association Rule)是数据挖掘领域中一个重要的研究课题,广泛应用于各个领域,既可以检验行业内长期形成的知识模式,也能够发现隐藏的新规律。本文利用关联规则对独立学院招生信息进行分析,建立独立学院招生决策模型。首先选取考生高考志愿表中的专业信息,然后利用关联规则进行挖掘,最后对挖掘出的规则进行分析和应用。实验结果表明,利用关联规则对考生高考志愿信息进行挖掘是可行的、有效的,它为独立学院编制招生计划和制定招生宣传方案提供了一种新的参考依据,在独立学院招生领域具有较好的应用前景。     

基于关联规则的自然灾害预测系统

建立自然灾害预测模型,对自然灾害进行预测和分析,有利于提升防灾减灾的技术水平.基于关联规则和Web文本挖掘技术提出自然灾害预测系统的设计方案及实现方法.该系统利用成熟开源的爬虫框架从权威的灾害信息发布平台中定向抓取非结构化的自然灾害信息,通过中文分词技术进行数据清理将其整理成结构化的自然灾害数据库,并利用改进的关联规则算法从中挖掘出自然灾害事件的关联规则,进而可通过实时监控关联规则的前端信息,实现对自然灾害事件的预测.试运行结果表明该系统能有效挖掘出自然灾害信息的关联规则,并具有较高置信度.     

融合关联规则的MOOC资源众包平台任务分配算法

在高校大力发展MOOC平台背景下,为了提升学生的主动性、MOOC平台资源库的丰富性,出现了众包协同构建资源的方法,让学生和教师构成学习共同体,在完成新知识学习的同时进行资源库建设。高校MOOC众包平台的任务就属于知识密集型任务,挑选合适的参与者直接关系到MOOC资源库构建质量。为了更好地构建高校MOOC资源平台,提出一种针对知识密集型众包任务的分配方案,它包含学生的准入筛选、预期工作能力评估两个阶段。首先利用改进Apriori课程关联算法对学生进行准入筛选;其次利用知识关联算法对学生预期工作能力进行评估并将众包任务分配到工作能力最合适的学生;最后对方案进行测试验证,结果表明该方案能够能较好地提升学生挑选和任务分配的效果,促进构建更高质量的MOOC资源库。     

EVIDENT: An expert system prototype for helping the law student learn admissibility of evidence under the federal rules

The applicability of expert systems for helping students learn various subjects has been overlooked in recent years, particularly in the area of law. There have been only a few expert systems developed for helping in computer-aided instruction, such as SOPHIE, BUGGY, and PROUST. This paper describes EVIDENT, an expert system prototype which has been developed to aid a law student or graduate studying for the bar examination in understanding whether a piece of evidence is admissible into court, under the federal rules of evidence. According to many law students, the law class of “evidence” is extremely difficult because the student must remember all the rules of evidence and be able to analyze a situation and know when the various rules and their exceptions apply, and how. To help the student learn this material, EVIDENT has been developed, as described in this paper, which gives the student a structured framework and approach to determining the admissibility of evidence.     

Student modeling and assessment in intelligent tutoring of software patterns

This paper presents the design, implementation, and evaluation of a student model in DEPTHS (Design Pattern Teaching Help System), an intelligent tutoring system for learning software design patterns. There are many approaches and technologies for student modeling, but choosing the right one depends on intended functionalities of an intelligent system that the student model is going to be used in. Those functionalities often determine the kinds of information that the student model should contain. The student model used in DEPTHS is a result of combining two widely known modeling approaches, namely, stereotype and overlay modeling. The model is domain independent and can be easily applied in other learning domains as well. To keep student model update during the learning process, DEPTHS makes use of a knowledge assessment method based on fuzzy rules (i.e., a combination of production rules and fuzzy logics). The evaluation of DEPTHS performed with the aim of assessing the system’s overall effectiveness and the accuracy of its student model, indicated several advantages of the DEPTHS system over the traditional approach to learning design patterns, and encouraged us to move on further with this research.     

关联规则发现在素质教育中的应用

目前许多学校都为学生建立了信息数据库,针对如何从海量的学生信息数据库中提取出具有规律性的信息,文中利用数据挖掘中的关联规则发现技术设计并实现了一个学生素质关联规则发现系统。通过对实验结果进行分析,可以得出以下结论:该系统能够根据用户的参数设置自动发现学生各群体各种素质之间的相关性和规律,并界面友好地显示给用户从而能够帮助教育决策者发现当前教育管理中的问题,改进教育方法和策略,以达到深化贯彻素质教育的目的。     

C++程序设计实践教材的创新与实践

提高学生的计算机知识水平和实践动手能力,是计算机教学的根本任务。本文提出了一种上机实践教材的新思路,遵循学生的学习规律,从阅读程序模仿程序入手,由浅入深,最后达到拓展思维、提高编程能力的目的。     

Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student’s behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student’s knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.     

C4.5分类算法在高职就业预测中的应用

将C4.5决策树分类算法用于高职就业预测,并提取挖掘规则。对学生基本信息、各科考试成绩,以及就业信息进行处理,选取决策属性,构造决策树,由提取的规则,获得就业和学生成绩之间的关系,挖掘结果显示,该算法能将学习成绩属性和是否是学生干部属性进行正确分类,做出一定的就业预测,对辅助决策具有一定的帮助。     

基于改进的Apriori算法的入侵检测系统研究

针对动态安全模型理论P2DR,本文在入侵检测技术中应用了关联规则数据挖掘算法,并适当改进了Apriori算法。该算法对关联规则进行强有力的压缩,减少了结果集中规则的数目。实验结果表明,改进的算法能够有效压缩关联规则数目,提高算法效率,适用于网络数据挖掘,并能有效地减少入侵检测技术中的误报率和漏报率。     

Why observing a dialogue may benefit learning

Abstract The Vicarious Learner project is investigating the fundamental role of dialogue for learning. More specifically, the project is exploring the benefits to learners of being able to observe others participating in discussion. Such opportunities are becoming fewer with the proliferation of computer-based courses and distance learning, as well as growing student numbers. This paper presents the theoretical aspects of the work. A high-level ‘process model’ of learning is presented and then a more detailed model of what happens in educational dialogues. This ‘logic model’ of dialogue breaks discussions into two parts: the introduction of new premises and the derivation of new premises by the application of rules of reasoning in the domain. It is argued that in ordinary conversation the first aspect dominates while in educational dialogues, many misunderstandings arise from the need for more emphasis on explicit demonstration of use of rules.     

A Data Analysis-Based Approach for Detecting Intruders

ABSTRACT

We introduce a novel anomaly intrusion detection method based on Linear Discriminant Analysis (LDA). This approach searches for those vectors in the underlying space that best discriminate among users' profile classes. The discrimination rules are based on linear combinations of the observed users' profiles, called discriminant factors. This new approach provides for the ability to learn and later determine whether a new profile does or does not correspond to those of known users. Unlike many researchers we used realistic data to learn the behaviors of four students' classes. After that we apply LDA to get an appropriate discrimination between the student classes. Thus one can easily determine if a new student is legitimate or not by projecting its profile onto the profile subspace. Simulations show that our approach outperforms both Principal Components Analysis (PCA) and Electre Tri methods.