Studentsrsquo; comprehensive evaluation system based on data mining method

He yongrong

School of Computer Science Zhejiang International Studies University

Hangzhou, China hangdianproe@163.com

Bian xiangjuan

School of Computer Science Zhejiang International Studies University

Hangzhou, China bianxiangjuan@163.com

Abstract—the paper researched comprehensive quality evaluation system of college student, and introduces data mining method to solve mess students data. Firstly, the paper construct studentsrsquo; data warehouse, then construct comprehensive multi-dimensions OLAP (Online Analysis Process) model, and through classification deterministic method, we can get useful message from the system to guide college manage students, and some Employers can find their satisfied employees.

Keywords-Data mining, comprehensive evaluation, OLAP

1. INTRODUCTION

With the higher education change from elite education to popular education, there are more and more studentrsquo;s management problems. The studentsrsquo; data is huge and complicated, and studentsrsquo; state and development is also canrsquo;t forecast. So the college managers hope to get a studentsrsquo; management system which has auxiliary deterministic ability and data mining technology just provide the effective method to solve the problem. Data mining method try to find useful information from huge data, this is the processes which find relationship between model and data from huge data^[1], this model and relationship can use to forecast. Under this background, utilize data mining technology in studentsrsquo; management system, construct perfect studentsrsquo; evaluation system, which can improve the students management level and speed up studentsrsquo; management work specialized^[2]. The paper firstly design students data warehouse, then construct studentsrsquo; data mining model which include: students information data mining, studentsrsquo; course selection data mining, studentsrsquo; obtain employee chance data mining, at last, we can obtain data mining result to get some reason about: the factors influencing student achievement, the factors influencing students course selection and factors influencing students obtain work chance, The whole process is just like figure 1.

1. STUDENTS COMPREHENSIVE EVALUATION DATA WAREHOUSE CONSTRUCTION

To finish data mining work, the first thing is prepare data, according to different department and spatialityrsquo;s studentsrsquo; information data, so all collective database should be reorganized and classified, systematized, to finish these work, the only thing to do is transfer these data to data warehouse. As teaching manager, their work often face deterministic analysis, their most attractive information is studentrsquo;s

graduation information and the information of the students adapt the society need. So, we use recent 5 years different departmentrsquo;s students graduation situation, course arrangement, employment tendency as basic data, the data warehouse structure is like figure 2.

Figure 1: The data mining process in the system

Figure 2:Students comprehensive evaluation data warehouse structure table

The studentrsquo;s comprehensive data warehouse was divided as computer department database, english department etc, then divided as thirteen data mart, the whole process:

Step 1 : construct data warehouse model function: determine system main body, and relationship with these bodies, refine the bodyrsquo;s every lever, such as , educational administration management system mainly divided as

studentsrsquo; achievement analysis theme, course arrangement theme, studentsrsquo; arranged employment situation;

Step 2: data warehouse physical databasersquo;s construction: define the system physical database model, not only recordrsquo;s type, default value, and constraints relationship, but also some indexes and physical view;

Step 3: data extraction, transformation, and integration: adopt Microsoft SQLSever tools extracting appoint record, delete unqualified data, and processing preliminary data integration.

Step 4: data import: when creating data warehouse, data transforming function of Microsoft SQLSever is necessary, because data from other database should be selected, processing, and loading to data warehouse.

1. CONSTRUCTION THE DETERMINISTIC TREE OF

STUDENTrsquo;S COMPREHENSIVE EVALUATION SYSTEM

The paper utilize deterministic classified theory to construct deterministic tree of studentrsquo;s comprehensive evaluation system, to realize qualitative analysis, the whole data classified process is like figure 3:

Figure 3: The whole process of data mining

Data mining classification is the key step of data mining application, to realize it, a suitable algorithm should firstly be selected, and also a proper program must be found to realize this algorithm. The paper adopt the famous deterministic classified method: ID3 algorithm ^[3], the algorithm trains all samples from root node, select an attribute to divide these samples, every value of attribute generates a branch, and then transforms branch attribute value of samples subset to a new node. This is a recursive process which has been used at every node, until every sample at node has been divided to a type. The figure 4 is showing the generating process of deterministic tree. ID3 algorithm is most typical deterministic tree algorithm; it was first put forward by J.R.Quinlan. The main core ideology of the algorithm is the greedy search algorithm which selected maximum information gain as current deterministic attribute. ID3 algorithm can keep depth of every branch of the deterministic tree is least, here give the ID3 algorithm detailed description：

ID3(table，attributes)

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基于数据挖掘方法的学生综合评价系统

何永荣 边相娟

浙江外国语大学计算机学院 浙江外国语大学计算机学院

中国杭州 hangdianproe@163.com 中国杭州 bianxiangjuan@163.com

摘要 - 本文研究了大学生综合素质评价体系，并介绍了数据挖掘方法来解决学生数据混乱问题。 首先，本文构建了学生数据仓库，然后构建了综合的多维OLAP（在线分析过程）模型，通过分类确定性方法，我们可以从系统中获取有用的信息来指导高校管理学生，一些雇主可以找到他们的满意的员工。

关键词 - 数据挖掘，综合评估，OLAP

一、引用

随着高等教育从大众化教育向大众化教育的转变，学生的管理问题也越来越多。学生的数据庞大而复杂，学生的状态和发展也无法预测。因此，高校管理者希望获得具有辅助确定能力的学生管理系统，数据挖掘技术只是提供解决问题的有效方法。数据挖掘方法试图从海量数据中寻找有用的信息，这是从大数据中找到模型与数据之间关系的过程[1]，该模型和关系可用于预测。在此背景下，利用数据挖掘技术在学生管理系统中，构建完善的学生评价体系，提高学生管理水平，加快学生管理工作专业化[2]。本文首先设计了学生数据仓库，然后构建了学生数据挖掘模型，包括：学生信息数据挖掘，学生选课数据挖掘，学生获取员工机会数据挖掘，最后得到数据挖掘结果得到一些理由：影响学生成绩的因素，影响学生选课的因素和影响学生获得工作机会的因素，整个过程如图1所示。

二、学生综合评估数据仓库建设

要完成数据挖掘工作，首先要根据不同部门和空间的学生信息数据准备数据，这样所有的集体数据库都要重新组织，分类，系统化，完成这些工作，唯一要做的就是转移这些数据 到数据仓库。 作为教学经理，他们的工作往往面临确定性分析，他们最有吸引力的信息是学生的毕业信息和学生的信息适应社会需要。 因此，我们用最近5年不同部门的学生毕业情况，课程安排，就业趋势作为基础数据，数据仓库结构如图2所示。

图1：系统中的数据挖掘过程

图2：学生综合评估数据仓库结构表

学生综合数据仓库分为计算机部门数据库，英语部门等，然后划分为十三个数据集市，整个过程：

步骤1：构建数据仓库模型功能：确定系统主体，与这些主体的关系，细化身体的每一个杠杆，如教育行政管理系统主要分为学生成绩分析主题，课程安排主题，学生安排就业情况;

步骤2：数据仓库物理数据库的构建：定义系统物理数据库模型，不仅记录了类型，默认值和约束关系，还有一些索引和物理视图;

步骤3：数据提取，转换和集成：采用Microsoft SQLSever工具提取指定记录，删除不合格数据，并处理初步数据集成。

步骤4：数据导入：创建数据仓库时，Microsoft SQLSever的数据转换功能是必要的，因为来自其他数据库的数据应该被选中，处理并加载到数据仓库。

三、构建学生综合评价体系的决定因素

本文利用确定性分类理论构建学生综合评价体系的确定性树，实现定性分析，整个数据分类过程如图3所示：

图3：数据挖掘的整个过程

数据挖掘分类是数据挖掘应用的关键步骤，要实现它，首先应选择合适的算法，并且必须找到合适的程序来实现该算法。本文采用着名的确定性分类方法：ID3算法[3]，算法从根节点训练所有样本，选择属性划分这些样本，属性的每个值生成一个分支，然后将样本子集的分支属性值转换为一个新节点。这是一个已在每个节点上使用的递归过程，直到节点上的每个样本都被划分为一个类型。图4显示了确定性树的生成过程。 ID3算法是最典型的确定性树算法;这是J.R.Quinlan首先提出的。该算法的主要核心思想是贪心搜索算法，该算法选择最大信息增益作为当前确定性属性。 ID3算法可以保持确定性树的每个分支的深度最小，这里给出ID3算法的详细描述：

ID3(table，attributes)

//input：training set table，attributes；

//output：Deterministic tree;

{if(table is empty)then Return(null)； N=a new node;//create node;

If(there are no predictive attributes in table)//第一种情况

then label N with most common value of attributes in table(deterministic tree)or with frequencies of attributes in table(probabilistic tree)；

else If(all instances in table have the same value V of attributes)

then//第二种情况

label N,”X.atrributes=V with probability 1”

else For each attribute A in table compute AVG ENTROPY(A,attributes,table)；

AS=the attributes for which AVG ENTROPY(A,attributes,table)is minimal；

If (AVG ENTROPY (A, attributes, table) is not substantially smaller than ENTROPY (attributes, table))

then Label N with most common value of attributes in table (deterministic tree) r with frequencies of attributes in table (probabilistic tree) ；

Else label N with AS;

FOR EACH VALUE V of AS DO

N1=ID3(SUBTABLE(table,A,V),attributes))； IF(N1!=null)then make an arc from N to N1 labeled

V；

End

End

End

Return N；

End

图4确定性树的生成过程。

确定性树的生成通过了两个阶段：学习和测试。 在研究阶段，确定性树采用自上而下的方式完成其递归过程，当树开始生成时，所有数据都在根节点中，然后以递归方式划分，直到生成分支节点。 第二阶段是测试阶段; 这个阶段的主要目的是删除一些噪音和非道德数据。 要停止递归过程，必须满足一个条件：同一节点的数据必须是同一类型; 没有其他属性可以按数据划分。

学生综合进化课程分为三个方面：道德水平，学术水平，课外实践水平[4]，顺序为：道德水平，学业水平，课外实践水平; 所有规则都是用if / then语言构造的：

if (moral level1)

then (if academic level1 or 2) and (extracurricular practice level1)

then (comprehensive quality1 ) (if academic level1 or 2) and (extracurricular practice level2)

then (comprehensive quality2 )(if academic level1 or 3) and (extracurricular practice level1)

then (comprehensive quality2 )

else(comprehensive quality3)

If (moral level2)

then (if academic level1) and (extracurricular practice level1) then (comprehensive quality1)

(if academic level1) and (extracurricular practice level2) then (comprehensive quality2 )

(if academic level2) and (extracurricular practice level1) then (comprehensive quality2 )

(if academic level2) and (extracurricular practice level2) then (comprehensive quality3 )

(if academic level3 or 4) and (extracurricular practice levela) then (comprehensive quality3 )

else (comprehensive quality4)

If (moral level3) then

(if academic level1) and (extracurricular practice level1) then (comprehensive quality3 )

Else

else (comprehensive quality4)

根据分类规则，我们可以获得如图5所示的确定性树：

图5学生综合进化的确定性树

四、学生全面进化原型系统开发

系统采用多维模型，模型可视为三维模型，采用数据库模型，处理后的数据可以方便地进行在线分析，系统可以按学生编号，方案，学年进行搜索。 该系统被划分为三个孤立的航空：道德，学术，课外实践。 道德数据通常来自他们自己的评估和同行评估，并由教师评估; 学术数据包括核心课程，必修课程和选修课程，计算分数时，可以对相应课程给予不同的权重，并对所有分数进行标准化，最后按顺序给出结果; 课外实践采用奖励制度，划分一些小项目，每个项目都有其上限，然后对分数进行标准化。 整个系统的功能模型如图6所示：图7和图8是系统工作界面。

图6学生综合系统功能

图7系统的整体渲染

图8评估结果的渲染

1. 结论

将现有的管理系统融入一套完善的学生管理信息系统，采用数据挖掘技术获取一些有用的信息，可广泛应用于学生综合评价，毕业生面试推荐和招生分析等，提高学生管理水平，加快学生管理水平。 学生专业化。

致谢

本文得到浙江省新人才计划项目的支持：“基于数据挖掘技术的学生综合能力评价系统”，国家自然科学基金“MEMS多场统一仿真模型构建与乐观研究”（61100101）， 也受到海洋机电设备技术重点学科的支持。

参考文献

1. Bodea, Constanta-Nicoleta, Bodea etc. Student performance in online project management courses: A data mining approach：3rd World Summit on the Knowledge Society, WSKS 2010，2010, 470-479
2. Ogor, Emmanuel N. Student academic performance monitoring and evaluation using data mining techniques . Electronics, Robotics and Automotive Mechanics Conference, CERMA 2007,2007,354-359
3. Vialardi, Ceacute;sar ; Chue, Jorge; Peche, Juan Pablo etc. A data mining approach to guide students through the enrollment process based on academic performance, User Modeling and User-Adapted Interaction, v 21, n 1-2, p 217-248, April 2011

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