基于数据挖掘技术的高校《就业指导》网络课程建设研究

发布时间：2018-09-11 08:05

【摘要】：我国高等教育的规模不断扩大,尤其是从精英型教育转向“大众化”教育以来,大学毕业生人数逐年上升,随之而来的就业问题也越来越突出。大学生是凝聚了国家、社会、家庭无数希望和目光的群体,如何帮助他们毕业后顺利找到适合自己专业和特长的工作,是每个高校普遍面临的重要课题。就业指导课程的开设可以引导和帮助大学生充分认识自身特点、了解社会需求、最终成功就业,也是解决现阶段大学生就业难问题、提高高校就业率的一个有效且必要的途径。在Web技术被广泛应用的大环境下,设计开发《就业指导》网络课程,能更有效地促进就业指导工作的开展,保证学生可以随时随地进行课程学习的可能性。本文在研究中引入了数据挖掘的思想。数据库技术越来越成熟,存储在数据库中的信息也在急剧增长。而据调查显示,大部分高校信息管理系统中存储的数据,80%以上都只提供简单的查询功能,隐藏在这些数据中的潜在关系几乎都未被发现和利用。笔者以S大学教务管理系统中的学生成绩信息、就业数据库中的学生就业信息为对象,结合学生的基本信息和在校期间的表现,利用数据挖掘技术中基于支持向量机(Support Vector Machine,SVM)的特征选择算法,找出这些不同数据库中信息之间的内在关系。在此基础上,分析了实验结果在建设《就业指导》网络课程中的应用,为建设该课程的学习平台提供了理论基础。论文总共分为五个章节,第一部分介绍了课题研究的背景、研究的意义和目的以及研究的思路和方法,让读者对本文有一个总体的认识;第二部分阐述了本论文研究课题所基于的理论基础,并介绍了现阶段的研究概况;第三部分对数据挖掘技术进行了概述,着重介绍了SVM的相关概念、原理和最优化方法;第四部分是本文的关键部分,经过对原始数据的收集、预处理,利用基于SVM的特征选择方法对数据进行分析,最终得出实验的结果,探讨其对《就业指导》网络课程建设的实际指导意义。最后一部分是论文的结论,总结了在论文研究进行过程中存在的不足和进一步努力的方向。
[Abstract]:The scale of higher education in China is expanding, especially since the elite education turns to "popular" education, the number of university graduates increases year by year, and the problems of employment become more and more prominent. College students are a group of numerous hopes and eyes of the country, society and family. How to help them to find a job suitable for their specialty and specialty after graduation is an important subject that every university generally faces. The establishment of employment guidance course can guide and help college students to fully understand their own characteristics, understand social needs, and finally succeed in employment. It is also an effective and necessary way to solve the problem of employment of college students at this stage and to improve the employment rate of colleges and universities. In the environment where Web technology is widely used, the design and development of "Employment guidance" network course can promote the development of employment guidance work more effectively, and ensure the possibility for students to study courses at any time and anywhere. In this paper, the idea of data mining is introduced. Database technology is becoming more and more mature, and the information stored in database is growing rapidly. According to the survey, more than 80% of the data stored in the information management systems of colleges and universities only provide simple query function, and the potential relationships hidden in these data are almost not found and utilized. The author takes the student achievement information in the educational administration management system of S University, the student employment information in the employment database as the object, combines the student basic information and the performance during the school period, The feature selection algorithm based on support vector machine (Support Vector Machine,SVM) in data mining technology is used to find out the internal relationship between the information in these different databases. On this basis, the application of the experimental results in the construction of the online course "Employment guidance" is analyzed, which provides a theoretical basis for the construction of the learning platform of the course. The paper is divided into five chapters. The first part introduces the background, significance and purpose of the research, as well as the research ideas and methods, so that readers have a general understanding of this paper; The second part describes the theoretical basis of this research topic, and introduces the current research situation, the third part of the data mining technology is summarized, focusing on the introduction of the relevant concepts of SVM, The fourth part is the key part of this paper, after collecting and preprocessing the original data, using the feature selection method based on SVM to analyze the data, finally get the results of the experiment. To explore its practical guiding significance for the construction of the network course of Employment guidance. The last part is the conclusion of the paper.
【学位授予单位】：上海师范大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：G647.38;G434

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