高考“一档多投”关键算法及相关投挡方式对比分析

发布时间：2018-06-21 07:58

本文选题：高考 + 数据仓库　；参考：《西南大学》2014年硕士论文

【摘要】：自1977年恢复高考以来,普通高等院校招生考试为我国社会主义建设做出了巨大贡献,得到了社会的广泛认可。然而,目前的高考招生录取投档模式仍然存在诸多如资源分配不合理、考生“高分低录”、院校分数线趋于扁平化等弊端。如何对现有的招生模式进行改进,使其更加适用于中国的高考,是亟需解决的问题。某省自2000年,高考招生录取工作全部在网上进行,累积了大量的电子招考数据,这些数据包含了丰富的可用信息,如何从这些已有的数据中获取有效可用的信息,为教育管理部门制定完善的招生录取政策提供决策支持。本文选取该省自2000年后近十年的高考招生录取数据,运用这些数据做了两个方面工作： 1、统计分析方面,根据该省高考招生数据,构建数据仓库,通过统计分析方法研究重点高校农村户籍学生比例下降的原因、考生报考院校的地域分布特点、专业的报考热度特点、报考人数与执行人数的关系等。结果表明重点高校农村户籍学生比例下降主要由城乡教育水平差距增大造成、而考生对高校的选择呈现幂律分布特征、从专业的报考热度可以反应某个专业的受欢迎的程度、报考人数与执行人数则能够反映出该省历年的生源情况。 2、算法设计方法,通过构建高考“一档多投”录取模式模型,设计适合于中国国情的高考的“一档多投”录取算法,并对该省历年高考招生数据进行模拟分析,得出两方面结论：对于考生来说,高考“一档多投”模式的录取算法,提高了高考整体的录取率,降低了考生“高分低录”、落榜等情况出现的概率,有效减小考生填报志愿的博弈程度；对于院校来说,提高了院校特色专业的排名,录取后的院校专业排名更加符合其真正实力的排名,凸显了院校的专业特色,促进院校特色专业发展。
[Abstract]:Since the college entrance examination was resumed in 1977, the college entrance examination has made a great contribution to the socialist construction of our country, and has been widely recognized by the society. However, there are still many disadvantages in the mode of college entrance examination admission, such as unreasonable allocation of resources, high scores and low records of candidates, flat lines in colleges and universities, and so on. How to improve the current enrollment model and make it more suitable for the college entrance examination in China is an urgent problem to be solved. In a certain province since 2000, the college entrance examination admission work has been carried out on the Internet, and a large amount of electronic recruitment data has been accumulated. These data contain abundant available information and how to obtain the valid available information from these existing data. To provide decision support for education management to formulate perfect enrollment policy. This paper selects the data of college entrance examination enrollment in the province since 2000, and makes use of these data to do two aspects of work: 1. In the aspect of statistical analysis, according to the data of college entrance examination in this province, the data warehouse is constructed. By means of statistical analysis, this paper studies the reasons for the decline of the proportion of rural household registration students in key colleges and universities, the regional distribution characteristics of examinees in colleges and universities, the characteristics of professional enlistment fever, and the relationship between the number of applicants and the number of executing students, and so on. The results show that the decrease of the proportion of rural household registration students in key universities is mainly caused by the increase of the gap between urban and rural education level, and the students' choice of colleges and universities is characterized by power-law distribution. On the other hand, the number of applicants and the number of students executed can reflect the situation of students in the province in the past years. 2, the algorithm design method, through the construction of the "one file multi-vote" admission model, This paper designs the "one file multiple input" admission algorithm suitable for Chinese national conditions, and simulates and analyzes the enrollment data of the provincial college entrance examination over the years, and draws two conclusions: for the examinee, the admission algorithm of the "one file multiple cast" model for the college entrance examination, It improves the overall acceptance rate of the college entrance examination, reduces the probability of the candidates'"high scores and low records" and the probability of falling out of the list, effectively reduces the game degree of candidates to fill in voluntary applications; for colleges and universities, it improves the ranking of characteristic majors in colleges and universities. After admission, the professional ranking of colleges and universities is more in line with their real strength ranking, which highlights the characteristics of colleges and universities, and promotes the development of specialty of colleges and universities.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：G647.3;TP301.6

【参考文献】