吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (5): 1866-1872.doi: 10.13229/j.cnki.jdxbgxb20210455

• 计算机科学与技术 • 上一篇    

基于决策树分类技术的学生考试成绩统计分析系统

孙小雪1(),钟辉2(),陈海鹏3   

  1. 1.吉林大学 中日联谊医院教学部,长春 130031
    2.吉林大学 大数据和网络管理中心,长春 130012
    3.吉林大学 计算机科学与技术学院,长春 130012
  • 收稿日期:2021-05-24 出版日期:2021-09-01 发布日期:2021-09-16
  • 通讯作者: 钟辉 E-mail:632621586@qq.com;zhongh@jlu.edu.cn
  • 作者简介:孙小雪(1986-),女,助理研究员.研究方向:数据挖掘,研究生培养与管理.E-mail:632621586@qq.com
  • 基金资助:
    国家自然科学基金青年科学基金项目(61602203);吉林省优秀青年人才基金项目(20180520020JH)

Statistical analysis system for students' examination results based on decision tree classification technology

Xiao-xue SUN1(),Hui ZHONG2(),Hai-peng CHEN3   

  1. 1.Teaching Department of China Japan Union Hospital,Jilin University,Changchun 130031,China
    2.Management Center of Big Data and Network,Jilin University,Changchun 130012,China
    3.College of Computer Science and Technology,Jilin University,Changchun 130012,China
  • Received:2021-05-24 Online:2021-09-01 Published:2021-09-16
  • Contact: Hui ZHONG E-mail:632621586@qq.com;zhongh@jlu.edu.cn

摘要:

为改善以往过于依赖人工统计分析学生考试成绩,无法挖掘成绩中所蕴含有用信息的缺陷,设计了基于决策树分类技术的学生考试成绩统计分析系统。系统的数据层利用SQL Sercer2000数据库存储系统运行过程中全部数据;业务逻辑应用层从数据层采集学生考试成绩数据后处理相关业务功能逻辑,采用决策树C4.5分类技术实现海量学生考试成绩精准分类,决策树C4.5分类技术基于信息增益率选取分类属性,采用后剪枝方法处理数据提升数据分类精度;业务逻辑层的成绩统计分析结果利用WEB服务器通过浏览器应用层传送至用户。选取某医学院研究生作为系统测试对象,系统测试结果表明:所设计系统可依据用户需求统计分析不同专业人员考试成绩,且噪声情况下仍具有较高的统计分析能力。

关键词: 计算机应用, 决策树分类, 学生考试成绩, 统计分析, 系统设计

Abstract:

In order to improve the defect of relying too much on manual statistical analysis of students' examination results and unable to mine the useful information contained in the results, a statistical analysis system of students' examination results based on decision tree classification technology is designed. The data layer of the system uses SQL Server 2000 database to store all the data during the operation of the system; The application layer of business logic collects the data of students' test scores from the data layer and processes the relevant business function logic. The C4.5 Classification Technology of decision tree is used to realize the accurate classification of massive students' test scores. The C4.5 Classification Technology of decision tree selects the classification attributes based on the information gain rate and uses the post pruning method to process the data to improve the data classification accuracy; The statistical analysis results of the business logic layer are transmitted to the users through the browser application layer using the web server. The system test results show that the designed system can statistically analyze the examination results of different professionals according to the user needs, and still has high statistical analysis accuracy in the case of noise.

Key words: computer application, decision tree classification, student test results, statistics analysis, system design

中图分类号: 

  • TP391

图1

系统总体结构图"

图2

系统功能结构图"

图3

系统设计流程图"

表1

研究对象分布"

序号专业名称考试人数/人考试年级
1超声医学科132018
2儿科32018
3耳鼻咽喉科32018
4放射科182018
5放射肿瘤科52018
6妇产科72018
7骨科442018
8核医学科22018
9急诊科22018
10检验医学科12018
11口腔全科32018
12麻醉科152018
13内科522018
14皮肤科32018
15神经内科222018
16外科702018
17眼科22018

表2

信息熵以及信息增益率"

序号专业名称信息熵信息增益率
1超声医学科0.9580.008
2儿科0.9460.012
3耳鼻咽喉科0.9750.008
4放射科0.9620.006
5放射肿瘤科0.9450.007
6妇产科0.9850.012
7骨科0.9460.015
8核医学科0.9250.012
9急诊科0.9850.012
10检验医学科0.9740.014
11口腔全科0.9680.009
12麻醉科0.9740.008
13内科0.9850.007
14皮肤科0.9460.012
15神经内科0.9750.019
16外科0.9680.015
17眼科0.9780.017

图4

考试未及格人员分布"

表3

考试成绩分析结果"

序号专业名称平均分最高分最低分不及格人数/人
1超声医学科83.4696750
2儿科88.6792860
3耳鼻咽喉科76.6781730
4放射科48.679007
5放射肿瘤科70.4089600
6妇产科71.6489600
7骨科55.6493014
8核医学科82.0082820
9急诊科69.0072660
10检验医学科89.0089890
11口腔全科47.007101
12麻醉科74.409400
13内科62.2586010
14皮肤科77.0097630
15神经内科75.778501
16外科69.908904
17眼科33.506701

图5

分数段统计结果"

图6

学生考试成绩统计分析系统强健性对比结果"

1 刘振宇, 宋晓莹. 一种可用于分类型属性数据的多变量决策树算法[J]. 东北大学学报: 自然科学版, 2020, 41(11): 1521-1527.
Liu Zhen-yu, Song Xiao-ying. An applicable multivariate decision tree algorithm for categorical attribute data[J]. Journal of Northeastern University(Natural Science), 2020, 41(11): 1521-1527.
2 崔文泉, 黄禹侨. 高维数据情形下的一种基于随机投影的集成分类方法[J]. 中国科学技术大学学报, 2019, 49(12): 974-984.
Cui Wen-quan, Huang Yu-qiao. A new random projection-based ensemble classifier for high-dimensional data[J]. Journal of University of Science and Technology of China, 2019, 49(12): 974-984.
3 刘通. 5G D2D网络中基于机器学习的中继选择策略[J]. 中国电子科学研究院学报, 2019, 14(10): 1016-1021.
Liu Tong. Relay selection strategy based on machine learning in 5G D2D network[J]. Journal of China Academy of Electronics and Information Technology, 2019, 14(10): 1016-1021.
4 王文霞. 数据挖掘中改进的C4.5决策树分类算法[J]. 吉林大学学报: 理学版, 2017, 55(5):1274-1277.
Wang Wen-xia. Improved c4.5decision tree classification algorithm in data mining[J]. Journal of Jilin University(Science Edition), 2017, 55(5):1274-1277.
5 杨勇, 张磊, 曲福恒, 等. 基于最频繁项提取和候选集剪枝的THIMFUP算法[J]. 吉林大学学报: 理学版, 2021, 59(3): 635-642.
Yang Yong, Zhang Lei, Qu Fu-heng, et al. THIMFUP algorithm based on the most frequent item extraction and candidate set pruning[J]. Journal of Jilin University(Science Edition), 2021, 59(3): 635-642.
6 赵宏, 常兆斌, 王伟杰. 基于深度自编码和决策树的恶意域名检测[J]. 微电子学与计算机, 2020, 37(5): 13-17.
Zhao Hong, Chang Zhao-bin, Wang Wei-jie. Malicious domain name detection based on deep auto-encoder and decision tree[J]. Microelectronics & Computer, 2020, 37(5): 13-17.
7 张旭, 周新志, 赵成萍, 等. 基于犹豫模糊决策树的非均衡数据分类[J]. 计算机工程, 2019, 45(8): 75-79, 91.
Zhang Xu, Zhou Xin-zhi, Zhao Cheng-ping, et al. Unbalanced data classification based on hesitant fuzzy decision tree[J]. Computer Engineering, 2019, 45(8): 75-79, 91.
8 颜晓佳, 张胜. 基于Excel软件的成绩管理系统设计与开发[J]. 教学与管理(中文版), 2020(3): 15-17.
9 王平, 鲜亮. 大学生学习成绩分析及相应的差异教学策略[J]. 化学教育, 2019, 40(24): 16-23.
Wang Ping, Xian Liang. Research of college students' academic achievements and corresponding difference teaching strategies[J]. Chinese Journal of Chemical Education, 2019, 40(24): 16-23.
10 郭慧, 刘忠宝, 柳欣. 基于云模型与决策树的入侵检测方法[J]. 计算机工程, 2019, 45(4): 142-147.
Guo Hui, Liu Zhong-bao, Liu Xin. Intrusion detection method based on cloud model and decision tree[J]. Computer Engineering, 2019, 45(4): 142-147.
11 邓蔚,陈秀婷,张清华,等.基于树模型的差分隐私保护算法[J].重庆邮电大学学报:自然科学版,2020,32(5):848-849.
Deng Wei, Chen Xiu-ting, Zhang Qing-hua, et al. Differential privacy protection algorithms based on tree models[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2020,32(5):848-849.
12 余敦辉, 张笑笑, 付聪, 等. 基于决策树的敏感词变形体识别算法研究及应用[J]. 计算机应用研究, 2020, 37(5): 1395-1399, 1405.
Yu Dun-hui, Zhang Xiao-xiao, Fu Cong, et al. Research and application of change form of sensitive words recognition algorithm based on decision tree[J]. Application Research of Computers, 2020, 37(5): 1395-1399, 1405.
13 高宁化, 王姮, 冯兴华. 基于动态模糊决策树的心电信号分类方法[J]. 计算机工程, 2020, 46(1): 80-86.
Gao Ning-hua, Wang Heng, Feng Xing-hua. Classification method of electrocardiogram signals based on dynamic fuzzy decision tree[J]. Computer Engineering, 2020, 46(1): 80-86.
14 柳春艳, 李丹, 张宝仁, 等. SPOC翻转课堂教学有效性的系统评价与元分析[J]. 开放教育研究, 2019, 25(1): 36,82-91.
Liu Chun-yan, Li Dan, Zhang Bao-ren, et al. Teaching effectiveness of SPOC flipped classroom in college: a systematic review and meta-analysis[J]. Education Research, 2019, 25(1): 36,82-91.
15 谭常春, 李曼丽. 面板数据均值共同变点分析在高校在校成绩上的应用[J]. 合肥工业大学学报: 自然科学版, 2020, 43(6): 855-858.
Tan Chang-chun, Li Man-li. Application of common mean breaks analysis of panel data in college performance[J]. Journal of Hefei University of Technology(Natural Science), 2020, 43(6): 855-858.
16 郭鹏, 蔡骋. 基于聚类和关联算法的学生成绩挖掘与分析[J]. 计算机工程与应用, 2019, 55(17): 169-179.
Guo Peng, Cai Cheng. Data mining and analysis of students' score based on clustering and association algorithm[J]. Computer Engineering and Applications, 2019, 55(17): 169-179.
17 王少卿, 焦艳. 高中化学教师资格考试大纲中动词的统计与分析[J]. 化学教育, 2019, 40(9): 70-74.
Wang Shao-qing, Jiao Yan. Statistics and analysis of verbs in outline of senior high school chemistry teacher qualification examination[J]. Chinese Journal of Chemical Education, 2019, 40(9): 70-74.
18 朵琳, 杨丙. 基于概念格的稀疏数据协同过滤校正自然噪声方法[J]. 吉林大学学报: 理学版, 2020, 58(5): 1173-1180.
Duo Lin, Yang Bing. Collaborative filtering correction of natural noise method of sparse data based on concept lattice[J]. Journal of Jilin University(Science Edition), 2020, 58(5): 1173-1180.
19 段亚军, 杨有龙, 白旭英. 基于模糊规则的随机缺失属性值数据分类算法[J]. 吉林大学学报: 理学版, 2019, 57(1): 89-96.
Duan Ya-jun, Yang You-long, Bai Xu-ying. Classification algorithm of random missing attribute value data based on fuzzy rule[J]. Journal of Jilin University(Science Edition), 2019, 57(1): 89-96.
20 王雅超. 基于大数据分析的散乱缺损信息无损恢复方法[J]. 吉林大学学报: 理学版, 2020, 58(3): 645-650.
Wang Ya-chao. Lossless restoration method of scattered defect information based on big data analysis [J]. Journal of Jilin University(Science Edition), 2020, 58(3): 645-650.
21 殷君茹, 侯瑞霞, 唐小明, 等. 基于瓦片金字塔模型的海量空间数据快速分发方法[J]. 吉林大学学报: 理学版, 2015, 53(6): 1269-1274.
Yin Jun-ru, Hou Rui-xia, Tang Xiao-ming, et al. Fast distribution method for large data set of spatial data based on tiles pyramid model[J]. Journal of Jilin University(Science Edition), 2015, 53(6):1269-1274.
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