基于决策树的大学生科研活动与毕业走向的关联研究

J4 ›› 2016, Vol. 38 ›› Issue (06): 1275-1280.

基于决策树的大学生科研活动与毕业走向的关联研究

胡灵敏1，骆力明1，茅潇潇2，高明2

（1.首都师范大学信息工程学院,北京 100048；2.华东师范大学软件学院,上海 200062）

收稿日期:2015-04-28 修回日期:2015-08-27 出版日期:2016-06-25 发布日期:2016-06-25
基金资助:
国家社会科学基金重大委托项目（14&ZH0036）

Relationship between college students’ scientific research and
prospects after graduation based on decision tree

HU Lingmin1，LUO Liming1，MAO Xiaoxiao2，GAO Ming2

(1.College of Information Engineering,Capital Normal University,Beijng 100048;
2.Institute of Software Engineering,East China Normal University,Shanghai 200062,China)

Received:2015-04-28 Revised:2015-08-27 Online:2016-06-25 Published:2016-06-25

摘要/Abstract

摘要：

本科生的科研活动越来越得到教育研究者的重视，然而，研究者很少关注学生科研活动与毕业走向之间的关联问题。为了探究本科生参与科研情况对其未来发展的影响，从学生毕业走向切入，挖掘影响学生未来毕业去向的重要因素。以北京市某师范高校计算机相关专业的2009级的所有学生的科研情况与毕业走向作为数据主要来源，以Weka作为模型建立和评估的辅助工具，采用J48决策树分类，进行十折交叉验证，建立了基于学生科研活动的毕业走向预测模型，并通过对模型进行验证评估，证明了模型预测准确率良好。研究结果表明，学生参与竞赛的数量、专业、作为项目负责人的项目数量、参与教学类项目的数量对学生的未来走向有着重要影响。

关键词: 科研活动, 决策树, 毕业走向, Weka

Abstract:

Educational researchers are paying more and more attention to scientific research activities, however, little attention is paid to the relationship between students’scientific research and their future direction. In order to explore this relationship, we take the careers after graduation as the pointcut, and extract the most important components that can influence the relationship. Taking computerrelated majors from a normal college in Beijing as the research sample, we build a student graduation prediction model based on students’ scientific research, and 10fold crossvalidation is used to validate the model. Evaluation results prove that the model's prediction accuracy is good. The results also indicate that the number of students’ participation in competition, students’ majors, the number of projects as a leader, and the number of students’ participation in projects on education have important influence on student’s future direction.

Key words: research activity;decision tree;prospects after graduation;Weka

胡灵敏1，骆力明1，茅潇潇2，高明2. 基于决策树的大学生科研活动与毕业走向的关联研究[J]. J4, 2016, 38(06): 1275-1280.

HU Lingmin1，LUO Liming1，MAO Xiaoxiao2，GAO Ming2. Relationship between college students’ scientific research and
prospects after graduation based on decision tree [J]. J4, 2016, 38(06): 1275-1280.

参考文献［11］

［1］	Guo Hui,Liu Lin,Peng Pai,et al.An empirical study on the impact of scientific research participation on the innovative quality of college students of science and engineering［J］.Research in Higher Education of Engineering,2014(2):106111.(in Chinese)
［2］	Salsman N,Dulaney C L,Chinta R,et al.Student effort in and perceived benefits from undergraduate research［J］.College Student Journal,2013,47(1):202211.
［3］	Cao Jian,Wang Haixin,Zhu Ruoshan,et al.Undergraduate research promote the innovation abilitya case study of Peking University ［J］.Chinese University Science & Technology,2013(Z1):1619.(in Chinese)
［4］	Petrella J K, Jung A, Petrella J K, et al.Undergraduate research:Importance,benefits,and challenges［J］.International Journal of Exercise Science,2008(3):9195.
［5］	Gu Yingying.Exploration on cultivation mode of undergraduate research ability:Practice and enlightenment from university of Hong Kong［J］.Experimental Technology and Management,2014,31(9):3740.(in Chinese)
［6］	Xiao Xiao,Chen Xiaoyue,Huang Jingjing,et al.American undergraduate scientific research training organization and management agencies related websites ［J］.China Agricultural Education,2006,39(5):5758.(in Chinese)
［7］	Lu Yuanyuan,Zhang Jian,He Haiyan.Research on WEKAbased customer classification information systems［J］.Computer Engineering & Science,2011,33(5):132135.(in Chinese)
［8］	Han Jiaowei,Kan Bo.Data mining:Concepts and techniques［M］.2nd Edition.Fan Ming,Meng Xiaofeng, translated.Beijing:China Machine Press,2009.(in Chinese)
［9］	Zhang Xueying.Analysis and comparison of overseas leading data mining tools［J］.Computer Engineering,2003,29(16):13.(in Chinese)
［10］	Li Sihai,Zhang Zhongwen.The Id3 decision tree algorithm in WEKA［J］.Journal of Changchun University,2011,21(2):6769.(in Chinese)
［11］	Wu Ye.The study and implementation of decision trees algorithm based on Weka［D］.Xi’an:Xidian University,2014.(in Chinese)
	附中文参考文献：
［1］	郭卉,刘琳,彭湃,等.参与科研对理工科大学生创新素质影响的实证研究［J］.高等工程教育研究,2014(2):106111.
［3］	曹建,王海欣,朱若珊,等.以本科生科研促进创新能力培养——北京大学的个案研究［J］.中国高校科技,2013(Z1):1619.
［5］	顾莹莹.本科生科研能力培养模式初探——香港大学的实践和启示［J］.实验技术与管理,2014,31(9):3740.
［6］	肖笑,陈笑玥,黄晶晶,等.美国本科生科研训练组织和管理机构相关网站的作用［J］.中国农业教育,2006,39(5):5758.
［7］	卢媛媛,张剑,何海燕.基于WEKA的客户分类信息系统研究［J］.计算机工程与科学,2011,33(5):132135.
［8］	韩家炜,堪博.数据挖掘概念与技术［M］.第2版.范明，孟小峰，译.北京:机械工业出版社,2009.
［9］	张雪英.国外先进数据挖掘工具的比较分析［J］.计算机工程,2003,29(16):13.
［10］	李四海,张忠文.WEKA中的Id3决策树算法［J］.长春大学学报,2011,21(2):6769.
［11］	吴烨.基于Weka平台的决策树算法研究与实现［D］.西安:西安电子科技大学,2014.

[1]	崔弘, 赵双, 张广胜, 苏金树. 基于机器学习的移动代理应用流量识别方法[J]. 计算机工程与科学, 2022, 44(04): 654-664.
[2]	贾俊杰, 段超强. 基于评分离散度的托攻击检测算法[J]. 计算机工程与科学, 2022, 44(03): 554-562.
[3]	王鑫, 陈建凯, 翟俊海, . 区间值属性单调决策树算法的扩展[J]. 计算机工程与科学, 2020, 42(03): 557-563.
[4]	陈丽芳,冯力静,刘保相. 神经网络规则优化建模与应用[J]. 计算机工程与科学, 2019, 41(12): 2247-2254.
[5]	陈力1，费洪晓2，丁海伦2，成琳2，翟纪宇2. 基于双决策树的数据采样方法[J]. 计算机工程与科学, 2019, 41(01): 130-135.
[6]	张元鸣，陈苗，陆佳炜，徐俊，肖刚. 基于MapReduce的Bagging决策树优化算法[J]. 计算机工程与科学, 2017, 39(05): 841-848.
[7]	钟伟1，黄元亮2. 基于特征融合与决策树技术的表情识别方法[J]. 计算机工程与科学, 2017, 39(02): 393-398.
[8]	吴思博，陈志刚，黄瑞. 基于相关系数的ID3优化算法[J]. 计算机工程与科学, 2016, 38(11): 2342-2347.
[9]	周美琴，陈诗旭，袁鼎荣，朱新华. 一种单位代价收益决策树剪枝算法[J]. J4, 2016, 38(05): 1023-1030.
[10]	董跃华,刘力. 基于相关系数的决策树优化算法[J]. J4, 2015, 37(09): 1783-1793.
[11]	张岩峰1,陈长松1,杨涛1，左俐俐2，丁飞1. 微博用户的个性分类分析[J]. J4, 2015, 37(02): 402-409.
[12]	郭冬梅，李朝品. 基于检索决策树的螨种分类鉴定专家系统[J]. J4, 2012, 34(3): 192-195.
[13]	卢媛媛,张剑,何海燕. 基于WEKA的客户分类信息系统研究[J]. J4, 2011, 33(5): 132-135.
[14]	常志玲，王岚. 一种新的决策树模型在就业分析中的应用[J]. J4, 2011, 33(5): 141-145.
[15]	丁春荣1,李龙澍2. 变精度粗糙集模型在决策树构造中的应用[J]. J4, 2010, 32(7): 86-88.