基于数据挖掘的学业预警模型构建

王斌; 冯向萍; 王业; 杨抒; 赵毅

doi:10.13568/j.cnki.651094.651316.2019.09.02.0001

您当前的位置：

首页 >

文章列表页 >

基于数据挖掘的学业预警模型构建

更新时间：2026-01-21

- 基于数据挖掘的学业预警模型构建
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 37, Issue 2, Pages: 183-189(2020)
- 作者机构：
  
  1. 新疆农业大学现代教育技术中心
  2. 新疆农业大学计算机与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2019.09.02.0001
  CLC： G647;TP311.13
- Published：2020
- 稿件说明：
移动端阅览
[1]王斌,冯向萍,王业,等.基于数据挖掘的学业预警模型构建[J].新疆大学学报(自然科学版)(中英文),2020,37(02):183-189.
[1]王斌,冯向萍,王业,等.基于数据挖掘的学业预警模型构建[J].新疆大学学报(自然科学版)(中英文),2020,37(02):183-189. DOI： 10.13568/j.cnki.651094.651316.2019.09.02.0001.

DOI：10.13568/j.cnki.651094.651316.2019.09.02.0001.

摘要

本文以新疆农业大学本科生学业预警体系为例

基于数据挖掘技术构建学业预警模型.首先建立数据仓库

提取特征属性

使用Pearson相关系数等对特征属性进行相关性分析;其次

采用随机森林算法对学生不及格状态进行评估;最后

利用Apriori算法建立预警规则

并对本研究实际应用于提高教学管理水平、推动教育教学改革做了有益的探索.

Abstract

This research uses data mining technology to construct academic early warning model for students to carry out academic early warning.The model firstly uses saliency

Pearson correlation coefficient and covariance calculation to analyze the correlation of the feature attributes extracted into the data warehouse

then uses stochastic forest algorithm to evaluate the status of students' registration

and finally uses Apriori algorithm to find out the list of early-warning students.The application of this model has a certain reference significance for improving the level of teaching management and promoting the application of information technology in education and teaching reform.

关键词

Keywords

references

王珊珊,孙其伟,陈云.高校数据仓库构建与应用研究[J].华东师范大学学报(自然科学版),2015(S1):509-515.WANG S S,SUN Q W,CHEN Y.Construction and application of university data warehouse[J].Journal of East China Normal University(Natural Science Edition),2015(S1):509-515.(in Chinese)

吕红胤,连德富,聂敏,等.大数据引领教育未来:从成绩预测谈起[J].大数据,2015,1(4):118-121.LV H X,LIAN D F,NIE M,et al.Big Data Leads the Future of Education:From Performance Forecast[J].Big Data,2015,1(4):118-121.(in Chinese)

陈桂香.大数据对我国高校教育管理的影响及对策研究[D].武汉:武汉大学,2017.CHEN G X.The impact of big data on the management of higher education in China and Its Countermeasures[D].Wuhan:Wuhan University,2017.(in Chinese)

杨财英.Apriori算法及其在学生成绩分析中的应用研究[D].长沙:湖南大学,2016.YANG C Y.Apriori algorithm and its application in student performance analysis[D].Changsha:Hunan University,2016.(in C hinese)

郭译浓.数据仓库技术在学生成绩分析中的应用研究[D].哈尔滨:哈尔滨师范大学,2017.GUO Y N.Application of Data Warehouse Technology in Student Achievement Analysis[D].Harbin:Harbin Normal University,2017.(in Chinese)

孙悦,袁健.基于Spark的改进随机森林算法[J].电子科技,2019,32(4):60-63+67SUN Y,YUAN J.Improved Stochastic Forest Algorithms Based on Spark[J].ElectronicScience Technology,2019,32(4):60-63+67.(in Chinese)

韩建军.基于随机森林算法的粮堆机械通风温度预测及控制研究[D].郑州:河南工业大学,2019HAN J J.Prediction and control of mechanical ventilation temperature of grain stack based on Stochastic Forest algorithm[D].Zhengzhou:Henan University of Technology,2019.(in Chinese)

刘月超,于炯,鲁亮.Storm环境下一种改进的任务调度策略[J].新疆大学学报(自然科学版),2017,34(1):90-95.LIU Y C,YU J,LU L.An improved task scheduling strategy in storm environment[J].Journal of Xinjiang University(Natural Science Edition),2017,34(1):90-95(in Chinese)

吴非,吴向前,陈晓燕.改进随机森林算法在Android恶意软件检测中的应用[J].新疆大学学报(自然科学版),2017,34(3):322-327WU F,WU X Q,CHEN X Y.Application of improved random forest algorithm in Android malware detection[J].Journal of Xinjiang University(Natural Science Edition),2017,34(3):322-327.(in Chinese)

李俊.基于关联规则的Apriori改进算法[J].企业科技与发展,2018(9):42-44+57Li Jun.An improved Apriori algorithm based on association rules[J].Sci-Tech&Development of Enterprise,2018(9):42-44+57(in Chinese)

陈邦豪.Apriori算法在大数据集上的高效应用[J].智能计算机与应用,2018,8(4):195-198+202CHEN H B.Efficient Application of Chen Banghao.Apriori Algorithms on Large Data Sets[J].Intelligent Comuter ang Applications,2018,8(4):195-198+202.(in Chinese)

Views

467

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰