基于校园大数据的学生行为特征分析与预测方法
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摘要
如何有效挖掘学生行为数据是提升学生信息化管理水平的重要内容。针对目前学生信息化管理平台不完善、挖掘精度低的问题,结合决策树、神经网络以及朴素贝叶斯算法建立组合模型,建立基于Spark的学生行为分析与预测平台;同时,以学生消费规律、生活习惯以及学习情况等校园行为作为大数据来源,进行预测分析和实例验证。结果表明:该模型预测结果与实际情况相吻合,平均预测误差不超过5%,验证了所用方法的有效性,可根据学生行为特性分析其行为规律,指导学生行为向全面健康方向发展。
How to effectively mine students ' behavior data is an important content to improve students' information management level. Aiming at the problems of imperfect student information management platform and low mining accuracy,a combination model based on decision tree,neural network and naive Bayesian algorithm is established to analyze and predict student behavior based on Spark. At the same time,campus behavior such as students' consumption law,living habits and learning situation is taken as an example. Large data sources are used for forecasting analysis and case validation. The results show that the prediction results of the model are consistent with the actual situation,and the average prediction error is no more than 5% which verifies the model. The law of students' behavior can be analyzed according to the characteristics of students' behavior,and the students' behavior can be guided to develop in an all-round and healthy way.
How to effectively mine students ' behavior data is an important content to improve students' information management level. Aiming at the problems of imperfect student information management platform and low mining accuracy,a combination model based on decision tree,neural network and naive Bayesian algorithm is established to analyze and predict student behavior based on Spark. At the same time,campus behavior such as students' consumption law,living habits and learning situation is taken as an example. Large data sources are used for forecasting analysis and case validation. The results show that the prediction results of the model are consistent with the actual situation,and the average prediction error is no more than 5% which verifies the model. The law of students' behavior can be analyzed according to the characteristics of students' behavior,and the students' behavior can be guided to develop in an all-round and healthy way.
引文
[1]何鑫.基于大数据的学生行为趋势挖掘系统设计与实现[D].成都:电子科技大学,2018.
[2]邓逢光,张子石.基于大数据的学生校园行为分析预警管理平台建构研究[J].中国电化教育,2017(11):60-64.
[3]李虎.大数据环境下校园网行为挖掘关键问题的研究与应用[D].郑州:郑州轻工业学院,2017.
[4]任华,张玲,叶煜.数字化校园中用户网络行为大数据的分析与监控[J].计算机与数字工程,2017,45(9):1814-1818.
[5]胡祖辉,施佺.高校学生上网行为分析与数据挖掘研究[J].中国远程教育,2017(2):26-32.
[6] SUN A,JI T,WANG J,et al. Wearable mobile internet devices involved in big data solution for education[J].International Journal of Embedded Systems,2016,8(4):293-299.
[7]毛乾任,王朝斌.基于C4. 5决策树的大学生笔记本电脑购买行为的数据挖掘[J].重庆理工大学学报(自然科学),2015,29(2):76-81.
[8]姜永超.基于数据挖掘的学生选课及学习行为分析算法研究[J].现代电子技术,2016,39(13):145-148.
[9]姜楠,许维胜.基于校园一卡通数据的学生消费及学习行为分析[J].微型电脑应用,2015,31(2):35-38.
[10]王晓雪,张家祯,郭贺,等.大数据在高校学生学习行为模式挖掘中的应用[J].智能计算机与应用,2017,7(6):167-168.
[11]梁柱.基于校园大数据的学生行为分析与预测方法研究[D].西安:西安理工大学,2017.
[12] MAILLO J,RAMíREZ S,TRIGUERO I,et al. k NN-IS:An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data[J]. Knowledge-Based Systems,2017,117:3-15.
[2]邓逢光,张子石.基于大数据的学生校园行为分析预警管理平台建构研究[J].中国电化教育,2017(11):60-64.
[3]李虎.大数据环境下校园网行为挖掘关键问题的研究与应用[D].郑州:郑州轻工业学院,2017.
[4]任华,张玲,叶煜.数字化校园中用户网络行为大数据的分析与监控[J].计算机与数字工程,2017,45(9):1814-1818.
[5]胡祖辉,施佺.高校学生上网行为分析与数据挖掘研究[J].中国远程教育,2017(2):26-32.
[6] SUN A,JI T,WANG J,et al. Wearable mobile internet devices involved in big data solution for education[J].International Journal of Embedded Systems,2016,8(4):293-299.
[7]毛乾任,王朝斌.基于C4. 5决策树的大学生笔记本电脑购买行为的数据挖掘[J].重庆理工大学学报(自然科学),2015,29(2):76-81.
[8]姜永超.基于数据挖掘的学生选课及学习行为分析算法研究[J].现代电子技术,2016,39(13):145-148.
[9]姜楠,许维胜.基于校园一卡通数据的学生消费及学习行为分析[J].微型电脑应用,2015,31(2):35-38.
[10]王晓雪,张家祯,郭贺,等.大数据在高校学生学习行为模式挖掘中的应用[J].智能计算机与应用,2017,7(6):167-168.
[11]梁柱.基于校园大数据的学生行为分析与预测方法研究[D].西安:西安理工大学,2017.
[12] MAILLO J,RAMíREZ S,TRIGUERO I,et al. k NN-IS:An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data[J]. Knowledge-Based Systems,2017,117:3-15.