基于数据挖掘技术的学生成绩预警应用研究
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摘要
学习成绩是评价一个学生学习情况的最重要最基础的指标,对学习成绩的分析有利于老师掌握学生的学习情况,进行针对性地进行教学辅导,而对学生而言,能提前知道自己未来课程在学习过程中出现的情况也有利于学生发现自身存在的问题并提前加以防范.现有的研究工作大多是基于对课程、历史成绩或行为数据的分析来对学生的总成绩进行预测,很少有研究将学生行为与学生课程成绩等方面结合起来综合全面的预测学生未来所有的课程的学习情况,对此,本文从一个新的角度出发,利用学生的行为、个人属性和历史成绩等三个方面数据,根据学生未来不同课程动态的进行影响因素的选择,并利用支持向量机对学生成绩进行预警,为数据挖掘技术在教育领域的应用做了一些探索性工作.
Academic achievement is the most important and most basic indicator for evaluating a student's learning situation. The analysis of academic performance is beneficial to teachers to master a student's learning situation and conduct the targeted teaching and counseling, while for the students, knowing in advance what happens to their future learning is also beneficial for students to discover their own problems which could be prevented in advance. Most of the existing research work is based on the analysis of the curriculum, historical performance or behavioral data to predict the student's total score, while few studies focus on combining a student behavior with his or her grades to comprehensively predict a student's learning in all future courses. This paper, from a new perspective, uses the three aspects data of student behavior, personal attributes and historical achievements which are identified by the influencing factors based on students' different curriculum dynamics in the future, the early warning of students' achievement is predicted with support vector machine, the experiment results show the exploratory work of data mining applied in education has certain significant meaning for the teachers as well as for students.
Academic achievement is the most important and most basic indicator for evaluating a student's learning situation. The analysis of academic performance is beneficial to teachers to master a student's learning situation and conduct the targeted teaching and counseling, while for the students, knowing in advance what happens to their future learning is also beneficial for students to discover their own problems which could be prevented in advance. Most of the existing research work is based on the analysis of the curriculum, historical performance or behavioral data to predict the student's total score, while few studies focus on combining a student behavior with his or her grades to comprehensively predict a student's learning in all future courses. This paper, from a new perspective, uses the three aspects data of student behavior, personal attributes and historical achievements which are identified by the influencing factors based on students' different curriculum dynamics in the future, the early warning of students' achievement is predicted with support vector machine, the experiment results show the exploratory work of data mining applied in education has certain significant meaning for the teachers as well as for students.
引文
[1] 周玉敏. 基于Rough集的数据挖掘在教学评价中的应用 [J]. 重庆邮电大学学报: 自然科学版, 2008, 20: 627.
[2] 吴暾华, 王萍, 刘婷. 基于支持向量机的大学生学业动态预警研究 [J]. 中国教育信息化, 2017, 23: 65.
[3] 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, 37: 26.
[4] 张静. 大数据技术在学生业绩分析中的研究与应用 [D]. 长春: 吉林大学, 2016.
[5] 刘譞. 基于学生行为的成绩预测模型的研究与应用 [D]. 成都: 电子科技大学, 2017.
[6] 袁洁婷, 高志强. 性别因素对大学生英语成绩影响的定量分析 [J]. 海外英语, 2015, 18: 68.
[7] 刘吉臻, 秦天牧, 杨婷婷, 等. 基于偏互信息的变量选择方法及其在火电厂SCR系统建模中的应用 [J]. 中国电机工程学报, 2016, 36: 2438.
[8] 曹莹, 苗志刚. 基于向量矩阵优化频繁项的改进Apriori算法 [J]. 吉林大学学报: 理学版, 2016, 54: 349.
[9] 胡世前, 姜倩雯, 凌冰, 等. 基于改进支持向量机的空气质量监测预警模型 [J]. 江苏大学学报: 自然科学版, 2016, 37: 491.
[10] Silva C, Ribeiro B. Multiclass ensemble of one-against-all SVM classifiers [C]// International Symposium on Neural Networks. Berlin, Germany: Springer International Publishing, 2016.
[11] López J, Maldonado S, Carrasco M. A robust formulation for twin multiclass support vector machine [J]. Appl Intell, 2017, 47: 1.
[2] 吴暾华, 王萍, 刘婷. 基于支持向量机的大学生学业动态预警研究 [J]. 中国教育信息化, 2017, 23: 65.
[3] 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, 37: 26.
[4] 张静. 大数据技术在学生业绩分析中的研究与应用 [D]. 长春: 吉林大学, 2016.
[5] 刘譞. 基于学生行为的成绩预测模型的研究与应用 [D]. 成都: 电子科技大学, 2017.
[6] 袁洁婷, 高志强. 性别因素对大学生英语成绩影响的定量分析 [J]. 海外英语, 2015, 18: 68.
[7] 刘吉臻, 秦天牧, 杨婷婷, 等. 基于偏互信息的变量选择方法及其在火电厂SCR系统建模中的应用 [J]. 中国电机工程学报, 2016, 36: 2438.
[8] 曹莹, 苗志刚. 基于向量矩阵优化频繁项的改进Apriori算法 [J]. 吉林大学学报: 理学版, 2016, 54: 349.
[9] 胡世前, 姜倩雯, 凌冰, 等. 基于改进支持向量机的空气质量监测预警模型 [J]. 江苏大学学报: 自然科学版, 2016, 37: 491.
[10] Silva C, Ribeiro B. Multiclass ensemble of one-against-all SVM classifiers [C]// International Symposium on Neural Networks. Berlin, Germany: Springer International Publishing, 2016.
[11] López J, Maldonado S, Carrasco M. A robust formulation for twin multiclass support vector machine [J]. Appl Intell, 2017, 47: 1.