摘要
当前大部分高等数学辅助教学系统缺少对不同学生学习情况的详细分析,不能对学生的学习效果进行预测,因此难以为学生提供个性化指导。为此,文章设计了一种新的基于数据挖掘的高等数学辅助教学系统。该系统的核心模块是决策分析模块,该模块基于C4.5算法框架,依据Fayyad边界点判定原理降低分割阈值点的判定数,并采用等价无穷小原理简化了信息熵的计算,克服了C4.5算法计算量大的弊端;该模块生成决策树,通过IF-THEN形式产生分类规则,实现成绩预测。实验结果表明,所设计系统能够有效实现数据预处理,完成学习效果预测,为学生提供个性化指导。
Existing auxiliary teaching systems of advanced mathematics usually lack detailed analysis and prediction of the students'learning effect,so it could not provide personalized help for students.A new auxiliary teaching system is designed based on data mining.The key module of the system is the decision and analysis module,which is built based on C4.5 algorithm framework.We overcome the limit of the heavy computation load of C4.5 algorithm by reducing the validated number of the threshold values according to the principle of Fayyad boundary point determination and simplifying the calculation of the entropy according to the principle of equivalent infinitesimal.The decision and analysis module produces a decision tree,generates a set of classification rules of IF-THEN form,and predicts the students'grades.Experimental results have shown that the designed system could effectively implement the data preprocessing,predict the grades and provide individualized guidance.
作者
梁锡军
渐令
LIANG Xi-Jun;JIAN Ling(College of Science,China University of Petroleum(East China),Qingdao,266580,China;School of Economics and Management,China University of Petroleum(East China),Qingdao,266580,China)
出处
《高等理科教育》
2019年第5期38-43,共6页
Higher Education of Sciences
基金
中国石油大学(华东)教学改革项目“新工科‘最优化方法’类课程算法教学模式与案例设计”(项目编号:JY-B201857)
中国石油大学(华东)青年教师教学改革项目“‘高等数学’数值实例库建设”(项目编号:QN201822)
关键词
数据挖掘
高等数学
辅助教学系统
C4.5算法
data mining
advanced mathematics
auxiliary teaching system
C4.5 algorithm
