基于考试过程和知识结构的数据挖掘算法研究  

Research on Data Mining Algorithm Based on Examination Process and Knowledge Structure

作　　者：代明竹 高嵩峰 DAI Ming- zhu, GAO Song- feng(School of Mechanical electronic and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,Chin)

机构地区：[1]北京建筑大学机电与车辆工程学院,北京100044

出　　处：《计算机科学》2018年第B06期437-441,共5页Computer Science

摘　　要：为了研究学生在不同阶段对知识点的掌握情况,基于对数据挖掘的理论研究,把知识结构与考试成绩相结合来进行数据研究。以教育测量学为基础,结合数据挖掘的决策树算法,针对原有的C4.5算法提出改进算法,应用试卷中涉及到的知识点的难易程度与知识点种类进行知识结构细化,以便确定单个学生或群体学生对知识点的掌握程度和试卷中各知识点之间的关系。结果显示,改进后算法的计算公式比原计算公式简单实用;根据决策树模型,使用剩余数据对计算公式进行验证,能够更快地得出对程序设计这个知识点的掌握是影响成绩相对重要的因素。使用测试数据对已创建的决策树进行验证,准确率为90%。最后对决策树进行可视化展示,为学生的学习安排、教师的教学方案及安排等提供有效的参考。In order to study the mastery of knowledge points at different stages of student,based on the theory of data mining,knowledge structure was combined with examination results to study data.Based on the theory of educational measurement and the decision tree algorithm of data mining,an improved algorithm was proposed according to the original C4.5 algorithm,applying the difficulty level of the knowledge points involved in the test papers and the knowledge structure to refine the knowledge structure in order to determine the degree of knowledge of individual students or groups of students and the relationship between the knowledge points.The experimental results show that the efficiency of the improved algorithm is improved,whose formula is simple and practical compared with the original formula.According to the decision tree model,the remaining data is used to verify the improved formula,and it is faster to draw the conclusion that the effect of knowledge points on programming is relatively important.Test data is used to verify the decision tree,and the accuracy rate is 90%.Finally,a visual display of the decision tree can give an effective reference for students to learn the arrangements,teachers to develop teaching programs and arrangements.

关 键 词：数据挖掘 决策树 C4.5 知识结构 试卷分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]