In recent years,some researchers have used machine learning to mine the relationship among the items knowledge and provide support for teachers'feedback.This study is based on Apriori algorithm,through simulation and empirical research,mining and analyzing test papers with different knowledge point structures.The simulation study shows that Apriori algorithm can mine the association rules among knowledge points for the test questions with complex attributes of knowledge points,and has a high accuracy.With the increase of samples,the accuracy of Apriori algorithm in mining test papers with complex knowledge points increases.The empirical study finds that Apriori algorithm can mine the association rules among knowledge points in the test papers of Chinese,mathematics,primary school science,middle school physics in primary and secondary schools.And the mining results of association rules among disciplines are different.After optimizing the granularity and deleting the basic knowledge points,Apriori algorithm can mine the association relationship between Chinese and mathematics residual knowledge points in primary and secondary schools,and the mining of interdisciplinary knowledge points in primary schools needs to be improved.
关键词
知识关联规则/Apriori算法/学科/跨学科/不同知识点结构
Key words
Knowledge Association Rules/Apriori Algorithm/Disciplin/Interdisciplin/Differences in Knowledge Point Structure
维普
万方数据