摘要
针对现阶段高校教学数据库中积累的成绩数据量大,而教育者从中获取的信息少的现状。为此,结合关联规则算法挖掘频繁项目集的特点,利用改进的Apriori算法对学生成绩数据进行分析处理,找出数据中隐藏的课程关联规则,将这些规则用于学生成绩预警,及时找出可能出现不及格的课程,对部分学生给出警告,加强学习监督。实验结果表明,改进的Apriori算法的效率明显优于改进前,得出的关联规则可以作为学生成绩的预警因子。
At present,there is a large number of student performance data in college teaching database,but the educators get very little valuable information：Combining the characteristics of the association rules to mine the frequent itemsets,a method that using the improved Apriori algorithm to deal with the student achievement data was put forward.The hidden relationship in lessons was found out and warning prompt was giuen,which is benefit to timely find out the possible failing classes.A warning was given to some students and learning supervision was strengthened.The experimental results show that improved Apriori algorithm is more efficient than the original algorithm,and association rules obtained can be used as warning factors of the student＇s grade.
出处
《计算机工程与设计》
北大核心
2015年第3期679-682,752,共5页
Computer Engineering and Design
