摘要
针对高校学生工作者任务繁多且直接管理的学生人数众多,难于对每个学生进行个性化的学习指导的实际问题,提出基于离群点检测的学生学习状态分析方法,将有限的教育资源分配给最迫切需求的学生。使用基于密度的局部离群点检测算法对学生考试成绩数据进行挖掘,找出可疑离群学生,然后对可疑离群学生进行学习状态分析。案例研究结果表明,本方法能够有效地找出学习状态异常的学生,可以提升高校学生工作者的管理效率。
The student supervisors are facing a great challenge in Chinese universities that they have a lot of work to do and serve too many students directly,so that they can hardly give a personalized learning guide for every student. We propose a method of learning state analysis of students based on outlier detection to solve this problem and allocate the limited educational resources to the neediest students. This method finds the suspicious outlying students through mining the students＇ scores based on the algorithm of density-based local outliers,and analyzes the learning state of these students. The case study shows that this method can efficiently find some students with exceptional learning state which may assist the college student supervisors in managing students more efficiently.
出处
《计算机与现代化》
2016年第3期35-40,共6页
Computer and Modernization
基金
华中科技大学教学研究基金资助项目(0122184032)
