大数据挖掘的高校专业课程管理设置优化方法
Optimization Method for University Curriculum Management Settings Based on Big Data Mining
陈云彪1
作者信息
摘要
设计一种基于大数据挖掘的高校专业课程管理设置优化方法,从多个维度和来源(如学生成绩、课程评价、教学资源使用情况等)收集专业课程数据,实现数据的全面性和多维性.通过对其进行数据合并、去重和关联,得到数据整合的结果.采用大数据挖掘技术,对整合后的数据进行分类和特征挖掘,能够发现传统方法难以察觉的数据规律和趋势,由此构建高效专业课程管理模型,综合反映课程的多个方面.通过设定课程评估值,实现了对课程管理设置的动态评估和持续优化.在实验测试中,设计方法教学资源率较高,优化效果较好.
Abstract
Optimization method for university curriculum management based on big data mining was designed,the method collects pro-fessional course data from multiple dimensions and sources(such as student grades,course evaluations,and teaching resource usage),achieving comprehensiveness and multidimensionality of data.It performs data merging,deduplication,and association to obtain integrated data results.By employing big data mining techniques,the integrated data is classified and feature-mined,revealing data patterns and trends that traditional methods are unable to detect.Consequently,a highly efficient professional curriculum management model is con-structed,comprehensively reflecting multiple aspects of the courses.Through the establishment of course evaluation values,dynamic as-sessment,and continuous optimization of curriculum management settings are achieved.Experimental tests show that the designed method results in a higher utilization rate of teaching resources and better optimization effects.
关键词
大数据挖掘技术/高校专业课程/课程设置/课程管理/优化方法Key words
big data mining technology/university professional courses/curriculum design/course management/optimization method引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据