基于多数据平台的学生学业预警模型构建研究

Research on Construction of Student Academic Early Warning Model Based on Multi Data Platform

传统的基于数据挖掘技术的学业表现研究存在数据来源单一、学习行为静态、数据关联模型准确度低等问题,如何对学业表现进行实时动态精确监测,更好地为教育管理者服务,实现学生学业预警,是目前较难解决的问题。针对以上问题,借助学校的多个平台数据系统,围绕学业表现预警问题,采用当下前沿的深度学习方法构建模型,进行学习表现预测,研究成果为学校和教师及时进行调整并给出个性化指导提供有益思路。

The traditional academic performance research based on data mining technology has some problems,such as single data source,static learning behavior,low accuracy of data association model,etc.It is a difficult problem to solve at present that conduct real-time dynamic and accurate monitoring of academic performance,better serve educational administrators and achieve student academic early warning.In view of the above problems,this paper uses the multiple platform data systems of the school to build a model based on the current cutting-edge deep learning method to predict the learning performance.The research results provide useful ideas for schools and teachers to timely adjust and provide personalized guidance.