摘要
基于高校图书馆大数据的大学生成绩预测对于推动高校图书馆的服务创新和高等教育数字化转型具有重要意义。文章针对鲜有图书馆利用数据用于大学生成绩预测模型构建的现状,结合高校教务处学业数据和图书馆利用数据,基于机器学习方法构建了大学生成绩预测模型。实验结果表明,对逻辑思维要求较高的科目对学生成绩有显著的正相关性;图书馆利用数据(如图书借阅、入馆次数等)与平均学分绩点(Grade Point Average,GPA)呈现明显的正相关关系。该研究旨在为高校图书馆精准化服务提供有力支持,并为高等教育数字化转型提供有益参考。
The prediction of college students’grades based on big data from university libraries is of great significance for promoting service innovation and digital transformation of higher education in university libraries.The article focuses on the current situation where few libraries use data to construct prediction models for college students’grades.Combining academic data from university academic affairs offices and library utilization data,a college student grade prediction model is constructed based on machine learning methods.The experimental results show that subjects with high requirements for logical thinking have a significant positive correlation with students’grades;There is a significant positive correlation between library utilization data(such as book borrowing,number of entries,etc.)and average GPA.This study aims to provide strong support for the precision services of university libraries and provide useful references for the digital transformation of higher education.
作者
刘存杰
解玲
李小涛
LIU Cunjie;XIE Ling;LI Xiaotao(Library,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《无线互联科技》
2024年第15期5-9,共5页
Wireless Internet Science and Technology
基金
2023年南京航空航天大学本科教育教学改革研究项目,项目名称:基于图书馆大数据的大学生学业预警模型研究,项目编号:2023JGTS15Z。
关键词
教育数据挖掘
机器学习
大学生成绩预测模型
高校图书馆
education data mining
machine learning
university student performance prediction model
university library