基于大数据的农业院校学生在线学习行为研究  

Research on online learning behavior based on big data for agricultural college student

作　　者：潘贺 李秀英 王鑫 PAN He;LI Xiuying;WANG Xin(Information Center,Jilin Agricultural University,Changchun 130118,China;College of Information Technology,Jilin Agricultural University,Changchun 130118,China)

机构地区：[1]吉林农业大学信息化中心,长春130118 [2]吉林农业大学信息技术学院,长春130118

出　　处：《计算机应用文摘》2024年第12期22-24,28,共4页Chinese Journal of Computer Application

基　　金：吉林省教育科学“十四五”规划课题:智慧校园视角下面向学生多源行为特征的高校学业成绩预测模型研究(GH22884)。

摘　　要：随着信息技术的快速发展和广泛应用,在线学习已成为当前教育的重要组成部分。学习者在网络学习过程中产生的海量行为数据可以深层次地反映其特征。文章以吉林农业大学在线开放课程“智慧农业”的学习行为数据为研究对象,采用大数据分析中的加权K-means聚类算法对学习者的行为属性进行分析,旨在揭示网络环境下学习者行为特征与学习效果之间的内在规律。根据实验结果,学习者被分为沉浸型学习者、投入型学习者、稳定型学习者和低参与度学习者4类,文章分别从学业过程预警、教学方法和推送服务的角度提出了教学改进的方法。With the rapid development and widespread application of information technology,online learning has become an important component of current education.The massive amount of behavioral data generated by learners during online learning can deeply reflect their characteristics.The article takes the learning behavior data of Jilin Agricultural University̓s online open course“smart agriculture”as the research object,and uses the weighted K-means clustering algorithm in big data analysis to analyze the behavioral attributes of learners,revealing the inherent laws between learner behavior characteristics and learning effectiveness in the network environment.According to the experimental results,learners were divided into four categories:immersive learners,immersive learners,stable learners,and low engagement learners.Methods for teaching improvement were proposed from the perspectives of academic process warning,teaching methods,and push services,respectively.

关 键 词：农业院校 智慧农业 创新 在线学习 行为研究 

分 类 号：G645[文化科学—高等教育学]