基于教育大数据挖掘的在线学习干预模型应用  

The Application of Online Learning Intervention Model Based on Education Big Data Mining

作　　者：康迎曦[1] 胡晓东[2] 周细凤[1] KANG Yingxi;HU Xiaodong;ZHOU Xifeng(College of Electrical and Information Engineering,Hunan Institute of Engineering,Xiangtan 411104,China;College of Intelligence Science and Engineering,Hunan Institute of Engineering,Xiangtan 411104,China)

机构地区：[1]湖南工程学院电气与信息工程学院,湖南湘潭411104 [2]湖南工程学院智能科学与工程学院,湖南湘潭411104

出　　处：《湖南工程学院学报(社会科学版)》2024年第4期1-9,共9页Journal of Hunan Institute of Engineering(Social Science Edition)

基　　金：湖南省教育科学“十四五”规划2023年度立项课题“基于教育大数据挖掘的在线学习分析与干预研究”(XJK23BXX002)

摘　　要：利用数据挖掘技术将在线学习行为数据转化为有价值的教学信息,加以筛选和分析,能及时预测学习风险,从而为学习者提供个性化的干预策略和精准的教学服务,可促进教育决策向数字化、智能化和精准化发展。构建一个可循环迭代的在线学习干预模型,能够挖掘和分析在线学习行为数据,精确诊断学习状态,预测学业水平和学习风险,实施个性化干预策略。将模型应用于教学实践中,对在线学习数据进行回归分析,结合问卷调查和数据统计分析方法对干预的有效性进行客观检验和主观评价,结果表明,发送学习预警通知、学习资源推送、数字徽章激励、学习进度条及学习过程可视化等干预策略,可有效提高学习者的自我管理能力,维持自主学习动力,提升在线学习质量。Using data mining technology to transform online learning behavior data into valuable teaching information,screening and analysis can timely predict learning risks,provide personalized intervention strategies and precise teaching services for learners,and promote the development of educational decision-making towards digitalization,intelligence,and precision.The paper constructs a cyclic and iterative online learning intervention model that can mine and analyze online learning behavior data,accurately diagnose learning status,predict academic level and learning risks,and implement personalized intervention strategies.By applying the model to teaching practice,conducting regression analysis on online learning data,and combining questionnaire surveys and data statistical analysis methods to objectively test and subjectively evaluate the effectiveness of interventions,the results indicate that intervention strategies such as sending learning warning notifications,pushing learning resources,digital badge incentives,learning progress bars,and visualizing the learning process can effectively improve learners’self-management ability,maintain self-directed learning motivation,and improve the quality of online learning.

关 键 词：教育大数据 数据挖掘 在线学习 干预策略 

分 类 号：G434[文化科学—教育学]