基于PCA与K-均值聚类的学习者特征识别研究  

Research on Learner Characteristics Identification Based on PCA and K-means Clustering

作　　者：李铮铮 贾金娜 刘蓓蕾 马静 LI Zhengzheng;JIA Jinna;LIU Beilei;MA Jing(School of Information and Engineering,Xi'an Technology and Business College,Xi'an 710200,China)

机构地区：[1]西安工商学院信息与工程学院,陕西西安710200

出　　处：《现代信息科技》2023年第22期142-145,149,共5页Modern Information Technology

摘　　要：学习者特征识别是在线教育决策的重要支撑。深入分析了学习者特征分析的内涵、阶段划分和主要作用,构建了由人口学特征、支持性特征、动力特征、信息能力特征和策略性特征构成的在线教育学习者特征五元模型,设计了基于PCA和K-均值聚类的学习者特征数据分析思路与方法,并进行了实例分析,能够为学习者特征及差异识别分析提供方法支撑。Learner characteristics identification is an important support for online education decision-making.It analyzes the connotation,stage division and main functions of learner characteristics analysis deeply,and constructs a fiveelement model of online education learner characteristics composed of demographic characteristics,supportive characteristics,dynamic characteristics,information ability characteristics and strategic characteristics.It designs the idea and method of learner characteristics data analysis based on PCA and K-means clustering,and conducts an example analysis.It can provide methodological support for learner characteristics and difference identification analysis.

关 键 词：主成分分析 K-均值聚类 特征数据 数据分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]