基于K-Means聚类分析的在线学习行为特征研究——以中外园林史课程为例  

Research on the Characteristics of Online Learning Behavior Based on K-Means Clustering Analysis:A Case Study of〈History of Gardens〉

作　　者：宋力[1] 杨立新[1] 郭蕊[1] 崔迪[1] SONG Li;YANG Li-xing;GUO Rui;CUI Di(College of Forestry,Shenyang Agricultural University,Shenyang 110161,Liaoning Province,China)

机构地区：[1]沈阳农业大学林学院,辽宁沈阳110161

出　　处：《沈阳农业大学学报（社会科学版）》2022年第5期615-621,共7页Journal of Shenyang Agricultural University(Social Sciences Edition)

基　　金：沈阳农业大学一流本科课程项目(KC2022431)

摘　　要：本研究依据中外园林史课程137名注册大学生的在线学习行为数据,对在线学习行为特征进行观察、相关分析和K-Means聚类分析。根据观察结果发现,包括如何计算学习成绩在内的教学设计细节对大学生的在线学习行为有直接影响;相关分析则显示,本研究中采用的10个在线学习行为指标中有七个指标与期末卷面成绩呈显著相关;以四个预设在线学习行为模式、针对10个在线学习行为指标和期末卷面成绩展开的K-Means聚类分析结果显示,七个指标的ANOVA检验达到显著性水平。据此本研究就各类在线学习行为模式中有效在线学习行为指标进行分析讨论,提出关注线上测试设计细节、完善线上课程建设和利用线上线下优势等对策建议。Based on the online learning data of 137 students from History of Gardens,the research explored students’characteristics of online learning behaviour by observation,correlation analysis and k-means clustering analysis.The observation results showed that online learning behaviour were directly influenced by the details of teaching design,including how to evaluate academic performance.Correlation analysis revealed that seven of ten online behaviour indicators were significantly correlated with the final test score.K-means clustering analysis was conducted based on four different online learning behaviour patterns as well as ten online learning behaviour indicators and the final test score.Among them,seven indicators reached the level of significance of ANOVA.In the last part of the paper,the effective learning behaviour indicators of four different online learning behaviour patterns found in the study was discussed.At last,this paper proposed the following suggestions:pay attention to the details of online course design,improve online course construction and take advantage of online and offline teaching,etc.

关 键 词：在线学习者 在线学习行为 学习模式 K-Means聚类分析 中外园林史 

分 类 号：G434[文化科学—教育学]