Research on Group User Portrait of Online Education Platform Based on Big Data  

基于大数据的在线教育平台群体用户画像研究

作　　者：TONG Wen-jing WANG Guo-peng SONG Li-zhe HU Ya-bao SI Zhan-jun 仝文静;王国鹏;宋丽哲;胡亚宝;司占军(天津科技大学人工智能学院,天津300457;国家开放大学,北京100039;数字化学习技术集成与应用教育部工程研究中心,北京100039)

机构地区：[1]College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China [2]The Open University of China,Beijing 100039,China [3]Engineering Research Center of Integration and Application of Digital Learning Technology,Ministry of Education,Beijing 100039,China

出　　处：《印刷与数字媒体技术研究》2024年第6期124-134,共11页Printing and Digital Media Technology Study

基　　金：2022年北京市教育科技“十四五”规划重点项目--基于智能分析的在线高等教育学习者学业评价与素质提升研究(No.CHAA22059)。

摘　　要：With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.随着大数据的飞速发展,在线教育可以借助大数据技术实现个性化、智能化的教育模式,来提高用户的学习效果和满意度。本研究以国开在线教育平台用户为研究对象,从用户行为维度对其数据进行采集、清洗,同时采用文本挖掘的方法进行大数据分析。利用RFM(Recency,Frequency,Monetary)模型和改进后的K-Means算法构建平台用户画像,分析不同类型用户的需求及偏好。并利用中文分词展示各类型用户的关键词以及其使用频次的词云图,确定不同用户的关注点,为后续课程推荐及精准营销提供便利。实验结果表明,改进后的K-Means算法可以很好地刻画用户行为画像,其轮廓系数指标提升到0.811,可以有效解决随机选择初始聚类中心的方法受离群样本点影响而导致结果不一致的问题。

关 键 词：User portrait Online education platform RFM model CLUSTERING 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]