【作 者】：
牟智佳，俞显，武法提
【关 键 词 】：
教育数据挖掘；学习分析；研究热点；演进趋势；可视化分析
【栏 目】：
历史与国际比较
【中文摘要】：
教育数据量的急剧增长、类型的多样性与可获取性以及教育计算的兴起推动了教育数据研究的发展，并引
起了国际研究者的深度关注与探索。研究以Web of Science 数据库中的教育数据挖掘类文献为样本数据，以知识图谱分
析、社会网络分析、聚类分析为研究方法，分别采用CiteSpcae III、Unicet 6.0、Bicomb 2.0、SPSS 20.0 对数据进行定量分
析。研究结果显示，数据刻画学习者模型、生成有效学习的教学支持、学习行为模式与特征、学习表现预测、学习反馈与
评价等为主要研究热点。最后，文章从学习情感识别与计算、人工智能分析与应用、学习推荐系统、个性化学习路径等方
面对研究趋势进行讨论。
【英文摘要】：
The development of educational data research has been pushed forward by rapid growth of
the amount of educational data, the diversity of its types, its accessibility and educational computing as
well. Many international researchers pay much attention to it. This study, which takes the educational data
mining literature in Web of Science database as sample data and adopts knowledge mapping analysis,
social network analysis and clustering analysis as research methods, conducts a quantitative analysis by
using CiteSpace III, Unicet6.0, Bicomb2.0 and SPSS20.0. The results indicate that describing learner's
model based on data, instructional support generating effective learning, learning behavior patterns and
characteristics, learning performance prediction, learning feedback and evaluation are among current
research hotspots. Then, this study predicts the research trends from learning emotion recognition and
computation, artificial intelligence analysis and application, learning recommendation system and
personalized learning path.
【下 载】：
国际教育数据挖掘研究现状的可视化分析： 热点与趋势