作　　者： ;

机构地区： 华南师范大学

出　　处： 《中国电化教育》 2020年第5期120-127,共8页

摘　　要： 课堂教学行为数据的采集与分析能有效反映教学效果,以往的课堂教学行为分析多采用教学视频打点分析、课堂观察量表等方法对课堂教学进行评价。在云计算与大数据服务支持的背景下,涌现了大量基于云+端应用模式的智慧教学环境,使教学行为数据的自动采集与分析成为可能。该文从教师专业发展的角度,将教育数据挖掘方法应用于智慧课堂环境下教师行为数据的可视化分析与应用,提出一种面向智慧课堂教师教学模式的频繁序列挖掘算法和聚类分析方法,通过教学视频案例的分析验证,发现针对某一具体学科聚类分析得到的优秀教师结果簇与实际教学中的优秀教师表现出较强的一致性,且各学科的智慧课堂教学模式呈现不同的发展特点。该方法对提高课堂教学质量和促进教师专业能力提升具有重要的实践探索意义,为“互联网+”时代下建立教师新型的评价方式提供新思路和新方法。 The collection and analysis of classroom teaching behavior data can effectively reflect instructional effect.In the past,most of the classroom teaching behavior analysis adopted teaching video analysis,classroom observation scale and other traditional methods to evaluate classroom teaching.Under the background of cloud computing and big data service support,a large number of intelligent teaching environments based on cloud application model have emerged,which make it possible to collect and analyze teaching behavior data automatically.From the perspective of teacher professional development,this paper applies educational data mining methods to the visual analysis and application of teacher behavior data in smart classroom,and proposes a frequent sequence mining algorithm and cluster analysis method for the teaching model in smart classroom.Through the analysis and verification of teaching video cases,it is found that the cluster of excellent teachers obtained by cluster analysis of a specific subject shows a strong consistency with the excellent teachers in practical teaching,and the smart classroom teaching mode of each subject presents different development characteristics.This method has great practical significance for improving the quality of classroom teaching and promoting the professional competence of teacher.And it also provides new ideas and methods for establishing a new evaluation method for teachers in the“Internet+”era.

关 键 词： 智慧课堂 课堂教学行为数据 数据挖掘分析方法 教师专业发展

分 类 号： [G434]

领　　域： []