With the rapid development of computer vision and multimedia technology, especially the visual tracking technology and network transmission, teacher-centered education is popular nowadays. The shortcomings of the conventional classroom teaching mode by manually student behavior analysis are gradually becoming less effective. Aiming at the main problems existing in the application of classroom teaching video resources in multimedia teaching, in this paper, we proposed an online classroom visual data tracking system, associated with an advanced tracking quality evaluation method based on data mining. Our proposed framework can offer a scientific basis for improving the quality of online education by discovering students’ learning patterns from their online learning data. The evaluation results can effectively demonstrated that the mining of various learning information of students is useful, and obtained the classification rules that affect the learning effect toward students. These clues can be adopted to uncover the learning effect of students and provide individual guidance for students’ learning behaviors. This work can reveal the pattern online classroom image teaching behavior from the perspective of behavior chain. We also noticed the online classroom visual tracking behavior can be divided into several components: selection, presentation, mapping, analysis and collection, as well as the analysis from students facial expression.

随着计算机视觉和多媒体技术（尤其是视觉跟踪技术和网络传输）的飞速发展，以教师为中心的教育如今很流行。传统的课堂教学模式通过人工学生行为分析的缺点正逐渐变得无效。针对课堂教学视频资源在多媒体教学中的应用存在的主要问题，提出了一种在线课堂视觉数据跟踪系统，并结合了一种基于数据挖掘的先进的跟踪质量评估方法。通过从学生的在线学习数据中发现他们的学习模式，我们提出的框架可以为提高在线教育质量提供科学依据。评估结果可以有效地证明，对学生各种学习信息的挖掘是有益的，并获得了影响学生学习效果的分类规则。这些线索可以用来揭示学生的学习效果，并为学生的学习行为提供个体指导。这项工作可以从行为链的角度揭示网络课堂形象教学行为的模式。我们还注意到，在线教室的视觉跟踪行为可以分为几个部分：选择，演示，映射，分析和收集，以及来自学生面部表情的分析。这些线索可以用来揭示学生的学习效果，并为学生的学习行为提供个体指导。这项工作可以从行为链的角度揭示网络课堂形象教学行为的模式。我们还注意到，在线教室的视觉跟踪行为可以分为几个部分：选择，演示，映射，分析和收集，以及来自学生面部表情的分析。这些线索可以用来揭示学生的学习效果，并为学生的学习行为提供个体指导。这项工作可以从行为链的角度揭示在线课堂图像教学行为的模式。我们还注意到，在线教室的视觉跟踪行为可以分为几个部分：选择，演示，映射，分析和收集，以及来自学生面部表情的分析。