On-line classroom visual tracking and quality evaluation by an advanced feature mining technique

Abstract

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.