课堂教学视频中学生学习行为识别系统的设计与实现

Gang Zhao, J. Wang, Jiaojiao Li, J. Chu, Nan Wu
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引用次数: 0

摘要

学生的学习行为不仅表现了学生对教学过程的参与,而且直接影响到教学质量。然而,手工观察和编码学生行为存在着工作量大、易受分析人员主观因素影响的问题。结合人工智能等技术,实现课堂学生行为的识别与分析,也是当前的研究趋势。现有的学生行为识别系统存在着不重视教师活动对学生行为识别的干扰,仅通过视觉信息就能识别的学生行为类别较少等问题。因此,本文采用基于视听信息的学生学习行为识别方法,开发了课堂教学视频中学生学习行为识别系统,对教学视频中的学生学习行为进行识别。首先,本文设计了一个预标注模块,用户可以在预标注模块中对教师的图像、语音和无声片段进行标注,以便对后续的学生行为进行准确识别。其次,学生行为识别模块提供了对真实课堂教学视频中八类学生行为的识别,并将结果可视化,用户可以了解当前教学视频中学生学习行为的百分比,以及个别行为类别的时间变化情况。系统的目的是帮助教师能够快速、科学地统计学生在课堂上的学习行为,掌握学生的学习动态,进行数据化的课堂教学分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation of System of Recognition of Students’ Learning Behavior in Classroom Teaching Videos
Student learning behavior not only shows student participation in the teaching process, but also affects the quality of teaching directly. However, manual observation and coding of student behaviors exist with high workload and are susceptible to subjective factors of analysts. Combining artificial intelligence and other technologies to achieve recognition and analysis of classroom student behavior is also a prevailing research trend. The current student behavior recognition system has problems such as failing to pay attention to the interference of teacher activates for student behavior recognition, fewer behavioral categories of students that can be recognized through visual information alone. Therefore, this paper develops a system of recognition of student learning behaviors in classroom teaching videos by a method on recognition of student learning behaviors based on audio-visual information to identify student learning behaviors in teaching videos. First, this paper designs a pre-annotation module in which the user marks the teacher's image, voice, and silent clips for providing accurate identification of subsequent student behaviors. Second, the student behavior recognition module provides for the recognition of eight categories of student behaviors in real classroom teaching videos and visualizes the results so that users can understand the percentage of student learning behaviors in the current teaching clip and the temporal changes of individual behavior categories. The purpose of system is to assist teachers to be able to count student learning behaviors in the classroom quickly and scientifically and grasp student learning dynamics for data-enabled classroom teaching analysis.
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