基于深度学习的课堂教学行为分类研究
Research on classroom behavior classification based on deep learning
深度学习正在改变许多传统研究的工作方式，教育领域亦不例外。本文从课堂教学行为的分类研究入手，首先对当前研究的现状进行了研究总结，随后对文本分类的传统算法和神经网络算法进行了详细阐释，最后基于Python和TensorFlow平台对不同模型进行了具体实现，并利用课堂实录整理的训练文本对模型进行了训练和测试，对不同分类算法的准确率、运行时间等指标进行了对比，实现了较为完整的课堂教学行为文本自动化分类方案，以期为后续研究提供一定的借鉴和思路。
eep learning is changing the traditional research method in many fields, and education is no exception.This paper focuses the work on the classification of classroom instructional behavior，within which a summary of current research is conducted firstly, then the algorithms that are used in this field are introduced and developed based on the framework of TensorFlow and the programming language of Python. With the training texts extracted from the class videos, an experiment is conducted to compare the accuracy and some other metrics among the different models used to classify the classroom instructional behavior. In the end of this paper, a summary of the work is made to be used as referrence by future researches.
宋志海、李青
教育计算技术、计算机技术科学、科学研究
深度学习，教学行为，文本分类，神经网络
deep learning instructional behavior text classification neural network
宋志海,李青.基于深度学习的课堂教学行为分类研究[EB/OL].(2018-01-08)[2025-08-23].http://www.paper.edu.cn/releasepaper/content/201801-39.点此复制
评论