基于深度学习的初中数学教师课堂言语画像的构建研究  

Research on the Construction of Math Teachers’Classroom Speech Portraits in Junior High School Based on Deep Learning

作　　者：高金慧 马玉慧[2] GAO Jinhui;MA Yuhui(Cangzhou No.8 Middle School,Cangzhou 061000,China;College of Education Science,Bohai University,Jinzhou 121000,China)

机构地区：[1]沧州市第八中学,河北沧州061000 [2]渤海大学教育科学学院,辽宁锦州121000

出　　处：《开放学习研究》2024年第2期11-18,共8页Journal of Open Learning

摘　　要：有研究表明,以教师课堂言语为载体的教学行为,可以占课堂中所有行为的80%左右,说明课堂的教学活动主要是靠教师的言语来进行的。随着大数据、人工智能的不断发展,将深度学习应用于教育领域已经成为趋势,本研究采用深度学习和知识图谱技术来分析教师课堂言语。首先构建了初中数学教师课堂言语的8个分类维度,前7个维度采用深度学习算法,选取了卷积神经网络(CNN)和循环神经网络(RNN)两个模型对70节初中数学课产生的10000多条教师言语文本进行分类,得出CNN模型的准确率为83.09%,RNN模型的准确率为80.26%。最后1个维度采用知识图谱技术提取教师课堂言语中带有前驱知识点的语句。最后结合两种技术生成了6张教师课堂言语画像。研究表明,采用深度学习算法和知识图谱技术对教师课堂言语进行分类的方法是切实可行的。同时,画像的产生给教师规范化使用课堂言语提供了有力的参考。Studies show that teaching behavior based on teachers’speech account for about 80%of all the behaviors in the classroom,indicating teachers’speech as main carrier of classroom teaching activities.Continuous development of big data and artificial intelligence promotes deep learning application in thefield of education.This article used deep learning and knowledge graph technology to analyze teachers’classroom speech.Firstly,eight dimensions of junior high school mathematics teachers’classroom speech were constructed.For thefirst seven dimensions,two models of Convolutional Neural Network(CNN)and Recurrent Neural Network(RNN)based on deep learning algorithms were selected to classify more than 10000 teachers’speech texts generated from 70 junior high school mathematics lessons.Results showed that the accuracy of the CNN model and RNN model was 83.09%and 80.26%.For the last dimension,knowledge graph technology was used to extract sentences with antecedent knowledge points in teacher’classroom speech.Then the above technologies were combined to generate 6 teachers’classroom speech portraits.By comparing the accuracy of CNN model and RNN model,method of deep learning algorithm and knowledge graph technology to classify teachers’classroom speech was shown to be feasible.Meanwhile,the generation of portraits provided a strong reference to standardize teachers’classroom speech.

关 键 词：课堂言语 深度学习 教师画像 

分 类 号：G434[文化科学—教育学]