摘要
目的学生课堂行为分析对于评估学生的课堂参与度有重要意义。提出一种新型的学生课堂行为数据的分析和处理模式,用于综合评估学生课堂参与度状况。方法基于课堂视频信息,利用Kinect传感器获取肢体骨骼以及面部特征与可观察到的学生行为之间的相关性,提取相关特征进行深度神经网络(deep neural network,DNN)分类器的构建,对不同等级的注意力集中水平进行分类;利用Kinect传感器的骨骼点信息和音频阵列进行多模态融合,对学生举手和回答问题情况进行统计。结果验证了注意力水平与学生特定行为之间确实存在相关性(等级1、等级2与看黑板相关系数分别为0.63、0.55;等级3与东张西望相关系数为0.78)。使用DNN对注意力等级分类,准确率为91.2%,较支持向量机(support vector machine,SVM)提高12.3%。使用音频阵列对学生定位识别准确率为89.0%。最终得到每个学生每节课的注意力等级图、各个注意力等级的时间占比及学生在课堂上的举手和起立发言次数,形成学生课堂参与度分析表。结论通过评估学生的课堂行为,结合课堂参与度的相关指标,能够全面客观地反映不同学生的课堂表现,并可作为教师教学的参考。
Objective Students’classroom behavior analysis is important for assessing students’classroom participation.A new analysis and processing model of students’classroom behavior data is proposed to comprehensively assess students’participation in classroom.Methods Based on classroom video information,Kinect sensor was used to obtain the correlation between limb bones and facial features and observable student behaviors.Relevant features were extracted to construct deep neural network(DNN)classifiers,and different levels of attention concentration were classified.Using the Kinect sensor’s limb bones’point information and audio array for multimodal fusion,the frequency of students’putting up hands and answering questions was counted.Results It was verified that there was a correlation between the level of attention and the specific behavior of the students(the correlation coefficients between level 1 and level 2 and the behavior of watching blackboard were 0.63 and 0.55,respectively;the correlation coefficient between level 3 and the behavior of looking around was 0.78).Using the DNN to classify attention levels,the accuracy rate was 91.2%,which was 12.3%higher than the support vector machine(SVM).The accuracy of student location recognition using audio arrays was 89.0%.Finally,each student’s attention level picture for each lesson,the proportion of each attention level,and the number of times of the students’raising their hands in the classroom as well as the number of standing up to answer questions formed a student class participation analysis table.Conclusion By assessing students’classroom behaviors and combining relevant indicators of classroom participation,they can comprehensively and objectively reflect the classroom performance of different students in the classroom,and can be used as a reference for teachers.
作者
缪佳
禹东川
MIAO Jia;YU Dongchuan(Key Laboratory of Child Development and Learning Science,Ministry of Education,Southeast University,Nanjing 210096,Jiangsu Province,China)
出处
《教育生物学杂志》
2019年第4期220-226,共7页
Journal of Bio-education
基金
国家自然科学基金(61673113)
