慕课授课中的学生听课行为自动分析系统  

Auto Analysis System of Students Behavior in MOOC Teaching

作　　者：戴亚平[1,2] 杨方方 赵翰奕 贾之阳 广田熏 DAI Ya-Ping;YANG Fang-Fang;ZHAO Han-Yi;JIA Zhi-Yang;HIROTA Kaoru(School of Automation,Beijing Institute of Technology,Bei-jing 100081;National Laboratory of Intelligent Control and Decision of Complex Systems,Beijing 100081;The 6th Research Institute of China Electronics Corporation,Bei-jing 100081)

机构地区：[1]北京理工大学自动化学院,北京100081 [2]复杂系统智能控制与决策国家重点实验室,北京100081 [3]华北计算机系统工程研究所(信息产业部电子第六研究所),北京100081

出　　处：《自动化学报》2020年第4期681-694,共14页Acta Automatica Sinica

摘　　要：为了解决在线课程(Massive open online course, MOOC)授课过程中,缺乏对于学生学习情况的跟踪与教学效果评估问题,本文依据视频信息对学生行为进行建模,提出了一种评判学生听课专心程度的行为自动分析算法.该算法能够有效跟踪学生的学习状态,提取学生的行为特征参数,并对这些参数进行D-S融合判决,以获得学生的听课专注度.经过多次实验的结果表明,本文采用的方法能够有效评判学生在授课期间的专心程度,在数据融合上,与贝叶斯推理方法相比,采用D-S融合方法能有效提高实验结果的准确性和可靠性.Aiming at solving the problems of students learning behavior tracking and instructors teaching evaluation in massive open online course(MOOC),a modeling approach of student attention is proposed first,then an automatic behavior analysis and decision making fusion algorithm(ABA)is proposed to evaluate the concentration of the students during lectures.The proposed method can effectively track the student learning state and acquire the characteristic parameters of the student,and then give the concentration evaluation of the student after data fusion and decision making.Multiple experiments are carried out using the approach proposed in this paper,the results show that the proposed method can effectively reduce the uncertainty in student behavior decision making.

关 键 词：学生注意力建模 特征提取 决策融合算法 慕课 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] G434[自动化与计算机技术—计算机科学与技术]