网络在线学习情绪检测系统研究与实现

发布时间：2019-05-05 08:32

【摘要】：在线学习是学生通过网络教学平台随时随地进行学习的一种全新的学习方式,这种在线学习方式是基于互联网技术和信息技术构成的开放式学习环境。与传统课堂式教学相比,网络教学时空分离的特性能够为人们提供方便快捷的学习途径,但其缺点是教师无法通过观察学生面部表情来分析学生的学习情绪与状态,进而不能及时调整教学策略。针对目前的网络教学系统的缺陷,本文研究和实现了具有情感交互功能的网络教学系统。本文对网络教学特点和教学心理学的进行了深入研究分析,设计了网络在线学习情感模型,该模型从认知度、兴奋度、趋避度三个维度来描述了在线学习者的情感。认知度和趋避度主要从学习者面部表情获取学习者的状态信息,兴奋度主要对人眼疲劳度进行检测从而获取学习者精神状态。基于该模型,利用图像处理技术实现了网络在线学习情绪检测系统,论文主要完成的工作包括:设计了网络在线学习情绪检测系统的人脸特征提取子模块中的检测算法,重点研究了如何减少人脸特征点的搜索时间,以满足在线学习系统的高实时性要求。构建了学习者人脸CLM模型,使用SVM分类器对认知度和趋避度的学习表情进行识别;通过提取学习者眼部特征,使用P80标准的PERCLOS方法对学习者兴奋度进行检测。基于HTML5和Java程序设计技术实现网络在线学习情感检测系统,并将其用于网络教学平台。在实现过程中充分考虑了应用场景、网络流量、服务器负载等因素,将整个图像处理与识别过程置于WEB前端完成,解决了采用传统方法将复杂的图像处理过程置于WEB服务器处理造成对服务器负载过大,不适合在线学习人数众多的网络学习的难题。本文通过对某课程下的25名学生做了对比检测实验,利用三维学习情绪模型分析得到的学生的学习状态。实验结果表明网络在线学习情感检测系统能够为教师提供了解网络在线学习中学生学习情绪的可靠途径。
[Abstract]:Online learning is a brand-new learning mode for students to study at any time and anywhere through the web-based teaching platform. This online learning mode is an open learning environment based on Internet and information technology. Compared with traditional classroom teaching, the separation of time and space in network teaching can provide a convenient and quick way for people to learn, but its disadvantage is that teachers can't analyze students' learning emotion and state by observing students' facial expressions. Therefore, the teaching strategy can not be adjusted in time. In view of the defects of the current network teaching system, this paper studies and implements the network teaching system with emotional interaction function. In this paper, the characteristics of network teaching and teaching psychology are deeply studied and analyzed, and an online learning emotion model is designed. The model describes the emotion of online learners from three dimensions: cognition, excitability and avoidance. Cognition and avoidance are mainly used to obtain learners' state information from facial expressions, and excitability is mainly used to detect human eye fatigue so as to obtain learners' mental state. Based on this model, an online learning emotion detection system based on image processing technology is implemented. The main work of this paper is as follows: the detection algorithm in the face feature extraction sub-module of the online learning emotion detection system is designed. This paper focuses on how to reduce the search time of facial feature points in order to meet the high real-time requirements of online learning system. The learner face CLM model is constructed and the SVM classifier is used to recognize the learning expression of cognitive degree and avoidance degree. The learner's eye feature is extracted and the P80 standard PERCLOS method is used to detect the learner's excitability. Based on HTML5 and Java programming technology, the online learning emotion detection system is implemented, and it is used in the network teaching platform. In the process of implementation, the application scenario, network traffic, server load and other factors are fully considered, and the whole image processing and recognition process is completed in the front end of WEB. The traditional method is used to put the complex image processing process into the WEB server processing, which results in too much load on the server and is not suitable for the online learning of a large number of people on-line. In this paper, a comparative test of 25 students in a course is carried out, and the learning state of the students is analyzed by using the three-dimensional learning emotion model. The experimental results show that the online learning emotion detection system can provide a reliable way for teachers to understand the online learning emotion of middle school students.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

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