学习情绪研究在远程教学系统中的应用探索

将学习情绪的研究应用到远程教学系统中.针对传统智能教学系统在情感方面的缺陷,介绍了远程教学系统的设计,智能教学系统的模型.该系统主要由情绪模型、认知模型、交互模型、教师模型和学生模型等主要模块构成.概括论述了人脸检测和面部表情识别的一般方法,人机情感交互系统过程和情感模型的建立流程.综合OCC情绪模型和具有反向情绪的二维情绪模型提出了一种新的适合学习情绪研究的情感建模方法,实现远程教学中认知和情感的交互.     

E-Learning中情绪认知个性化学生模型的研究*

为了提高E-Learning情绪教学的适应性和教学效果,针对传统学生模型的不足,引入人格、学习情绪及学习风格。通过OCC三维情绪空间描述学习情绪和丹尼尔·沙博人格划分理论进行情绪调节,通过美国心理学家布鲁姆的认知理论描述学生的认知能力,通过Felder-Silverman学习风格并结合支持向量机技术描述学习偏好的个性化特征。将情绪、认知、学习风格相结合构建一个完善的适合E-Learning教学的学生模型。通过将此学生模型应用到E-Learning教学中,不仅可以解决网络教学系统的情感缺失,而且大大提高了实     

智能教学系统中认知型学生模型的建立

在智能教学以学为主的学习模式中,学生模型是提供真正智能化指导的基础,该文提出在基于网络的智能教学系统中建立认知型学生模型的一种方法,介绍基于这种认知型学生模型的一个基于网络的小学数学智能教学系统,并对该系统的实验结果进行分析。     

基于情感学习本体的教学反馈策略生成算法的研究

研究如何采用本体建模的方法解决智能教学系统中的情感理解与情感反馈的问题。构建了一个情感学习本体用于描述在学习过程中出现的与情感相关的概念及概念之间的关系,设计了基于该本体的教学反馈策略的生成算法,可以在不需要和学生进行问答交互的情况下获得学生学习过程的感知和理解状态,可以更好地理解学生的学习状态以提供更好的基于"情感触发"的认知和情感支持。并将情感学习本体和一个C语言程序设计领域知识本体以及反馈策略生成算法应用到一个以教学视频为主体学习资料的智能教学系统中。应用实例表明,该教学反馈策略生成算法能对学习者提供有效的认知反馈和情感反馈。     

基于人脸表情识别的智能网络教学系统研究

针对传统智能网络教学系统中存在情感缺失的问题,探索性地设计并提出了一种基于人脸表情识别的智能网络教学系统模型.该模型以教育心理学为理论基础,以人脸面部表情识别为关键技术,通过捕捉和识别学习者的表情,判断和理解其情绪状态,然后根据学习者特定的情绪状态给出相应的情感鼓励或情感补偿.该系统模型促进了人机和谐交互,从一定程度上补偿了网络教育中的情感缺失,为解决网络教育中的情感缺失并实现和谐人机情感交互做出了有益的基础性工作.     

基于情感识别的智能教学系统研究

针对传统的智能教学系统(ITS)在情感方面的缺失,提出了基于情感识别技术的ITS模型.该系统模型在传统的教学系统上新增情感识别模块,利用人脸表情识别以及文本识别等技术所构建,可以获取和识别学生的学习情感,并根据学习情感进行相应的情感激励策略,实现情感化的教学.     

基于MAS的个性化JAVA智能教学系统建模

大多数智能化教学系统以呈现教学材料为主,对学生提供被动式的教学,忽略了学生的个性特征以及个性化需求,群体协作学习要求,而人工智能领域的Agent理论为建立智能化的教学提供了可能.文中利用Agent理论改进传统的基于web的远程教学网站,尝试建立满足学生个性化需求的智能教学系统.介绍了通过多Ageat建立JAVA智能教学系统的模型.该模型根据学生的认知水平和协作学习要求建立教学策略,实现教学环节和测试环节的交互,争取实现针对不同程度的学生实施个性化的教学.     

基于MAS的个性化JAVA智能教学系统建模

大多数智能化教学系统以呈现教学材料为主,对学生提供被动式的教学,忽略了学生的个性特征以及个性化需求,群体协作学习要求,而人工智能领域的Agent理论为建立智能化的教学提供了可能。文中利用Agent理论改进传统的基于Web的远程教学网站,尝试建立满足学生个性化需求的智能教学系统。介绍了通过多Agent建立JAVA智能教学系统的模型。该模型根据学生的认知水平和协作学习要求建立教学策略,实现教学环节和测试环节的交互,争取实现针对不同程度的学生实施个性化的教学。     

基于Web和数据挖掘的ITS系统的设计与实现

网络化和智能化是计算机辅助教学发展的两大趋势,基于网络环境的智能教学系统(ITS)的研究已经成为当前和今后相当长时期的一个重要课题。本文结合“首都师范大学智能教学系统”的开发过程,主要讨论了系统模型的构建、数据挖掘技术的应用以及知识库、教师模型、认知型学生模型的建构、基于模糊理论的知识表示和推理等关键技术和实现方法。目的在于借助通用的、一般的技术手段,为学习者提供一个自主的、智能化、个性化的、有利于学生意义建构的学习环境。     

基于OCC模型的E-learning系统情感建模

根据OCC模型理论,提出一种在e-learning系统中基于认知评价的学生情感识别模型。采用模糊推理方法实现学生对学习事件的期望度推理,并通过构建动态贝叶斯网络对所构建的模型进行了计算机仿真测试和评估,结果验证了模型的合理性和有效性,从而为构建具有情感智能的e-learning系统提供了一种新的情绪识别模型和架构。     

Agent based affective tutoring systems: A pilot study

An important trend in the development of Intelligent tutoring systems (ITSs) has been that providing the student with a more personalized and friendly environment for learning. Many researchers now feel strongly that the ITSs would significantly improve performance if they could adapt to the affective state of the learner. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. However, ATSs are not widely employed in the tutoring system market. In this paper, a survey was conducted to investigate the critical factors affecting learner’s satisfaction in ATSs based on an ATS developed by us. The results revealed that learner’s attitude toward affective computing, agent tutor’s expressiveness, emotion recognition accuracy, number of emotions recognized by agent tutor, pedagogical action and easy of the use of the system have significant influence on learner’s satisfaction. The results indicate institutions how to further strengthen the ATSs’ implementation.     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

Training and assessing numerical abilities across the lifespan with intelligent systems: The example of Baldo

Baldo is an intelligent system that can be used to assess and train numerical abilities across the lifespan, especially for children and elderly. Baldo includes digital and tangible materials to propose activities, built on the game-based learning approach, strongly grounded on well-known and experimentally supported theories about numerical and mathematical cognition. Moreover, Baldo has a special module devoted to motivational and affective dimensions related to mathematic cognition and a tutoring system to select specific activities that are adequate to the actual and potential level of the people involved in the training pathway.     

An intelligent tutoring module controlled by BDI agents for an e-learning platform

INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the student’s profile.     

基于情感建模的教学辅助系统的研究*

设计了一个以情感交互为核心的人性化的教学辅助系统.该系统综合了情感计算、计算机图形图像技术和移动Agent技术.系统以心理学和人工心理理论为基础构建了情感认知模型,定义了情绪空间、基本情绪和基本学习心理状态,并建立了基本情绪和基本学习心理状态与情绪空间的映射关系.通过情感认知模型对识别表情的处理,得到学生的学习心理状态及状态评价值.采用基于图像处理的人脸检测、表情识别和姿态识别方法编程实现了教学辅助系统.     

Java课程网络资源的建设与探索

本文以当前最热门的程序设计语言之一的Java课程的网络课程建设为例,论述了在网络课程建设中如何对学生进行类型划分以及相应的课程建设工作。还论述了专题知识建设的方法、针对初级和中级学习者的案例程序库和项目案例库的建设以及自我测试模块的建设,讨论了提高学生实践能力和就业能力的SCJP(Sun认证Java程序员)业界认证考试辅导模块的建设。     

基于Web的个性化学习系统的设计

为改善基于Web学习系统存在的不足,提出了一个基于数据挖掘技术的个性化学习系统模型,并详细描述了应用决策树及BP神经网络算法对个性化导航模块设计的方法.应用决策树方法,根据学生初始注册信息,为学生的学习能力进行分类;应用BP神经网络算法,对经过预处理的有用的教学数据进行挖掘,以得出学生对知识点的掌握情况;在分析对比学生的学习状态与课程要求的基础上为学生提供下一步学习的导航信息.基于该模型实现的个性化学习系统真正体现了因材施教的教育理念.