基于代理的远程教学系统及学生模型的研究
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摘要
基于Web的远程教育是一种利用网络进行教学的新型网络应用，但是目前大多数基于Web的远程教学系统缺乏智能性和自适应性。而在众多新技术中，Agent技术尤其适合改善远程教学的不足。Agent是一个具有自主能力、交互能力、反应能力和预动能力的软件实体，能代表用户或其它程序，以主动服务的方式完成一组工作。使用Agent技术可以增加教学内容的趣味性和人性化色彩，改善教学效果，增强系统的智能性和自适应性。
本文通过分析传统基于Web的远程教学系统模型的不足，提出了一个基于Agent的远程教学系统模型。此模型共分6个模块，每个模块由多个Agent组成的。利用Agent间的协作机制，实现各模块间的知识交换与共享，形成了一个层次结构的多代理系统；利用Agent的智能推理能力，学习学生的个性特点，自适应的生成一个适合学生的教学策略，智能地组织教学环节，引导学生更好的学习，发挥更大潜力。模型的设计思想既发挥了教师的主导作用，又充分体现了学生的认知主体作用。
本文所提出的教学系统模型，以学生模型模块最为重要，它是其它模块正常运行的基础。然而目前大多数关于学生模型的研究要么复杂难以实现，要么简单有很少的推理能力，针对这种情况结合作者所参与的远程教育项目，本文提出一个多Agent的学生模型。这个模型包括学生四个方面的特点，每个方面的特点由一个Agent对它进行推理。作者通过改进Sherlock II方法，运用概念图、模糊理论，设计出学生模型中各Agent的不确定推理算法。这些推理方法，不仅有较好的推理能力，而且实现简单。并且本文所提出的方法具有通用性和扩展性。
作者把学生模型运用到学生自测系统中，通过测试，得出了一系列表结果，这些结果显示了加入学生模型的自测系统具有自适应性并且能够比较准确地推理出学生的认知水平，证明了学生模型中算法的可行性和正确性。本文所提出的设计方案具有很强的实用价值。
Web-based Distance Education, employing the network as its channel of learning and teaching, is a kind of brand-new Internet application. However, a majority of web-based distance tutoring systems are devoid of intelligence and self-adaptation. Among most of new techniques, Agent technique is particularly suitable for web-based distance tutoring system to improve its shortcomings. Agent, bearing autonomy, sociability, reactivity, and pre-activeness, is a software entity, and able to stand in for other users or programs to complete work on its own initiative. In a word, using Agent technique can increase the interest of teaching content and the color of personalization, ameliorate teaching effect and enhance intelligence and self-adaptation of tutoring system.
After analyzing the deficiencies of traditional web-based distance tutoring system models, this paper proposes an agent-based distance tutoring system model. The model is made up of six modules and each module includes a few agents. Utilizing the cooperative mechanism among agents, the model realizes knowledge exchange and share among the modules and shapes a lay-structure multi-agent system. Utilizing agent's intelligent reasoning ability, the model can learn characteristics of student's personality, adaptively build a sort of teaching strategy which adapts to student, intelligently organize teaching process and lead student to study better and to exert more potential. In addition, the designing ideas of the model not only bring a leading role of teacher into play but also fully reflect cognitive main body of student.
The key module in the model mentioned above is student model, which is basis for other modules to well work. However, now most of related researches are too complex to realize or easily implement but have little reasoning ability. Aiming at this kind of condition and combining the projects that the author has made in the field of distance education, the paper puts forward a multi-agent student model. The model involves the traits from four aspects of student and each aspect has an agent to reason its traits. By improving on Sherlock II method and adopting concept map and fuzzy theory, the author raises a new kind of reasoning way for every agent in the student model. These ways not only hold relatively good reasoning ability but also are easily realized. Moreover, the ways discussed in the paper are in possession of expansibility and can be generally used.
By introducing the student model into student self-testing system, the author gets a
series of tables which show that the testing system with the student model takes on self-adaptation and can exactly infer student's cognitive level. And the tables demonstrate that the ways in the student model are feasible and correct. Therefore, the scheme discussed in the paper has strongly practical worthiness.
本文通过分析传统基于Web的远程教学系统模型的不足，提出了一个基于Agent的远程教学系统模型。此模型共分6个模块，每个模块由多个Agent组成的。利用Agent间的协作机制，实现各模块间的知识交换与共享，形成了一个层次结构的多代理系统；利用Agent的智能推理能力，学习学生的个性特点，自适应的生成一个适合学生的教学策略，智能地组织教学环节，引导学生更好的学习，发挥更大潜力。模型的设计思想既发挥了教师的主导作用，又充分体现了学生的认知主体作用。
本文所提出的教学系统模型，以学生模型模块最为重要，它是其它模块正常运行的基础。然而目前大多数关于学生模型的研究要么复杂难以实现，要么简单有很少的推理能力，针对这种情况结合作者所参与的远程教育项目，本文提出一个多Agent的学生模型。这个模型包括学生四个方面的特点，每个方面的特点由一个Agent对它进行推理。作者通过改进Sherlock II方法，运用概念图、模糊理论，设计出学生模型中各Agent的不确定推理算法。这些推理方法，不仅有较好的推理能力，而且实现简单。并且本文所提出的方法具有通用性和扩展性。
作者把学生模型运用到学生自测系统中，通过测试，得出了一系列表结果，这些结果显示了加入学生模型的自测系统具有自适应性并且能够比较准确地推理出学生的认知水平，证明了学生模型中算法的可行性和正确性。本文所提出的设计方案具有很强的实用价值。
Web-based Distance Education, employing the network as its channel of learning and teaching, is a kind of brand-new Internet application. However, a majority of web-based distance tutoring systems are devoid of intelligence and self-adaptation. Among most of new techniques, Agent technique is particularly suitable for web-based distance tutoring system to improve its shortcomings. Agent, bearing autonomy, sociability, reactivity, and pre-activeness, is a software entity, and able to stand in for other users or programs to complete work on its own initiative. In a word, using Agent technique can increase the interest of teaching content and the color of personalization, ameliorate teaching effect and enhance intelligence and self-adaptation of tutoring system.
After analyzing the deficiencies of traditional web-based distance tutoring system models, this paper proposes an agent-based distance tutoring system model. The model is made up of six modules and each module includes a few agents. Utilizing the cooperative mechanism among agents, the model realizes knowledge exchange and share among the modules and shapes a lay-structure multi-agent system. Utilizing agent's intelligent reasoning ability, the model can learn characteristics of student's personality, adaptively build a sort of teaching strategy which adapts to student, intelligently organize teaching process and lead student to study better and to exert more potential. In addition, the designing ideas of the model not only bring a leading role of teacher into play but also fully reflect cognitive main body of student.
The key module in the model mentioned above is student model, which is basis for other modules to well work. However, now most of related researches are too complex to realize or easily implement but have little reasoning ability. Aiming at this kind of condition and combining the projects that the author has made in the field of distance education, the paper puts forward a multi-agent student model. The model involves the traits from four aspects of student and each aspect has an agent to reason its traits. By improving on Sherlock II method and adopting concept map and fuzzy theory, the author raises a new kind of reasoning way for every agent in the student model. These ways not only hold relatively good reasoning ability but also are easily realized. Moreover, the ways discussed in the paper are in possession of expansibility and can be generally used.
By introducing the student model into student self-testing system, the author gets a
series of tables which show that the testing system with the student model takes on self-adaptation and can exactly infer student's cognitive level. And the tables demonstrate that the ways in the student model are feasible and correct. Therefore, the scheme discussed in the paper has strongly practical worthiness.
引文
[1] 许彦青.申瑞民.张同珍.申丽萍.加强学习算法的智能多代理在远程教学中的应用.计算机工程.2001.Vol.27，No.8.p125-127
[2] 武法提.基于代理的远程教学系统.全国现代远程教育资源建设第一期研讨班讲义.p1-2
[3] 符云清.适合远程教育交互协作环境的网络通信协议研究[博士学位论文].重庆.重庆大学.2001.p3-9
[4] M Tokoro. Computational Field Model: Toward a New Computing Model/Methodology for Open Distributed Environment. Proceeding of 2nd IEEE Workshop on Future Trends in Distributed Computing System. Sept 1990
[5] L Gasser anf M Huhns. Distributed Artificial Intelligence. Pittman London,1989 (2)
[6] 何炎祥.陈莘萌.Agent和多Agent系统的设计与应用.第一版.武汉大学出版社.2001.6.
[7] 刘大有.杨鲲.陈建中.Agent研究现状与发展趋势.软件学报.2000.Vol.11.No.3.p315-321
[8] 毛新军.王怀民.Agent技术及其标准化.计算机科学.2001.Vol.28.No.4.p1-4
[9] 申瑞民.舒蓓.个性化的远程教学模型.
[10]何茜.现代远程教育教学效果反馈模型的研究[硕士学位论文].重庆.重庆大学.2001
[11]董少春.金莹.徐巧慧.陆现彩.徐士进.E-Learning中的教学设计.全国高等学校教育技术协作委员会第二届年会学术交流会.重庆.2001.12
[12]Konstantinos Solomos. Nikolaos Avouris. Learning from Collaborating Intelligent Tutors:An Agent-Based Approach. Journal of Interactive Learning Research.1999.Vol.10.No.3.4.p243-262
[13]Ashraf Saad, PhD. A Multi-Agent Spreading Activation Network Model for Online Learning Objects.
[14]Fuhua Lin. Pete Holt. Larry Korba. Timothy K.Shih. A Framework for Developing Online Learning Systems
[15]申瑞民.汤轶阳.基于概念图的个性化自主学习分析模型及实现.计算机科学.2001.Vol.28.No.10.p39-42
[16]Mark Urban-Lurain. Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology.
[17]蔡自兴.徐光佑.人工智能及其应用.第二版.清华大学出版社.
[18]Adriana Soares Pereira. Claudio Fernando Resin Geyer. A Pedagogical Agent for Educational Environments in the Internet
[19]Dongming Xu. Huaiqing Wang. Kaile Su. Intelligent Student Profiling with Fuzzy Models. Proceedings of the 35th Hawaii Conference on System Sciences-2002
[20]R.Stathacopoulou.G.D.Magoulas. M.Grigoriadou. Neural Network-based Fuzzy Modeling of the Student in Intelligent Tutoring Systems
[21]Joseph E.Beck. Beverly Park Woolf. Using a Learning Agent with a Student Model
[22]Hugo Gamboa. Ana Fred. Designing Intelligent Tutoring Systems: A Bayesian Approach
[23]Cristina Conati. Kurt VanLehn. POLA: a student modeling framework for Probabilistic On-Line Assessment of problem solving performance
[24]Brent Martin. Constraint-Based Modeling: Representing Student Knowledge
[25]Paul T.Baffes. Raymond J.Mooney. A Novel Application of Theory Refinement to Student Modeling. Proceedings of the Thirteenth National Conference on Artificial Intelligence 1996.p403-408
[26]John Self. Formal Approaches to Student Modeling. AAI/AI-ED Technical Report No.92
[27] Slavomir Stankov . Student Model Developing for Intelligent Tutoring Systems.
[28]周学海.周立.龚育昌.赵振西.自适应超媒体技术及其在智能化CAI中的应用.计算机工程与应用.2001.2.p102-114
[29]赵振宇.徐用懋.模糊理论和神经网络的基础与应用.第一版.清华大学出版社 广西科学技术出版社. 1996
[30]Joseph Giarratano. Gary Riley著.印鉴.刘星成.汤庸译.专家系统原理与编程.第一版.机械工业出版社.2000
[31]Nicola Capuano. Marco Marsella. Saverio Salerno. ABITS: An Agent Based Intelligent Tutoring System for Distance Learning
[32]Barry Kort. External Representation of Learning Process and Domain Knowledge: Affective State as a Determinate of its Structure and Function. AI-ED 2001
[33]Katz. S.,Lesgold.A.. Eggan. Gordin,M.. Modeling the student in Sherlock II. Journal of Artificial Intelligence in Education 3(4).p495-518
[34]Gurer,D.. desJardins,M.. Schlager. M.. Representing a student's learning states and transitions. the 1995 American Association of Artificial Intelligence Spring Symposium on Representing Mental States and Mechanisms
[35]Anthony Jameson. Numerical Uncertainty Management in User and Student Modeling: An Overview of Systems and Issues
[2] 武法提.基于代理的远程教学系统.全国现代远程教育资源建设第一期研讨班讲义.p1-2
[3] 符云清.适合远程教育交互协作环境的网络通信协议研究[博士学位论文].重庆.重庆大学.2001.p3-9
[4] M Tokoro. Computational Field Model: Toward a New Computing Model/Methodology for Open Distributed Environment. Proceeding of 2nd IEEE Workshop on Future Trends in Distributed Computing System. Sept 1990
[5] L Gasser anf M Huhns. Distributed Artificial Intelligence. Pittman London,1989 (2)
[6] 何炎祥.陈莘萌.Agent和多Agent系统的设计与应用.第一版.武汉大学出版社.2001.6.
[7] 刘大有.杨鲲.陈建中.Agent研究现状与发展趋势.软件学报.2000.Vol.11.No.3.p315-321
[8] 毛新军.王怀民.Agent技术及其标准化.计算机科学.2001.Vol.28.No.4.p1-4
[9] 申瑞民.舒蓓.个性化的远程教学模型.
[10]何茜.现代远程教育教学效果反馈模型的研究[硕士学位论文].重庆.重庆大学.2001
[11]董少春.金莹.徐巧慧.陆现彩.徐士进.E-Learning中的教学设计.全国高等学校教育技术协作委员会第二届年会学术交流会.重庆.2001.12
[12]Konstantinos Solomos. Nikolaos Avouris. Learning from Collaborating Intelligent Tutors:An Agent-Based Approach. Journal of Interactive Learning Research.1999.Vol.10.No.3.4.p243-262
[13]Ashraf Saad, PhD. A Multi-Agent Spreading Activation Network Model for Online Learning Objects.
[14]Fuhua Lin. Pete Holt. Larry Korba. Timothy K.Shih. A Framework for Developing Online Learning Systems
[15]申瑞民.汤轶阳.基于概念图的个性化自主学习分析模型及实现.计算机科学.2001.Vol.28.No.10.p39-42
[16]Mark Urban-Lurain. Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology.
[17]蔡自兴.徐光佑.人工智能及其应用.第二版.清华大学出版社.
[18]Adriana Soares Pereira. Claudio Fernando Resin Geyer. A Pedagogical Agent for Educational Environments in the Internet
[19]Dongming Xu. Huaiqing Wang. Kaile Su. Intelligent Student Profiling with Fuzzy Models. Proceedings of the 35th Hawaii Conference on System Sciences-2002
[20]R.Stathacopoulou.G.D.Magoulas. M.Grigoriadou. Neural Network-based Fuzzy Modeling of the Student in Intelligent Tutoring Systems
[21]Joseph E.Beck. Beverly Park Woolf. Using a Learning Agent with a Student Model
[22]Hugo Gamboa. Ana Fred. Designing Intelligent Tutoring Systems: A Bayesian Approach
[23]Cristina Conati. Kurt VanLehn. POLA: a student modeling framework for Probabilistic On-Line Assessment of problem solving performance
[24]Brent Martin. Constraint-Based Modeling: Representing Student Knowledge
[25]Paul T.Baffes. Raymond J.Mooney. A Novel Application of Theory Refinement to Student Modeling. Proceedings of the Thirteenth National Conference on Artificial Intelligence 1996.p403-408
[26]John Self. Formal Approaches to Student Modeling. AAI/AI-ED Technical Report No.92
[27] Slavomir Stankov . Student Model Developing for Intelligent Tutoring Systems.
[28]周学海.周立.龚育昌.赵振西.自适应超媒体技术及其在智能化CAI中的应用.计算机工程与应用.2001.2.p102-114
[29]赵振宇.徐用懋.模糊理论和神经网络的基础与应用.第一版.清华大学出版社 广西科学技术出版社. 1996
[30]Joseph Giarratano. Gary Riley著.印鉴.刘星成.汤庸译.专家系统原理与编程.第一版.机械工业出版社.2000
[31]Nicola Capuano. Marco Marsella. Saverio Salerno. ABITS: An Agent Based Intelligent Tutoring System for Distance Learning
[32]Barry Kort. External Representation of Learning Process and Domain Knowledge: Affective State as a Determinate of its Structure and Function. AI-ED 2001
[33]Katz. S.,Lesgold.A.. Eggan. Gordin,M.. Modeling the student in Sherlock II. Journal of Artificial Intelligence in Education 3(4).p495-518
[34]Gurer,D.. desJardins,M.. Schlager. M.. Representing a student's learning states and transitions. the 1995 American Association of Artificial Intelligence Spring Symposium on Representing Mental States and Mechanisms
[35]Anthony Jameson. Numerical Uncertainty Management in User and Student Modeling: An Overview of Systems and Issues
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