E-learning Evolution: From M-learning to Educational ... - CiteSeerX
E-learning Evolution: From M-learning to Educational Semantic Web and beyond Cui Guangzuo, Yang Gongyi, Chen Hu, Chen Fei, Guo Jiuling Modern Education Technology Centre, Peking Universit, .China [email protected]
Abstract: An e-learning model with ICT technology (ELM) is proposed in this paper. With this model, some education technologies and e-learning evolution are interpreted, such as network education, mobile education, ubiquitous education, educational semantic web, and etc. In the meantime, the way of how to combine new ICT technologies into education is also demonstrated. After discussing the convenience and challenge of various education technologies, a new model called intelligent education is introduced and some recent research results are presented. At last, the author looks ahead the future of information technology and human related disciplines and their effects on education. Keywords: E-learning, M-learning, Educational Semantic Web, Intelligent Education
1. E-learning Model with ICT Technology E-learning means the education technology enhanced by ICT technology. With the development of new ICT technologies, e-learning has been improved continuously, and as a result, new education technologies emerged and named after the corresponding ICT technology, such as network education, mobile education, ubiquitous education and educational semantic web. What will the ICT technology tend to and what will be the next education technology? To deal with this, a new model for e-learning technology is proposed, depicted as figure1. In ELM model, ICT technology enhances education in the following way: information technology is used to design and present course content, and communication Figure1 E-learning Technology Model technology is used as an interaction method among educate, Educatee and educational agent. With the information technology’s evolution from data model to information model and now to knowledge representation, and communication technology’s evolution from wire to wireless and now to mobile communication, the e-learning technology evolutes from CAI, network education, mobile education, ubiquitous education to educational semantic web and so on. In the following, these e-learning technologies are demonstrated.
2. From M-learning to Educational Semantic Web Network Education: with fixed wired network and information model. Mobile Education (also named m-learning) [1-6]: Courseware is designed with data model and information model; communication with mobile network. So, the interaction can be completed anywhere and anytime with text message. On the other hand, the courseware can not be accessed by some devices with different contexts, such as different screen size. So, in m-learning, not all the devices can access the published course content. Ubiquitous Education (also called u-learning): is almost the same as m-learning except Figure2 Portal for PC Screen that in u-learning universal information access (UIA) is required [19]. UIA means that the published courseware can be accessed anywhere, anytime, any network and by any device. UIA has been one unresolved key problem in ubiquitous computing which is also called device independence. The related factors which affect UIA include input device, output device, network type, device screen, OS type, application tool, human preference, dynamic environment, unpredictable action, and etc. To demonstrate the difficulty of UIA, we give an explanation. Figure2 is an education portal designed for PC which looks very well with PC screen. But what about is it when accessed with PDA or Hand Hold? It is terrible. So what kind of resource should course content be for UIA? Let’s look at an example of figure3.
Figure3 Adaptation of SCORM Resource
Figure3 represents a part of courseware resource with SCORM standard. What to present when a device accesses this course at first time? The well solution is that the content of first screen should depend on, at least, the screen size. Figure4 Adaptation with Resource Tree That is, with different screen size, the presented content should match with that size. The above problem is called content adaptation. How to implement content adaptation? This can be demonstrated in figure4 [7]. From figure4, we can see that with resource tree representation, the proper content size can be selected according to the screen size. But the candidate resource sets maybe more than one, which node set should be selected? At the worst case, the selected content may be has none meaning. So, to produce the proper content, other constraints should be
confined, that is, the selected resource node set should be a meaningful block in logical. How to guarantee that? It should refer to educational semantic web. Educational Semantic Web: Educational Semantic Web (ESW) [9-12] is a new education technology enabled by semantic web technology [8]. In ESW, the education content and activity should be annotated with well-defined meaning which is understandable to human and machine. The well-defined meaning is represented with a special data model, called education ontology [14-16]. In education ontology, the concepts and their relations in course content and education activity are defined in a formal method called description logics. With these logical defined educational resources, intelligent education application can be developed by reasoning with logic programming and rules. The educational semantic web diagram is depicted as figure5 and a draft educational ontology is as figure6 (proposed by author’s group).
3. Intelligent Education With the research and development of ICT, new technologies will come out, and where should education technology go? From the above statement, we can see that e-learning technologies are developed for education practice, that is, only for educate and educatee. It is natural to think that ICT technology should not be confined only for education practice. The problem is: how does ICT technology provide support to education research (theory and method)? Is it possible? And a more challenge problem is: are there any other technologies or theory, except for ICT, that can be used to enhance education? Of course, these are human related disciplines, such as brain science, cognition science, knowledge science, logics, philosophy, and etc. The evolution of education technology with human related disciplines and ICT technology is depicted as figure7. In figure7, D-Com stands for data communication, D-Process stands for data process and K-reasoning stands for knowledge reasoning. From figure7, we see that the
development trend of information technology is knowledge representation and reasoning (KRR). With KRR, intelligent applications can be developed. On the other hand, from the viewpoint of application, domain users can also model and develop applications without relying on software engineers. In other words, education domain users include educate, educate, researcher, and other ones who are engaged in education. So, as the development of technology, not only education practice, but also education theory can also be enhanced by ICT in an intelligent way. At the meantime, evolution roadmap of ICT is very like the cognition process for human being to learn knowledge, depicted as figure8. As figure8 indicates, the cognitive process is as follows: collect large amount of data; select useful data (information) from large data; extract knowledge about a concept from synthesizing multiple aspects of information and setting up connection with other concepts; the knowledge is kept in long-term store. At the meantime, data selection and information synthesis are controlled by thinking and reasoning with knowledge originated from motivation. On the other hand, with the information model’s evolution from data model, information model to knowledge model, the course model changes from digital course, multimedia course to knowledge representation course. All of these reflect that the development process of information technology is almost consistent with the cognition process. Figure7 also indicates that, besides ICT, human related disciplines are also an important force to improve education. From viewpoint of history of thousands of years, human discipline plays an important role to education. Especially, in recent years, with the development of human related disciplines, we understand human mind more and more deeply, such as how to understand, how to learn, how to think, and etc. As a result, the human related achievements provide human a better way to understand themselves, to guide human to learn and to educate.
Figure9 Intelligent Education Architecture
In a word, with the rapid development ICT technology and human related disciplines, education will be enhanced in an intelligent way! Figure9 demonstrates more details of this intelligent way which called Intelligent Education Architecture (IEA) [17]. Figure9 indicates that IEA include five parts, they are Human Related Disciplines (HRD), Intelligent ICT (IICT, include Information Science, AI, Knowledge Engineering), Education Theory Research (ETR, includes Education Method), Education Practice and Intelligent Education Domain Engineering. Relations among these five parts are as follows:
(1) Education Theory Research is supported by Human Related Disciplines, Intelligent ICT and Intelligent Education Domain Engineering. (2) Intelligent Education Domain Engineering is developed with the guide of Education Theory Research and Intelligent ICT. (3) Optimized Education Practice can make teaching process more efficient and the Human Related Disciplines can progress more rapidly. (4) All education activities are supported by Intelligent Education Domain Engineering. In the proposed Intelligent Education, all parts of education will form an ecosystem where every part in education will be enhanced by others. More details refer to [17].
4. Some Researches in Knowledge Science and Intelligent Education Laboratory at Peking University Knowledge Science and Intelligent Education Laboratory at Peking University (KSIE) aim to build an education ecosystem with Intelligent Education Architecture [17]. Such a system will provide scientific education research method and scientific education practice（includes teaching and learning）. With this system, the efficiency of education theory and method will be predicted by intelligent simulation or formal verification, not like the nowadays experiment with real person. The goal is good, but implementing such a system depends on a great progress of Human Related Disciplines and Intelligent ICT where the learning principle is understood and the proper model of learning nature is constructed. Indeed, it needs a lot of work and a long time, but we are doing something to proceed to the goal. Some researches on this work in recent years are presented as below.
4.1 Education Software Automation based on Education Ontology This means that, with educational ontology, we can develop an infrastructure platform to generate application system in terms of user requirements. Such an infrastructure is proposed as figure10, more details refer to [17].
4.2 Educational Semantic Web Service Composition Model Semantic Web Service (SWS) technology [13, 22, 23, 24] provides a good choice to realize education software automation. With SWS, every education service is annotated with well-defined meaning which can be understood by
human and machine [18, 21]. With large amount of educational semantic web services available on www, we can compose some of them according to requirements and generate a realistic system. The above process is called SWS composition [18, 21, 25]. Figure11 is a proposed SWS composition model for education by our group which is called OntoComposer [21]. Figure12 is an implementation and its operation interface of OntoComposer, the labeled numbers in figure12 represent the process to compose a new service with available web services, details refers to [26].
4.3 Ontology-based Search Engine Search engine is an efficient tool to retrieve information from large amount of web resources. With semantic web, web resources are annotated with well-defined meaning understandable to human and machine. From figure5, with educational semantic web, educational ontology provides formal semantic description for educational resources which can be understood by machine. In this way, search engine can understand web resource and select information more efficiently (depicted as figure13), more details refer to [20].
4.4 Ontology-based M-learning Platform One problem in m-learning is context aware application. That is, the presentation of courseware content should be adaptive in terms of the user context. The user context includes device feature and user preference. When user accesses education server, the server selects proper content and style, and returns it to client. In this way, the courseware could be accessed by any device. Figure14 is a proposed design for m-learning with web service. The context-aware adaptation works as following: (1) at sever end, the e-learning
system is composed of simple services which include operation and resource individually. (2) When server receives access request, it groups some simple services into one page in terms of user context automatically and returns it to client. The adaptive presentations are depicted as figure15. More details refer to [6].
5. Conclusions The advance of ICT would enhance education continuously. E-learning evolution with ICT indicates that M-learning provides a convenient interaction, educational semantic web provides a machine understandable description of education resource, and ontology model provides a knowledge representation model of course content, which will improve cognitive process for learn content in nature. Besides ICT, human related disciplines have become an important force to enhance education recently. A kind of education technology to combine ICT and human related disciplines into education and form a scientific ecosystem education system will be the next generation technology, which is called Intelligent Education.
References [1] Cui Guangzuo, etc. Mobile Education: A New Direction of Education Technology. Ceta Annual Conference of China. 2001.12. [2] Cui Guangzuo, etc. Mobile Virtual Campus: Design and Implementation. China Distance Education. 2002.12. [3] Cui Guangzuo, etc. Study and Implementation of SMS-based Mobile Education Architecture. GCCCE2002, Beijing. [4] Cui Guangzuo, etc. MVClass: Mobile Virtual Class for Open and Distance Education.International Conference of Distance Education " 2nd AEARU Workshop on Network Education”, Taiwan.2003.12 [5] Dong Sheqin, Yue Weining, Cui Guangzuo And Wang Guoping. A Educational Information Platform based on Mobile Device. Computer Application of Chine. 2004.V24.11, Pp142-145. [6] Cui Guangzuo, etc. A Study of Concept and Key Technologies of Mobile Education. China Distance Education. 2005.9. [7] Cui Guangzuo. WebUnify: An Ontology-based Web Site Organization and Publication Platform for Device Adaptation. SNPD2004 International Conference, Beijing. 2004.7. [8] Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American 2001.5. [9] Lora Aroyo, Darina Dicheva. The New Challenges for E-learning: The Educational Semantic Web. IEEE Journal of Educational Technology & Society,2004.10.Pages 59-69. [10] Emanuela Moreale, Maria Vargas-Vera. Semantic Services in e-Learning: an Argumentation Case Study. IEEE Journal of Educational Technology & Society,2004.10.Pages 112-128. [11] Terry Anderson and Denise Whitelock The Educational Semantic Web: Visioning and Practicing the Future of Education. Journal of Interactive Media in Education, UK, 2004 (1). [12] Kendall Clark, Bijan Parsia and Jim Hendler Will the Semantic Web Change Education? Journal of Interactive Media in Education, UK, 2004 (1). [13] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng. Semantic Web Services. IEEE Intelligent Systems, MARCH/APRIL 2001. Pp46-53. [14] Cui Guangzuo. OntoEdu: Ontology based Education Grid System for e-learning. Fifth International Agricultural Ontology Service (AOS) Workshop, Invited report.2004.5. [15] Cui Guangzuo, etc. OntoEdu: Ontology based Education Grid System for e-learning. GCCCE2004,HongKong
[16] Cui Guangzuo, etc.OntoEdu: A Case Study of Ontology-based Education Grid System for e-Learning . The Official Journal of Global Chinese Society FOR Computers in Education,2004,pp59-72 [17] Cui Guangzuo, etc. Architecture Study of Intelligent Education. Keynote Speaker. GCCCE2006, Beijing. [18] Yang Li-na. A Kind of Task Composition Method Based on Education Ontology. Master Thesis, Peking University, 2004.7. [19] Cui Guangzuo. Application and Challenge of education under Ubiquitous Environment. Ubiquitous Forum of Information Industry Ministry in China. 204.11. [20] Xiao Hong. Study on the Semantic of SCORM based on Knowledge Organization & Retrieval. Master Thesis, Peking University, 2006.7. [21] Liu Yang. A Kind of Ontology based Hybrid Semantic Web Service Composition: Mechinsm and Implementation. Master Thesis, Peking University. 2006.7. [22] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng,Semantic Web Services. IEEE Intelligent Systems.MARCH/APRIL 2001.pp46-54. [23] Sheila A. McIlraith,David L. Martin. Bringing Semantics to Web Services. IEEE Intelligent System,JANUARY/FEBRUARY 2003.pp90-94. [24] Massimo Paolucci.Autonomous Semantic Web Services. IEEE Intelligent Computing. SEPTEMBER • OCTOBER 2003. pp34-42. [25] Brahim Medjahed, Athman Bouguettaya, A Multilevel Composability Model for Semantic Web Services. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 7, JULY 2005.pp954-969. [26] Cui Guangzuo. OntoComposer: Development Model and Implementation of Semantic Web Service based on Domain Ontology. To be published.
Abstract: An e-learning model with ICT technology (ELM) is proposed in this paper. With this model, some education technologies and e-learning evolution are interpreted, such as network education, mobile education, ubiquitous education, educational semantic web, and etc. In the meantime, the way of how to combine new ICT technologies into education is also demonstrated. After discussing the convenience and challenge of various education technologies, a new model called intelligent education is introduced and some recent research results are presented. At last, the author looks ahead the future of information technology and human related disciplines and their effects on education. Keywords: E-learning, M-learning, Educational Semantic Web, Intelligent Education
1. E-learning Model with ICT Technology E-learning means the education technology enhanced by ICT technology. With the development of new ICT technologies, e-learning has been improved continuously, and as a result, new education technologies emerged and named after the corresponding ICT technology, such as network education, mobile education, ubiquitous education and educational semantic web. What will the ICT technology tend to and what will be the next education technology? To deal with this, a new model for e-learning technology is proposed, depicted as figure1. In ELM model, ICT technology enhances education in the following way: information technology is used to design and present course content, and communication Figure1 E-learning Technology Model technology is used as an interaction method among educate, Educatee and educational agent. With the information technology’s evolution from data model to information model and now to knowledge representation, and communication technology’s evolution from wire to wireless and now to mobile communication, the e-learning technology evolutes from CAI, network education, mobile education, ubiquitous education to educational semantic web and so on. In the following, these e-learning technologies are demonstrated.
2. From M-learning to Educational Semantic Web Network Education: with fixed wired network and information model. Mobile Education (also named m-learning) [1-6]: Courseware is designed with data model and information model; communication with mobile network. So, the interaction can be completed anywhere and anytime with text message. On the other hand, the courseware can not be accessed by some devices with different contexts, such as different screen size. So, in m-learning, not all the devices can access the published course content. Ubiquitous Education (also called u-learning): is almost the same as m-learning except Figure2 Portal for PC Screen that in u-learning universal information access (UIA) is required [19]. UIA means that the published courseware can be accessed anywhere, anytime, any network and by any device. UIA has been one unresolved key problem in ubiquitous computing which is also called device independence. The related factors which affect UIA include input device, output device, network type, device screen, OS type, application tool, human preference, dynamic environment, unpredictable action, and etc. To demonstrate the difficulty of UIA, we give an explanation. Figure2 is an education portal designed for PC which looks very well with PC screen. But what about is it when accessed with PDA or Hand Hold? It is terrible. So what kind of resource should course content be for UIA? Let’s look at an example of figure3.
Figure3 Adaptation of SCORM Resource
Figure3 represents a part of courseware resource with SCORM standard. What to present when a device accesses this course at first time? The well solution is that the content of first screen should depend on, at least, the screen size. Figure4 Adaptation with Resource Tree That is, with different screen size, the presented content should match with that size. The above problem is called content adaptation. How to implement content adaptation? This can be demonstrated in figure4 [7]. From figure4, we can see that with resource tree representation, the proper content size can be selected according to the screen size. But the candidate resource sets maybe more than one, which node set should be selected? At the worst case, the selected content may be has none meaning. So, to produce the proper content, other constraints should be
confined, that is, the selected resource node set should be a meaningful block in logical. How to guarantee that? It should refer to educational semantic web. Educational Semantic Web: Educational Semantic Web (ESW) [9-12] is a new education technology enabled by semantic web technology [8]. In ESW, the education content and activity should be annotated with well-defined meaning which is understandable to human and machine. The well-defined meaning is represented with a special data model, called education ontology [14-16]. In education ontology, the concepts and their relations in course content and education activity are defined in a formal method called description logics. With these logical defined educational resources, intelligent education application can be developed by reasoning with logic programming and rules. The educational semantic web diagram is depicted as figure5 and a draft educational ontology is as figure6 (proposed by author’s group).
3. Intelligent Education With the research and development of ICT, new technologies will come out, and where should education technology go? From the above statement, we can see that e-learning technologies are developed for education practice, that is, only for educate and educatee. It is natural to think that ICT technology should not be confined only for education practice. The problem is: how does ICT technology provide support to education research (theory and method)? Is it possible? And a more challenge problem is: are there any other technologies or theory, except for ICT, that can be used to enhance education? Of course, these are human related disciplines, such as brain science, cognition science, knowledge science, logics, philosophy, and etc. The evolution of education technology with human related disciplines and ICT technology is depicted as figure7. In figure7, D-Com stands for data communication, D-Process stands for data process and K-reasoning stands for knowledge reasoning. From figure7, we see that the
development trend of information technology is knowledge representation and reasoning (KRR). With KRR, intelligent applications can be developed. On the other hand, from the viewpoint of application, domain users can also model and develop applications without relying on software engineers. In other words, education domain users include educate, educate, researcher, and other ones who are engaged in education. So, as the development of technology, not only education practice, but also education theory can also be enhanced by ICT in an intelligent way. At the meantime, evolution roadmap of ICT is very like the cognition process for human being to learn knowledge, depicted as figure8. As figure8 indicates, the cognitive process is as follows: collect large amount of data; select useful data (information) from large data; extract knowledge about a concept from synthesizing multiple aspects of information and setting up connection with other concepts; the knowledge is kept in long-term store. At the meantime, data selection and information synthesis are controlled by thinking and reasoning with knowledge originated from motivation. On the other hand, with the information model’s evolution from data model, information model to knowledge model, the course model changes from digital course, multimedia course to knowledge representation course. All of these reflect that the development process of information technology is almost consistent with the cognition process. Figure7 also indicates that, besides ICT, human related disciplines are also an important force to improve education. From viewpoint of history of thousands of years, human discipline plays an important role to education. Especially, in recent years, with the development of human related disciplines, we understand human mind more and more deeply, such as how to understand, how to learn, how to think, and etc. As a result, the human related achievements provide human a better way to understand themselves, to guide human to learn and to educate.
Figure9 Intelligent Education Architecture
In a word, with the rapid development ICT technology and human related disciplines, education will be enhanced in an intelligent way! Figure9 demonstrates more details of this intelligent way which called Intelligent Education Architecture (IEA) [17]. Figure9 indicates that IEA include five parts, they are Human Related Disciplines (HRD), Intelligent ICT (IICT, include Information Science, AI, Knowledge Engineering), Education Theory Research (ETR, includes Education Method), Education Practice and Intelligent Education Domain Engineering. Relations among these five parts are as follows:
(1) Education Theory Research is supported by Human Related Disciplines, Intelligent ICT and Intelligent Education Domain Engineering. (2) Intelligent Education Domain Engineering is developed with the guide of Education Theory Research and Intelligent ICT. (3) Optimized Education Practice can make teaching process more efficient and the Human Related Disciplines can progress more rapidly. (4) All education activities are supported by Intelligent Education Domain Engineering. In the proposed Intelligent Education, all parts of education will form an ecosystem where every part in education will be enhanced by others. More details refer to [17].
4. Some Researches in Knowledge Science and Intelligent Education Laboratory at Peking University Knowledge Science and Intelligent Education Laboratory at Peking University (KSIE) aim to build an education ecosystem with Intelligent Education Architecture [17]. Such a system will provide scientific education research method and scientific education practice（includes teaching and learning）. With this system, the efficiency of education theory and method will be predicted by intelligent simulation or formal verification, not like the nowadays experiment with real person. The goal is good, but implementing such a system depends on a great progress of Human Related Disciplines and Intelligent ICT where the learning principle is understood and the proper model of learning nature is constructed. Indeed, it needs a lot of work and a long time, but we are doing something to proceed to the goal. Some researches on this work in recent years are presented as below.
4.1 Education Software Automation based on Education Ontology This means that, with educational ontology, we can develop an infrastructure platform to generate application system in terms of user requirements. Such an infrastructure is proposed as figure10, more details refer to [17].
4.2 Educational Semantic Web Service Composition Model Semantic Web Service (SWS) technology [13, 22, 23, 24] provides a good choice to realize education software automation. With SWS, every education service is annotated with well-defined meaning which can be understood by
human and machine [18, 21]. With large amount of educational semantic web services available on www, we can compose some of them according to requirements and generate a realistic system. The above process is called SWS composition [18, 21, 25]. Figure11 is a proposed SWS composition model for education by our group which is called OntoComposer [21]. Figure12 is an implementation and its operation interface of OntoComposer, the labeled numbers in figure12 represent the process to compose a new service with available web services, details refers to [26].
4.3 Ontology-based Search Engine Search engine is an efficient tool to retrieve information from large amount of web resources. With semantic web, web resources are annotated with well-defined meaning understandable to human and machine. From figure5, with educational semantic web, educational ontology provides formal semantic description for educational resources which can be understood by machine. In this way, search engine can understand web resource and select information more efficiently (depicted as figure13), more details refer to [20].
4.4 Ontology-based M-learning Platform One problem in m-learning is context aware application. That is, the presentation of courseware content should be adaptive in terms of the user context. The user context includes device feature and user preference. When user accesses education server, the server selects proper content and style, and returns it to client. In this way, the courseware could be accessed by any device. Figure14 is a proposed design for m-learning with web service. The context-aware adaptation works as following: (1) at sever end, the e-learning
system is composed of simple services which include operation and resource individually. (2) When server receives access request, it groups some simple services into one page in terms of user context automatically and returns it to client. The adaptive presentations are depicted as figure15. More details refer to [6].
5. Conclusions The advance of ICT would enhance education continuously. E-learning evolution with ICT indicates that M-learning provides a convenient interaction, educational semantic web provides a machine understandable description of education resource, and ontology model provides a knowledge representation model of course content, which will improve cognitive process for learn content in nature. Besides ICT, human related disciplines have become an important force to enhance education recently. A kind of education technology to combine ICT and human related disciplines into education and form a scientific ecosystem education system will be the next generation technology, which is called Intelligent Education.
References [1] Cui Guangzuo, etc. Mobile Education: A New Direction of Education Technology. Ceta Annual Conference of China. 2001.12. [2] Cui Guangzuo, etc. Mobile Virtual Campus: Design and Implementation. China Distance Education. 2002.12. [3] Cui Guangzuo, etc. Study and Implementation of SMS-based Mobile Education Architecture. GCCCE2002, Beijing. [4] Cui Guangzuo, etc. MVClass: Mobile Virtual Class for Open and Distance Education.International Conference of Distance Education " 2nd AEARU Workshop on Network Education”, Taiwan.2003.12 [5] Dong Sheqin, Yue Weining, Cui Guangzuo And Wang Guoping. A Educational Information Platform based on Mobile Device. Computer Application of Chine. 2004.V24.11, Pp142-145. [6] Cui Guangzuo, etc. A Study of Concept and Key Technologies of Mobile Education. China Distance Education. 2005.9. [7] Cui Guangzuo. WebUnify: An Ontology-based Web Site Organization and Publication Platform for Device Adaptation. SNPD2004 International Conference, Beijing. 2004.7. [8] Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American 2001.5. [9] Lora Aroyo, Darina Dicheva. The New Challenges for E-learning: The Educational Semantic Web. IEEE Journal of Educational Technology & Society,2004.10.Pages 59-69. [10] Emanuela Moreale, Maria Vargas-Vera. Semantic Services in e-Learning: an Argumentation Case Study. IEEE Journal of Educational Technology & Society,2004.10.Pages 112-128. [11] Terry Anderson and Denise Whitelock The Educational Semantic Web: Visioning and Practicing the Future of Education. Journal of Interactive Media in Education, UK, 2004 (1). [12] Kendall Clark, Bijan Parsia and Jim Hendler Will the Semantic Web Change Education? Journal of Interactive Media in Education, UK, 2004 (1). [13] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng. Semantic Web Services. IEEE Intelligent Systems, MARCH/APRIL 2001. Pp46-53. [14] Cui Guangzuo. OntoEdu: Ontology based Education Grid System for e-learning. Fifth International Agricultural Ontology Service (AOS) Workshop, Invited report.2004.5. [15] Cui Guangzuo, etc. OntoEdu: Ontology based Education Grid System for e-learning. GCCCE2004,HongKong
[16] Cui Guangzuo, etc.OntoEdu: A Case Study of Ontology-based Education Grid System for e-Learning . The Official Journal of Global Chinese Society FOR Computers in Education,2004,pp59-72 [17] Cui Guangzuo, etc. Architecture Study of Intelligent Education. Keynote Speaker. GCCCE2006, Beijing. [18] Yang Li-na. A Kind of Task Composition Method Based on Education Ontology. Master Thesis, Peking University, 2004.7. [19] Cui Guangzuo. Application and Challenge of education under Ubiquitous Environment. Ubiquitous Forum of Information Industry Ministry in China. 204.11. [20] Xiao Hong. Study on the Semantic of SCORM based on Knowledge Organization & Retrieval. Master Thesis, Peking University, 2006.7. [21] Liu Yang. A Kind of Ontology based Hybrid Semantic Web Service Composition: Mechinsm and Implementation. Master Thesis, Peking University. 2006.7. [22] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng,Semantic Web Services. IEEE Intelligent Systems.MARCH/APRIL 2001.pp46-54. [23] Sheila A. McIlraith,David L. Martin. Bringing Semantics to Web Services. IEEE Intelligent System,JANUARY/FEBRUARY 2003.pp90-94. [24] Massimo Paolucci.Autonomous Semantic Web Services. IEEE Intelligent Computing. SEPTEMBER • OCTOBER 2003. pp34-42. [25] Brahim Medjahed, Athman Bouguettaya, A Multilevel Composability Model for Semantic Web Services. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 7, JULY 2005.pp954-969. [26] Cui Guangzuo. OntoComposer: Development Model and Implementation of Semantic Web Service based on Domain Ontology. To be published.
