UlakAgent: A MOBILE RESEARCHER AGENT FOR RESOURCE SHARING AMONG LEARNING MANAGEMENT SYSTEMS

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1 UlakAgent: A MOBILE RESEARCHER AGENT FOR RESOURCE SHARING AMONG LEARNING MANAGEMENT SYSTEMS A MASTER S THESIS in Department of Computer Engineering Atılım University by NEZAKET TEZCAN AUGUST 2009

2 UlakAgent: A MOBILE RESEARCHER AGENT FOR RESOURCE SHARING AMONG LEARNING MANAGEMENT SYSTEMS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF ATILIM UNIVERSITY BY NEZAKET TEZCAN IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN THE DEPARTMENT OF COMPUTER ENGINEERING AUGUST 2009

3 Approval of the Graduate School of Natural and Applied Sciences, Atılım University. Prof. Dr. Abdurrahim Özgenoğlu Director I certify that this thesis satisfies all the requirements as a thesis for the degree of Master of Science. Prof.Dr. İbrahim AKMAN Head of Department This is to certify that we have read the thesis UlakAgent: A Mobile Researcher Agent for Resource Sharing among Learning Management Systems submitted by Nezaket Tezcan and that in our opinion it is fully adequate, in scope and quality, as a thesis for the degree of Master of Science. Asst.Prof.Dr. Fatma Cemile Serçe Supervisor Examining Committee Members Prof. Dr. Ali Yazıcı Assoc.Prof.Dr. Ferda Nur Alpaslan Asst.Prof.Dr. Çiğdem Turhan Asst.Prof.Dr. Fatma Cemile Serçe Instructor Aylin Akça Okan Date: 08/19/2009

4 I declare and guarantee that all data, knowledge and information in this document has been obtained, processed and presented in accordance with academic rules and ethical conduct. Based on these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last name: NEZAKET TEZCAN Signature:

5 ABSTRACT UlakAgent: A MOBILE RESEARCHER AGENT FOR RESOURCE SHARING AMONG LEARNING MANAGEMENT SYSTEMS Tezcan, Nezaket M.Sc., Computer Engineering Department Supervisor: Asst. Prof. Dr. Fatma Cemile Serce August 2009, 94 pages With the abundance and variety of knowledge, finding the results appropriate for the individual in accordance with the learning profile- by spending less time and effort, has become one of today s objectives. For the learning management systems (LMS) used in distance education to possess that kind of a function secures a position in this sector. This study proposes a mobile agent for online learning environments, UlakAgent. The agent is designed and developed as one of the agents in MODA, a multi-agent adaptive system developed for learning management systems. UlakAgent is responsible for searching content resources both in local and global repositories. The agent moves to other learning management systems integrated with MODA, works there, collects content resources, goes to other available platforms and then gets back to home learning management system with results. It communicates with the other agents of MODA to deliver adaptive content to the learner. The study gives the architecture of UlakAgent and includes a sample utilization of the agent. A MODA community was constructed with three learning management systems, and the mobile agent had its search travel in this community. iii

6 Information on technologies used in MODA community and about the UlakAgent has been given in this study. Keywords: Mobile Agents, Distance Learning, Learning Management Systems, Multiagent Systems, Adaptive Learning Management System. iv

7 ÖZ UlakAgent: ÖĞRENİM YÖNETİM SİSTEMLERİNDE KAYNAK PAYLAŞIMI İÇİN HAREKETLİ ARAŞTIRMA ETMENİ Tezcan, Nezaket Yüksek Lisans, Bilgisayar Mühendisliği Bölümü Tez Yöneticisi: Yrd. Doç. Dr. Fatma Cemile Serçe Ağustos 2009, 94 sayfa Bilginin çokluğu ve çeşitliliği, daha az zaman ve çaba harcayarak; hem amaçlanan, hem de kişiye uygun olan - öğrenme profiline göre - sonuçlara ulaşmayı günümüz hedefleri arasına sokmaktadır. Uzaktan eğitimde kullanılan öğrenim yönetim sistemleri için bu tür bir işleve sahip olmak, sektörde bir adım önde olmalarını sağlamaktadır. Bu çalışma, çevrimiçi öğrenme sistemleri için UlakAgent isminde hareketli bir etmen önermektedir. UlakAgent etmeni, öğrenim yönetim sistemleri için geliştirilen uyarlanabilir çoklu etmen sistemi olan MODA nın etmenlerinden biri olarak tasarlanmış ve geliştirilmiştir. Hem yerel, hem de evrensel veri havuzlarındaki kaynak içeriklerini aramak için görevlendirilmiştir. Etmen MODA ile bütünleştirilmiş diğer öğrenim yönetim sistemlerine taşınır; orada gerekli çalışmayı yaparak kaynak içeriklerini toplar; sonra da diğer uygun platformlara giderek orada da bu işlemleri sırasıyla gerçekleştirir. Sistemdeki tüm platformları dolaşan etmen, elde ettiği arama sonuçlarıyla birlikte ana sisteme geri döner. MODA nın diğer etmenleriyle iletişime geçerek uyarlanmış içerik listesini kullanıcıya taşır. Bu çalışma ile, UlakAgent ın mimarisi ile örnek kullanımı anlatılmaktadır. Bu örnek çalışmada üç tane öğrenim yönetim sistemi kullanılarak bir v

8 MODA birliği oluşturulmuş ve hareketli etmenin araştırma seyahatini bu birlik üzerinden yapması sağlanmıştır. Bu çalışmada MODA birliği için kullanılan teknolojiler ile MODA birliği ve UlakAgent hakkında bilgiler verilmektedir. Anahtar Kelimeler: Hareketli Etmenler, Uzaktan Öğrenim, Öğrenim Yönetim Sistemleri, Çoklu Etmen Sistemi, Uyarlanabilir Öğrenim Yönetim Sistemleri vi

9 To My Parents vii

10 ACKNOWLEDGMENTS I express sincere appreciation to my supervisor Asst.Prof.Dr. Fatma Cemile Serçe for her guidance and insight throughout the research I would like to thank, my parents for their endless support, guidance and kind words. To my friend Sonnur Özdemir, I offer sincere thanks for her continuous support, kind words and patience during this period. To my friend Dursun Turan Üstündağ, To my instructors and my friends. Thank you all! viii

11 TABLE OF CONTENTS ABSTRACT... iii ÖZ... v ACKNOWLEDGMENTS... viii TABLE OF CONTENTS... ix LIST OF TABLES... xiii LIST OF FIGURES... xiv LIST OF ABBREVIATIONS... xvi 1 INTRODUCTION Background of the Study Purpose of the Study Significance of the Study Approach of the Study Road Map DISTANCE EDUCATION Introduction History of distance education Distance Education Theories ix

12 2.4 From distance learning to e-learning E-learning LMS AGENT TECHNOLOGY Agent Multi-agent Systems Mobile Agents Characteristics of Mobile Agents Mobile Agent Toolkits Standardization Major Technical Advantages of Mobile Agents Application Domains of Mobile Agents Type of Mobility Agent Usage in Distance Education Mobile Agent Usage in Distance Education PIAVEE Campus TILE JADE Framework IPMS x

13 3.6.2 WSIG UlakAgent MODA The Conceptual Framework of MODA Learner Profile Course Content Profile Learner-Course Content Matching Mechanism The Multi-Agent System Architecture MODA Agents LMS-MODA Communication Protocol UlakAgent Changes in MODA System for Exposing MODA Community The Acquaintance of MODA Agents in MODA Community Search Process with UlakAgent in MODA Community Research Action Scenario between LMS and MODA in MODA Community Data Files in MODA Community CommunityAgent MODA Community with UlakAgent Communication in MODA Community with UlakAgents xi

14 4.6.1 Communication between LMS Environment in Local and MODA in Local Communication between MODA in Local and MODA in Remote Communication between MODA in Local and LMS Environment in Local Communication between LMS Environment in Local and MODA in Remote Web Service Web Services in MODA community UTILIZATION OF ULAKAGENT DISCUSSIONS AND CONCLUSIONS REFERENCES APPENDICES APPENDIX A APPENDIX B xii

15 LIST OF TABLES TABLES Table 1: Summary of agent properties Table 2: Overview of some existing mobile agent toolkits Table 3: Thirty Content Types used in the framework Table 4: Example MODA Community xiii

16 LIST OF FIGURES FIGURES Figure 1: An agent interacting with the environment Figure 2: Multi-Agent System Characterisation Figure 3: Intelligent Agent Scope Figure 4: Client-server paradigm vs. Mobile Agent approach Figure 5: Basic Structure of Mobile Agent Figure 6: Overall View of PIAVEE Figure 7: The two layered network of Campus Figure 8: Implementation of mobile agents technology in adaptation mechanism Figure 9: Equivalent algorithms using strong and weak mobility Figure 10: Instance push transfer protocol diagram Figure 11: On-demand transfer protocol diagram Figure 12: WSIG Architecture Figure 13: The Architecture of MODA Figure 14: The Acquaintance Model of MODA Agents Figure 15: Request Packet Structure Figure 16: Response Packet Structure Figure 17: Request and Response Data in Communication Scenarios between LMS and MODA Figure 18: The Data File Structure for Course Content Key List Figure 19: The Data File Structure for Course Content List xiv

17 Figure 20: The Acquaintance Model of MODA Agents in Community Figure 21: Request and Response Data in Communication Scenarios between LMS and MODA in MODA Community Figure 22: The Data File Structure for Course Content Information List in MODA Community Figure 23: MODA System Figure 24: Architecture of MODA Community Figure 25: Use of SOAP and WSDL in a Web Service architecture Figure 26: Moodle LMS integrated with MODA Figure 27: Some Search Results in Moodle LMS for Search Key Java Figure 28: Search output in Moodle MODA System during the search process in community (Local System) Figure 29: Search output in Moodle MODA System during the search process in community (Remote System-1) Figure 30: Search output in Moodle MODA System during the search process in community (Remote System-2) xv

18 LIST OF ABBREVIATIONS AIM ACL API CMS DAI DF FIPA HTTP IMAGE IPMAS IPMS ITS JADE LMS MAF MAS MASIF Articulated Instructional Media Agent communication languages Application ProgrammingIinterface Course Management System Distributed Artificial Intelligence Directory Facilitator Foundation for Intelligent Physical Agents Hypertext Transfer Protocol Intelligent Mobility Agent for Complex Geographical Environments Multi-Agent System Inter-Platform Mobility Service Intelligent Tutoring Systems Java Agent Development Framework Learning Managament System Mobile Agent Facility Multi-Agent System Mobile Agent System Interoperability Facilities xvi

19 ODTP OMG PIAVEE PCTP SOAP TILE UDDI W3C WSDL WSIG ÖYS VLE On-Demand Transfer Protocol Object Management Group Platform Independent Agent-based Virtual Educational Environment Push Cache Transfer Protocol Simple Object Access Protocol The Technology Integrated Learning Environments Universal Description, Discovery and Integration World Wide Web Consortium Web Service Description Language Web Service Integration Gateway Öğrenim Yönetim Sistemi Virtual Learning Environment xvii

20 CHAPTER 1 1 INTRODUCTION 1.1 Background of the Study Distance education is an instructional model in which the instuctor is not in the same place at the same time as the student (Casey, 2008). Distance education as a concept emerged in the 1840s with the letters sent through the postal service. following advances seen in the Internet technologies, nowadays the distance education is given through Learning Management Systems (LMS), software designed to deliver on-line education, flourished with various audio and visual materials via Internet connections. Distance education is becoming more important in today s global world. Universities and schools support traditional classroom-based learning with LMS, the main element of the Internet-based education. Majority of the LMSs are presenting the same tools and materials for all of the learners and they are not aware of the other LMSs and their available resources. With the technological advances, creating intelligent and adaptive systems that will provide the learners to be presented with appropriate content to his/her own profile is now possible. Agent technologies have become a key factor in creating adaptive and intelligent learning systems. Agents are, also, used to support web-based education, by assisting, tutoring, and monitoring learners through their learning process, and also to achieve learner s learning goals by delivering knowledge in an adaptive or individualized way. These types of agents are called as pedagogical agents, and support human learning in interactive learning environments. Cosmo (Lester, Voerman, Towns & Callaway 1999), Adele (Johnson, Shaw and Ganeshan, 1998), PPP Persona (André, Rist & Müller 1997), Herman-the-Bug (Lester, Stone & Stelling 1999) are given as examples for the usage of the pedagogical agents in 1

21 learning, Adele (Johnson, Shaw and Ganeshan, 1998), PPP Persona (André, Rist & Müller 1997) are also examples for the use of agents in providing adaptiveness. Mobile Agent has claimed a continuously increasing interest among the researches in agent technology as a result of the advantages it brings. Such as reducing the bandwidth usage, total completion time, and latency, continuing process when disconnected, load balancing, dynamically deploying components. For mobile agents to be deployed on the distributed systems, interoperability between different types of middleware needs to be ensured. Mobile agent technology can be used to overcome the some common deficiencies, such as slow access, no adaptivity to individual student, limitation by bandwidth, ans so on. There are the some studies in distance learning where mobile agent technology is used, such as PIVEE (Chhetri, Krishnaswamy, Markham, Hurst, and Casey, 2004), Campus (Westhoff and Unger, 1998), and TILE (Hong, Kinshuk, He, Patel, and Jesshope, 2001). MODA (Serce, 2007) is a multi-agent adaptive system developed for learning management systems. The starting point of MODA is to form an independent adaptive learning management system. For this reason, a TCP-based protocol used for the data communication between LMS and MODA is improved. 1.2 Purpose of the Study Globalization results in sharing knowledge and resources in global context. Internet with the huge amount of published information has become a massive library used all around the world. Finding a piece of information hosted on the Internet is an easy task with the help of the search engines. There are many learning management systems and there are a lot of content resources stored in these systems. In addition, LMSs are unaware of each other in reality. MODA (Serce, 2007) has been developed as a multi-agent system to construct an online community for sharing resources and provide adaptive content. In the last version of MODA, there is a local researcher agent who is responsible for finding resources in the hosting learning management system. In this thesis, this researcher agent of MODA has been redesigned and developed so that it can search and find resources in the community of learning management systems. The 2

22 researcher agent, designed as a mobile agent, searchs for learning objects to extend course materials as well as assist the online learning process. It manages to keep the independent structure among each and every system composed of any LMS and MODA by using the web-service technology. 1.3 Significance of the Study Distance education method is applied by numerous educational institutions and some others are also planning to use it as well. Universities, educational institutions are either developing their own LMS or making use of open source LMSs. Studies (Brusilovsky, and Peylo, 2003) showed that adaptive systems are advantageous in receiving feedback. For this reason adaptability is favored in LMS. Along with the use of the Internet, the information presented in it is also increasing. As a result of growing number of resources providing various information, the Internet is becoming a gigantic library. With the abundance and variety of knowledge, finding the results appropriate for the individual in accordance with the learning profile- by spending less time and effort, has become one of today s objectives. To meet these needs, IT sector is continuing to develop new Technologies constantly. Multiplicity of similar information is a known fact. Because of the systems possesing this knowledge and the variety of the applications, creating new and platform-free technologies is becoming a necessity. LMS is one of them. In both open and closed systems, the LMSs besides from having a wide scope of features, is used to provide the need for learning, yet usually lacks the interaction with other systems. Nowadays, the distance education is mostly realized by using the web-based learning environments. These systems are strongly driven by information revolution and they are either used on the Internet or intranet. However, they have a number of common deficiencies, such as slow access (late response from the servers), inadaptibility to individual students, bandwidth limitations, and many more. From this critical point, UlakAgent and MODA community system have been developed. The benefits of 3

23 mobile agent technology and MODA adaptive learning system can be a solution to these common deficiencies. UlakAgent is a mobile agent and using mobile agent technologies allows the system to achieve high-performance, scalability, and disconnected operation through reduction in network bandwidth and delay, load balancing, and code mobility. Therefore, it is supposed that MODA Community with UlakAgent will improve the learning process. Adaptive methods can be presented to the users of various LMSs, and MODA because of the help of the created community. Preparing an adaptive content is highly costly (Reeve, and Sherman, 1993). Through the community those adaptive contents can be shared. The total cost effect can also be decreased. Since the options given to the students are getting denser in the info-pool, the chances of reaching the most effective resource are getting higher. Since there will be a community consisting of different adaptive methods, securing an adaptability feature to the whole system along with the tractability will be possible. After inheriting the advantages of the mobile agent to the community, during the information transfer a highly effective structure will be composed. 1.4 Approach of the Study Firstly, the chosen learning management systems forming the MODA community were integrated with MODA adaptive learning system (Serce, 2007). These are Moodle and Docebo, the open source LMSs developed with PHP language. MODA is a multi-agent system and has several intelligent agents. JADE (Bellifemine, Caire, and Greenwood, 2006) framework was used to implement these intelligent agents. In this study, the IPMS (Bellifemine et al, 2007) was implemented, a JADE add-on providing platform-to-platform mobility for JADE agents, into MODA System. Thus, the infrastructure required for migration of UlakAgent was prepared in MODA System. UlakAgent can visit each LMS in the community and make a search with keyword on related LMS. For searching the resources and getting the chosen content in any LMS, web-services for each LMS were developed. WSIG (JADE Board, 2008), also a JADE add-on, provides the bidirectional interconnectivity between agents and Web services. For the aim of getting any course 4

24 resources from any LMS, WSIG and its agent, WSIGAgent, were integrated into MODA System. Both WSIGAgent and UlakAgent were developed to call for related web-service. Therefore, each LMS or each MODA integrated with it in MODA Community can be independent from each other system. Both web-service and mobile agent technology ensure the interoperability between this distributed environment. 1.5 Road Map Chapter 2 contains the literature reviews about distance learning, and agent technologies with pedagogical and mobile agents. The chapter also presents the detailed description of the previous studies on both pedagogical and mobile agents related to learning systems. Chapter 3 explains the implementation details of the adaptive learning system, MODA, and its technical architecture. Agents in MODA, the interactions among the agents, and agent communications are explained in this chapter. Chapter 4 contains the implementation details of the MODA community and UlakAgent. The chapter explains the changes in MODA system for forming the MODA Community, and technical details about the usage of some technologies used in constituting the community. Chapter 5 provides screenshots of integration of the system into the open source learning management systems. Chapter 6 provides the discussion for the conclusion and possibilities for further research. 5

25 CHAPTER 2 2 DISTANCE EDUCATION 2.1 Introduction Education is a term which has come into being since human existence. One of the methods used in order to transfer pre-obtained information and the experience gained out of it is distance education. Distance learning is often referred to as distance education. On the contrary to the classic education, in distance education the students do not have to be physically present at a particular geographical location during their education. Briefly, distance education is an instructional model in which the teacher is not in the same place at the same time as the student (Casey, 2008). In the traditional education model, the information and the course content are conveyed through face to face interaction. Meanwhile, from its first example seen in the 1800s to today, various methods have been tried out to initiate interaction and the knowledge transfer in distance education. The first presentation method of the course materials were letters and the postal service was used (Matthews, 1999). With the help of the technological advances, the material presentation has changed rapidly in time. For now, the farthest point reached is the World Wide Web (www) and the use of the Internet network. Distance learning has been characterized by presentation of course material via print, audiotape, videotape, and/or computer. Technological developments have expanded the options available for delivery of distance learning courses, such as the use of CD- ROM/DVD-ROM multimedia packages and World Wide Web (Rogers, 1995). 6

26 Distance education uses three current and popular forms [of media]; a) broadcast television, b) two-way videoconferencing, and c) asynchronous learning networks (Birnbaum, 2001). Asynchronous distance education provides for multi-modal, Web-based delivery of instruction that can be reviewed by the student at any time (Birnbaum, 2001). This type of distance instruction allows students to access the materials, lectures, instruction, etc. from any place and at any time, as opposed to synchronous distance education. In the distance, the interaction between the instructor and the student does not take place face-to-face or in real time; the teacher and the student may never meet or speak to each other. All communication can occur via or regular mail. As the technology changed the definition of distance education has also transformed. Each and every technological advance has brought innovations and this triggered changes in tools used within the distance education. The first microprocessor was developed by Intel in In time, using interactive computer-based technology in distance education has become a need. However, the basic premises of distance education remain the same. Within the 10 years since the World Wide Web was developed for users to connect to the Internet, the possibilities for distance education seem practically limitless, and with these new possibilities, come new emerging definitions of distance education (Chaney, 2005). Dohmen (Dohmen, 1967), Peters (Peters, 1973), Moore (Moore, 1973), and Holmberg (Holmberg, 1977) defined the first descriptions in literature. Keegan (1988) analyzed them and identified six common elements of these definitions; 1) Separation of teacher and learner; 2) Influence of an educational organization; 3) Use of technical media; 4) Provision of two-way communication; 7

27 5) Possibility of occasional meetings; and 6) Participation in an industrialized form of education (Keegan, 1988). After this study, the new descriptions provided the new view of distance education were made in the education literature. One of them was the definitions of Garrison and Shale: Distance education implies that the majority of educational communication between (among) teacher and student(s) occurs noncontiguously. It must involve two-way communication between (among) teacher and student(s) for the purpose of facilitating and supporting the educational process. It uses technology to mediate the necessary two-way communication. Terms of two-way communications, the separation of learner and the learning group, and social issues such as industrialization, privatization, and eye-to-eye contact are highlighted in the above definitions. Keegan (1996) analyzed these definitions and emphasized terms to develop a definition of distance education. Keegan incorporated this form of education into five characteristics. The quasi-permanent separation of teacher and learner throughout the length of the learning process (this distinguishes it from conventional face-to-face education); The influence of an educational organization both in the planning and preparation of learning materials and in the provision of student support services (this distinguishes it from private study and teach-yourself programs); The use of technical media print, audio, video, or computer to unite teacher and learner and carry the content of the course; The provision of two-way communication so that the student may benefit from or even initiate dialogue (this distinguishes it from other uses of technology in education); and 8

28 The quasi-permanent absence of the learning group throughout the length of the learning process so that people are usually taught as individuals rather than in groups, with the possibility of occasional meetings, either face-to-face or by electronic means, for both didactic and socialization purposes (Keegan, 1996). 2.2 History of distance education In the very first distance education model, the presentation of the course materials to the students started with letters and the postal service was used for the distribution process (Matthews, 1999). The course was the Pitman Shorthand training program that taught shorthand via correspondence study in England in the 1840 s (Verduin & Clark, 1991). By 1882, William Rainey Harper, Yale Professor, improved a correspondence program at Chautauqua College of Liberal Arts, NY. The State of New York authorized academic degrees of students completing the required correspondence courses from To further the development of this movement, a Correspondence University was established in Ithaca, NY, in The student who has prepared a certain number of lessons in the correspondence school knew more of the subject treated in those lessons, and knew it better, than the student who has covered the same ground in the classroom (Watkins, 1991). The pioneers mentioned above were usually for providing instruction for women (Casey, 2008). Another distance learning pioneer was provided by the Colliery School of Mines, in Wilkes-Barre, PA, in The instructional delivery system was used to teach mine safety (Moore & Kearsley, 1996). The academic recognition of distance learning achieved in 1892 when the University of Chicago created the first college-level distance learning program. Students far from campus would use the United States Postal Service to exchange assignments and lessons (Hansen, 2001). The Radio broadcasting was invented in the 1920s (Bridgeman, 2001) so that the dependence on mail delivery decreased for distance learning program. Reducing the instructional delivery time and increasing the classroom performance by allowing 9

29 distant students to hear their instructor supported to expand the popularity of using the radio broadcasting on giving the education. By 1921, the first educational radio licenses were granted to the University of Salt Lake City, the University of Wisconsin, and the University of Minnesota (Casey, 2008). The use of television as an instructional medium began in 1934 when the University of Iowa broadcast courses by television (Casey, 2008). In 1964, the University of Wisconsin created the Articulated Instructional Media (AIM) Project. This project was the first attempt to identify, categorize, and systemize distance learning practices and offered direction on how to create and incorporate multimedia instructional packages for the benefit of the independent learner (Gooch, 1998). After the AIM Project, Open University was opened in England in After the creation of the microprocessor in 1971 by the Intel Corporation (Intel Museum, 2006), the golden age of distance education started. The satellite television systems that had been created in the 1960s became costeffective in the 1980s and reduced the cost of employee training by providing on location instruction (Casey, 2003). Satellite telecommunications used to transmit broadcasting of lectures and instruction to off-campus locations became a popular way to conduct distance education. The World Wide Web, developed by Tim Berners- Lee, provided a potential linkage for all of the computers in the world and in 1991 the information superhighway was born (Berners-Lee, 2009). The web also increased possibilities for distance learning experiences. With the introduction of high-speed broadband transmission, distance learning over the Internet became the next instructional frontier. The potential for interactive, virtual classrooms was limited only by the budget, institutional vision, and course management software (Casey, 2003). In our country the first distance education put to practice was mostly started with teaching through letters. The initial example that stands out among the others was in foreign language education. Publishers such as Fono, Naci from Limosal marketed these methods in teaching English since the beginning of 70s (Şansal, 2002). 10

30 In Turkey, the distance education program in Open University faculty has completed its 24 th year in practice. The program started airing on television in 1998; and in 1994 the first e-learning (with media tools such as cassettes and CDs) was carried out. In 2003 both e-books and e-television practices were conducted (AOF, 2009). 2.3 Distance Education Theories Distance education theories were developed from leading scholars in the discipline by Holmberg (Holmberg, 1995), Wedemeyer (Wedemeyer, 1974), Moore (Moore, 1973) and Peters (Peters, 1971). Although forms of distance education were in existence since the 1840's, the need for theory base for distance education was still largely unfulfilled in the 1970's. Moreover, the traditional distance education theories continued to be revised due to rapidly changing technologies (Holmberg, 1995). Keegan (Keegan, 1996) classified theory of distance education into three groups; 1. Theory of independence and autonomy, 2. Theory of industrialization of teaching, and 3. Theory of interaction and communication. Theory of independence and autonomy was argued by Borje Holmberg (1960), Wedemeyer (1974), Rudolf Delling (1966) and Moore (1973) from the 1960's and 1970's. The main dimensions in theory are transactional distance and learner autonomy. Theories of autonomy and independence reflect the essential component of the independence of the learner. Theory placed the learner in the middle of the educational process (Keegan, 1996; Saba, 2003). According to Moore, the distance teaching programmes by their nature require more autonomous behavior by the learner. To succeed in such programmes, the learner must be able to act independently and autonomously (Moore, 1991). Theory was related to the industrialized form of teaching and learning. Otto Peters (1971), Desmond Keegan, Randy Garrison, and John Anderson are theorists of this form that are mainly interested in how the field functions and how it is organized. Structural concerns and issues (e.g. industrialization) are the main focus of this group 11

31 of theories, along with how those issues influence the teaching and learning process, course standardizations, administration process (Keegan, 1996; Saba, 2003). The third approach integrates theories of interaction and communication formulated by Bääth (1982), Sewart (1987), and Daniel & Marquis (1979). Until the 1990s, the theories of interaction and communication mainly treated communication between the tutor/helping organisation and the individual student, while recently theories involving collaborative learning, group interaction and social constructivism emphasising learning as a process and result of a collective experience of the learning group have received much attention. 2.4 From distance learning to e-learning In parallel with the major change in electronic telecommunication in 1980s, the popularity of the distance education aroused world wide. The new communication technologies have made it feasible to dethrone the traditional classes among teaching environments. In addition to this, the use of the Internet and the virtual world has pushed its popularity to a higher level. With the use of the Internet and World Wide Web in distance education, the definition of the traditional distance education has widened and concepts like online learning, e-learning, web based learning have emerged. E-learning is technologybased distance education. E-learning is an innovative approach to distance learning. E-learning creates new learning environment, technologies and opportunities that allow flexible learning, increased potential for interaction and access to a wide clientele. 2.5 E-learning In principle, e-learning is a kind of distance learning. Learning materials can be accessed from the web or intranet via a computer and tutors. Learners can communicate with each other using , chat or discussion forums that are services given by the learning management system (LMS). LMS is the software designed to deliver on-line education. There are many advantages to setting up a learning management system in both academia and industry. 12

32 E-learning has been applied to every field and all forms of education, challenged educators especially distance educators worldwide especially in the field of library and information science. It is a tractable type of distance learning which has a fully integrated instructional medium. It enables individuals to learn by themselves and encourages self-directed learning, self reflection, learner-centered learning and just in time learning. This form of distance learning is gaining in popularity and many thousands of students register for online courses every year. There is also a growing area of mobile learning, or mlearning, that uses mobile devices (such as mobile telephones and PDAs) as the main platform for the provision of the learning experience, either via a web browser or other, often bespoke, software (Liu, T. C., H. Y. Wang, J. K. Liang, T. W. Chan, H. W. Ko and J. C. Yang, 2003). 2.6 LMS The distance education programs, which have a rising popularity due to the Internet usage, accommodate new applications developed in accordance with the technological advances. One of them is LMS. LMS are specialized Learning Technology Systems (IEEE LTSC, 2001), based on the state-of-the-art Internet and web technologies in order to provide education and training following the open and distance learning paradigm. A LMS is software system for planning, delivering, and managing all learning events within an organization, including online, virtual classroom, and instructor-led courses. The focus of LMSs is to manage learners, keeping track of their progress and performance across all types of training activities (Greenberg, 2002). A LMS is a web-based system and works over the Internet. LMSs support knowledge transfer processes by providing an integrated set of tools for the management of distinct teaching and learning activities, such as course development, enrollment, progress tracking, discussion fori, collaboration tools, assessment and grading, etc. 13

33 The ever-growing technology enables numerous activities and cases which are already available in traditional education models to be actualized with LMSs. With the chat application programs question and answer activities can be implemented. With online exams an examination system has been created which is very similar to a classic one. Moreover, the discussion forums have made it possible to set up an effective communication environment between students themselves and the instructor or all together at once. Contrary to the traditional education, tracking student activities can be done with ease. All around the world and also in our country lots of universities carry out their distance education programs via LMSs. Along with the LMSs programmed solely for commercial purposes, open source programs are also available. Both of them have huge user groups. Olat (University of Zurich, 2000), Moddle (Moodle, 2009), and Docebo (Docebo, 2009) can be given as examples for the open source LMSs. 14

34 CHAPTER 3 3 AGENT TECHNOLOGY 3.1 Agent The agents defined in literature by Hewitt (Hewit, 1977) have been used in various applications and the areas of usage are constantly expanding due to new studies. Some of the basic areas the agents are used are production control, air traffic control, traffic and transportation management, information filtering and gathering, electronic commerce, business process management, entertainment and healthcare services and so on. Multi-agent systems are used in education as well, in pararleel with the progress made in distance education programs. There are many different or similar definitions about what an agent is. There is not any agreed-upon definition of agents although first definition was made by Hewitt in 1977 during the early studies in distributed artificial intelligence (DAI). He described an object as an actor and he called it as a computational agent. These actors would communicate with other actors by passing electronic messages and would carry out their actions concurrently (Hewitt, 1977). According to Russel and Norvig, an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors (Russel and Norvig, 1995). On the other hand, Maes described the autonomous agents as computational systems that inhabit some complex dynamic environment, sense and act autonomously in this 15

35 environment and by doing so realize a set of goals or tasks for which they are designed (Maes, 1995). A more detailed description is put forward by Wooldridge and Jennings: a hardware or (more usually) software-based computer system that enjoys the following properties: Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state; Social ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language; Reactivity: agents perceive their environment, (which may be the physical world, a use via a graphical user interface, a collection of other agents, the Internet, or perhaps all of these combined), and respond in a timely fashion to changes that occur in it; Pro-activeness: agents do not simply act in response to their environments, they are able to exhibit goal-directed behavour by taking the initiative (Wooldridge and Jennings, 1995). A consensus can be observed in two of the joint capabilities of the agents: Capable of autonomous action: Agents decide for themselves what they need to do in order to satisfy their design objectives. Capable of interacting with other agents: not simply by exchanging data, but by engaging in analogues of the kind of social activity that we all engage in every day of our lives: cooperation, coordination, negotiation, and the like (Wooldridge, 2002). In Graesser and Franklin, the various definitions were discussed and the table below has been prepared by considering the definition referring to those features of the agents. 16

36 Table 1: Summary of agent properties (Graesser and Franklin, 1996) Property Other Names Meaning reactive (sensing and acting) responds in a timely fashion to changes in the environment autonomous goal-oriented temporally continuous pro-active purposeful exercises control over its own actions does not simply act in response to the environment is a continuously running process communicative socially able communicates with other agents, perhaps including people learning mobile flexible character adaptive changes its behavior based on its previous experience able to transport itself from one machine to another actions are not scripted believable "personality" and emotional state. According to this work, every agent satisfies the first four properties. Some useful classes of agents such as mobile, learning agents have further features. Thus a hierarchical classification based on set inclusion occurs naturally (Graesser and Franklin, 1996). Wooldridge and Jennings also underline similar things and add that additional features like mobility, veracity, benevolence and rationality can be possessed in terms of the agents application scope. These features do not exist in each and every agent type; yet they can be acquired as application and task-based characteristics. These attributes depending on the agent application area are defined as follows (Wooldridge and Jennings, 1995) Mobility: is the ability of an agent to move around electronic networks; Veracity: is the assumption that an agent will not knowingly communicate false information; 17

37 Benevolence: is the assumption that different agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it; Rationality: is the assumption that an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals from being achieved. Besides, Wooldridge interpreted the agents relationship with the environment as in Figure 2.1 which shows an abstract top-level view of an agent. After the agent takes sensory input from the environment, it produces actions that affect this environment as output. The interaction is usually an on-going, non-terminating one (Wooldridge, 1999). Figure 1: An agent interacting with the environment (Wooldridge, 1999). In 1995 Wooldridge and Jennings classified the agents as weak and strong agent description. Weak agency is characterised by autonomy, social ability, reactiveness, and proactiveness. Clearly all of these features are basic agent characteristics which have received world wide acceptance (Wooldridge and Jennings, 1995). Besides covering the given weak agency notion, strong agent term can attribute to mentalistic and emotional notions. Knowledge, belief, intentions, desires and 18

38 emotions set examples for this type of notions. These are potentially more contentious (Wooldridge and Jennings, 1995). 3.2 Multi-agent Systems Although agents are an old concept, multi-agent systems (MAS), a society of agents, is a newer area of research. MAS emerged from distributed systems is also a technique in the artificial intelligence area. MAS is a loosely coupled network of software agents that typically interact with others to satisfy their goals. Software agents in MAS are working together to achieve a more complex task. Those problems cannot reach a solution with a single agent s sole abilities and knowledge. MAS are designed to act on behalf of humans (Durfee, Lesser, and Corkill, 1989). In MAS as shown in Figure 2, agents are independent in that they have independent access to the related environment so that each agent should incorporate a learning algorithm to learn and/or explore the environment. Then, the agents should have some sort of communication between them to behave as a group. In other words, they must organize themselves to act together. Figure 2: Multi-Agent System Characterisation (Medina, Cabanillas, Padget, and Turci, 2004) 19

39 In (Jennings, 1998), MAS is referred to all types of systems composed of multiple (semi-)autonomous components and a multiagent system must have the certain characteristics (Jennings, 1998): Each agent has incomplete capabilities to solve a problem; There is no global system control; Data is decentralized; and Computation is asynchronous. Some ability of MAS increasing the interest in the related research to MAS is given as: The ability to provide robustness and efficiency, To allow inter-operation of existing legacy systems, To solve problems in which data, expertise, or control is distributed (Jennings, Sycara, and Wooldridge, 1998). MAS applications cover a variety of domains, including Aircraft maintenance Electronic book buying coalitions Military demining Wireless collaboration and communications Military logistics planning Supply-chain management Joint mission planning Financial portolio management 20

40 3.3 Mobile Agents Since its inception by James E. White in 1996 (White, 1996), the interest in mobile agents among the research community has been continuously growing. This is mostly related to many interesting research questions that can be raised. For instance, the use of agents in the area of security was initiated by a simple idea of a moving code (Braun and Rossak, 2005). Mobile agents (White, 1996) are a paradigm that derives from two different disciplines (Brown and Rossak, 2005). The first discipline is artificial intelligence, which mentioned the concept of an intelligent agent (Russell and Norvig, 1995), and the second is distributed systems, which defines the concept of code mobility (Picco, 2000). A mobile agent is a mobile code that can migrate from a starting host to many other remote hosts in a network of heterogeneous computer systems and fulfill a task specified by its owner. It works autonomously and communicates with other agents and host systems. Thus, when mobile agents need to move, they initiate the migration process and mobile agents usually migrate to all hosts in related network for completing own tasks. During the self-initiated migration, the agent carries all its code and data, and in some systems it also carries some kind of execution state (Braun and Rossak, 2005). It is assumed that mobile agents reduce network bandwidth usage. Furthermore, they achieve more flexible and efficient information discovery under certain circumstances. In addition, they enhance distributed applications by enabling user to access information ubiquitously anywhere and anytime. According to IBM Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires. Intelligent agents can then be described in terms of a space defined by the three dimensions of agency, intelligence, and mobility (Figure 3) (Goodall, 2000). 21

41 Agency Agent Interactivity Service Interactivity Application Interactivity Data Interactivity User Representation Asynchrony Mobility Static Mobile scripts Mobile objects Threshold of Intelligent Agency Preference Learning Planning Reasoning Intelligence Figure 3: Intelligent Agent Scope (Goodall, 2000) Characteristics of Mobile Agents Brown and Rossak identify four characteristics of mobile agents: 1. Mobile agents are typically used in wide-area and heterogeneous networks without worrying about security of the network connections. 2. The mobile agent s migration is initiated by the agent. 3. Mobile agents migrate to access resources available only at other servers in the network. 4. Mobile agents have multi-hop ability. After a mobile agent has moved to the first server, it might migrate further to other servers to fulfill its task Mobile Agent Toolkits Mobile agent toolkits were referred to the tool for primarily using the mobile agent construction. Mobile agent toolkits provide an environment where mobile agents live and execute. An agency or toolkit is responsible for hosting and executing agents in 22

42 parallel and provides them with an environment so that they can access services, communicate with each other, and, of course, migrate to other agencies. An agency also controls the execution of agents and protects the underlying hardware from unauthorized access by malicious agents (Braun and Rossak, 2005). Some examples of mobile agent toolkits and the organizations using them are listed in Table 2. Many different types of mobile agent toolkits differing in terms of functionality, technology and area of application have been developed since the announcement of Telescript (White, 1994), first mobile agent programming language developed by General Magic Inc. There are two common features of these toolkits: mobility and communication. Mobility is necessary for migration of mobile agents. When exchanging messages during the migration, agents may need to communicate with others. Table 2: Overview of some existing mobile agent toolkits (Braun et al, 2005) Mobile Agent Toolkit ADK Aglets Ajanta Concordia D Agents Grasshopper Mole Semoa Tacoma Tracy Voyager Organization Tryllian Open Source University of Michigan Mitsubishi Dartmouth College IKV University of Stuttgart Fraunhofer Society Uni Tromso University of Jena ObjectSpace/Recursion The most prominent examples are Aglets by IBM and Grasshopper by IKV. Aglets (Lange and Oshima, 1998) are Java mobile agents initiated by IBM Tokyo Research Labs in 1995, which have an environment and are the most famous mobile 23

43 agent toolkit. Beside the providing autonomous execution environment, IBM Aglets Toolkit also describes programming concepts for mobile agents chiefly related to this toolkit. Aglets API provides the fundamental functionality for Aglets to be created, managed, and dispatched to remote hosts. In other words, it allows Aglets mobility (Lange, and Chang, 1996). The Aglets Project is now an open source project at Sourceforge (Braun and Rossak, 2005). Voyager is a concept of mobile agents by ObjectSpace, started in 1996, and the product was purchased by Recursion Software, Inc. (USA). When compared with other mobile agents development platforms, Voyager integrates with Java more intensely, which can be used to develop mobile agent systems, and also to build up traditional distributed system. Voyager is a pure Java distributed computing platform that can be utilized to rapidly produce distributed applications of high capability (ObjectSpace Voyager Core Package Technical Overview, 1997). Grasshopper (Bäumer, Breugst, Choy. and Magedanz, 1999), which has been developed by GMD FOKUS and IKV++ GmbH, is a mobile agent development and runtime platform which is built on top of a distributed processing environment. An integration of the traditional client/server paradigm and mobile agent technology is achieved. Most importantly, Grasshopper has been designed in conformance with the first mobile agent industry standard, namely the Object Management Group's Mobile Agent System Interoperability Facility (MASIF) (OMG Mobile Agent Systems, 1995). In addition, the latest Grasshopper version is also compliant with the specifications of the Foundation for Intelligent Physical Agents (FIPA) (FIPA homepage, 2009) Standardization Object Management Group (OMG) was the first organisation to deal with agent standardization. OMG wrote a specification document called Mobile Agent System Interoperability Facilities (MASIF), formerly known as Mobile Agent Facility (MAF) (OMG Mobile Agent Systems, 1995), which focuses on an agent transport protocol and some common functions that every MASIF-compliant toolkit must implement. MASIF is based on CORBA as system infrastructure. MASIF does not define anything related to agent communication, because this issue is extensively 24

44 addressed by CORBA. To handle security issues of mobile agents, MASIF also relies on CORBA principles. MASIF is actually a set of definitions and interfaces for interoperable mobile agent toolkits. It consists of an interface for agent transfer and management (MAFAgentSystem) and one interface for locating and naming mobile agents (MAFFinder) (Braun and Rossak, 2005). Several Mobile Agent Toolkits are MASIF compliant, such as Aglets (Lange and Oshima, 1998), Grasshopper (Bäumer, Breugst, Choy. and Magedanz, 1999), SMART (Wong, Helmer, Naganathan, Polavarapu, Honavar, and Miller, 2001) or SOMA (Bellavista, Corradi, and Stefanelli, 2001). Foundation for Intelligent Physical Agents (FIPA), an organization geared at producing standards for the interoperation of heterogeneous agents, is another standardization approach in agent technology. The FIPA specifications defined the issues on agent communication, including agent communication languages (ACL), message transport protocols, and ontologies, but do not consider agent mobility in first publication. In 2000 a new set of FIPA specifications was released. The specifications also included issues for agent mobility. FIPA specifications are used in more recent Mobile Agent Toolkits, such as JADE (Bellifemine, Caire, and Greenwood, 2006) or Mobile-C (Chen, Cheng, and Palen, 2006) Major Technical Advantages of Mobile Agents It is known that mobile agents are the notation of weak agency with the mobility capability that identifies the migration to other host in the heterogenous networks. Mobile agents provide a new and interesting approach to the distributed systems and it should be believed that they will be the most promising technology to solve most of the problems of the networked future (Braun and Rossak, 2005). Although it is possible to propose an alternative, based on existing technology, to almost every mobile agent-based function (Chess, Harrison, & Kershenbaum, 1995), mobile agents have significant advantages over conventional approaches at the 25

45 design, implementation and execution stages in most cases. The motivation for using mobile agents stems from a number of anticipated benefits (Kinshuk, Hong, and Patel, 2002): Efficiency and reduction in network traffic: Mobile agents consume fewer network resources since they move the computation to the data rather than vice versa. Also, mobile agents can package a conversation and ship it to a destination host, where interactions can take place locally, hence, reducing the network traffic as shown in Figure 4. Asynchronous autonomous interaction: Tasks can be encoded into mobile agents and then dispatched. The mobile agent can operate asynchronously and independent of the sender program. Interaction with real-time entities: Real-time entities require immediate responses to changes in their environment. Controlling these entities from across a potentially large network will incur significant latencies. Mobile agents offer an alternative to save network latency. Local processing of data: Dealing with vast volumes of data when it is stored at remote locations, the processing of data is inefficient over the network. Mobile agents allow processing to be performed locally, instead of transmitting data over a network. Support for heterogeneous environments: Both the computers and networks on which a mobile agent system is built are heterogeneous in character. As mobile agent systems are generally computer and network independent, they support transparent operation. Convenient development paradigm: The design and construction of distributed systems can be made easier by the use of mobile agents. Mobile agents are inherently distributed in nature and hence are natural candidates for such systems (Kinshuk, Hong, and Patel, 2002). 26

46 Client-Server Approach Mobile Agent Approach Figure 4: Client-server paradigm vs. Mobile Agent approach (Kinshuk, 2004) Application Domains of Mobile Agents Mobile Agent is a continuously increasing interest on the agent technology research because of having some advantages such as reducing the bandwidth usage, total completion time, and latency, continuing process when disconnected, load balancing, dynamically deploying components. Therefore, it has a wide application area where mobility is mostly needed, such as information retrieval, e-commerce, network management, load balancing, and so on (Braun and Rossak, 2005). Most mobile agent applications are developed in the field of electronic commerce, for example, online shopping or electronic marketplaces. For example, Agents in Telescript environment are the providers and consumers of goods in the electronic marketplace applications (White, 1994). Furthermore, one of them is the market- 27

47 based resource-control component of D Agents. The D'Agents system supports interagent economic transactions with a system infrastructure consisting of a currency model, a banking system, and a set of currency aware resource managers (Bredin, Kotz and Rus). Mobile agents that move across a network consume resources. D Agents has a system for controlling the activities of mobile agents that uses electronic cash, a banking system, and a set of resource managers. Another example of Mobile Agent is the IMAGE (Intelligent Mobility Agent for Complex Geographical Environments) project. It has the mobile agent that is similar to traditional travel agent. It assists the user on travel decisions between two geographical points, completes the purchase of the travel and accommodation, and also provides information about possible activities during the travel process, or at the point of destination (Edwards and Blythe, 2003). The IMAGE project provides users with mobile, personalized, location-based information on transport and tourism related services, how to reach them, and how to pay for them by flexible, mobile, and stationary means (Edwards and Blythe, 2003) Type of Mobility A mobile agent has a structure consisting of code, state and data parts (Bellifemine et al, 2007). Figure 5: Basic Structure of Mobile Agent (Bellifemine et al, 2007) 28

48 The code is the program that is executed by the moved agent after the migration. Script languages, such as Perl, TCL (later renamed to D Agents), Telescript (an object-oriented, remote programming language (White, 1994) or Java could be the source code. It is the portable intermediate Java byte code format; and with the C programming language, it could be the executable machine language format for a single processor (Braun and Rossak, 2005). The state is the snapshot view of the agent environment just before starting the migration process. It might be quite complete information from within the underlying (virtual) machine about call stack, register values, and instruction pointers. Hindered by some of limitations in the Java virtual machine, many Java-based mobile agent toolkits do not provide a strong determination of the execution stack (Braun and Rossak, 2005). The data part is all of the variables used by agents. Mobile Agent can move all its code, data and state to destination platform during the migration process (Bellifemine et al, 2007). Type of mobility can be weak or strong. This distinction is done according to the way of the rebuilding agent of the destination system after migration occurs. Also it is related to the way of storing the snapshot state of the agent before migration. In weak mobility, there is a fixed point at agent code for starting migration and the current data at this point is captured to initialize the agent at new host. During the initializing, the agent will restart on the destination platform by using this data. In strong mobility, migration starts at any point of the agent s execution and also it resumes the execution from this point when migration occurred (Schoeman and Cloete, 2003). Whereas all parts of an agent are migrated to destination in strong mobility, execution state of agents is discarded during the migration in weak mobility. Mobile agents are programmed and deployed on Mobile Agent Toolkits. Many different mobile agent toolkits have been developed. Some of them provide the strong mobility, such as Telescript (White, 1994), D Agents (Brazier, Overeinder, Steen, and Wijngaards, 2002). On the other hand, Aglets (Lange, and Oshima, 1998), Grasshopper (Bäumer, Breugst, Choy. and Magedanz, 2000), SeMoA (Roth, and 29

49 Jalali-Sohi, 2001), and JADE are the mobile agent toolkits that provide the weak mobility (Brazier, Overeinder, Steen, and Wijngaards, 2002). 3.4 Agent Usage in Distance Education Agents have important roles in e-learning. They enhance the learning content with experience and LMSs. They are used to give help, advice, feedback, or to act as a peer learning or to participate in assessments/simulation or to personalize the learning experience. Also, they can facilitate the participation, interaction, and instructor s activities in LMS. Pedagogical agents, the autonomous agents in education, support human learning by interacting with learning in the context of interactive learning environments (Shaw, Johnson and Ganeshan, 1999). Pedagogical agents employ features that add expressiveness and believability to the look of the user interface, - features that have proven to enhance user motivation and satisfaction (Lester, Converse, Kahler, Barlow, Stone, Bhoga, 1997). They serve as pedagogical expert and cognitive tools for learning. They can interact with learners and other agents so that they create a cooperative learning environment and also are able to play a powerful motivational role. Animated pedagogical agents, the animated characters, facilitate human learning in computer-based learning environments. These agents have animated personas that respond to user actions (Johnson, Shaw, Ganeshan, 1998). An animated pedagogical agent introduced immersively into a learning environment can observe students progress and provide them with visually contextualized problem solving advice. They can engage in a continuous dialogue with the student, and emulate aspects of dialogue between a human teacher and student in instructional settings. In the recent years, animated pedagogical agents in the interface of the learning systems have become increasingly popular. There are several implemented animated pedagogical agents. Herman-the-Bug: Herman-the-Bug (Lester, Stone & Stelling 1999) is a lifelike agent that has visual and verbal actions controlled by a real-time behaviour- 30

50 sequencing engine (Stone & Lester 1996) in response to changing problem-solving contexts. These actions composed of many activities including walking, flying, swimming, shrinking, expanding, fishing, bungee jumping, teleporting and acrobatics. Design-A-Plant where Herman-the-Bug lives is a learning environment for the domain of botanical anatomy and. Cosmo: Cosmo (Lester, Voerman, Towns & Callaway 1999) is a 3D character provides problem-solving advice in the Internet Protocol Advisor, a learning environment for the domain of Internet packet routing. Cosmo helps to teach network routing mechanisms by navigating through a series of subnets. Cosmo has the ability of agents to dynamically combine gesture, locomotion and speech to refer to objects in the environment while they deliver problem-solving advice (Johnson, Rickel & Lester 2000). Adele: Adele (Johnson, Shaw and Ganeshan, 1998), pedagogical agent, is designed to operate over the the World Wide Web. Adele-based courses are currently being developed for continuing medical education in family medicine and graduate level geriatric dentistry. Adele-based courses provide the case-based clinical diagnosis applications. PPP Persona: PPP Persona (André, Rist & Müller 1997), an animated pedagogical agent for interactive WWW presentation. The persona agent appears in cartoon figures or 3D models during the guiding the learner through Web-based material. The persona has the some presentation acts, such as showing, pointing, explaining and verbally commenting textual and graphical output on a window-based interface. 3.5 Mobile Agent Usage in Distance Education Of course it is assumed that the pedagogical agents can be mobile, capable of moving from one physical place to another and they may learn from the environment by observation. In addition, the mobile agents are anticipated to provide a solution for some of the issues in web-based distance education. Some of the problems are indicated as follows: Slow access to course materials because of the the Internet connection bandwidth limitations; 31

51 Inadequate adaptation to individual students, because the interactions between client and server normally take place using hypertext transfer protocol (HTTP). HTTP is a stateless protocol, which makes it difficult to track the students progress and, hence, analyse the mental processes of the student (Kinshuk and Patel, 1997); and Hard-to-achieve, continuous, real-time interaction between student and system, because of the connection unreliability and bandwidth limitations, or the student may not be able to maintain continuous online connection (Kinshuk, Hong and Patel, 2002). In order to provide a solution for these problems applications using animated pedagogical agents mentioned in the previous chapter have been developed. In addition, some applications were developed to provide the adaptivity, such as InterBook (Brusilovsky, Schwar, & Weber, 1996) that supports adaptivity and authoring. However, these applications are considered as a far cry from getting a concise solution all by themselves (Kinshuk, Hong and Patel, 2002). The connections with unreliable and low bandwidth make it hard to access course materials, each standing up as a profound info pool. Aside from that vantage point, searching data among these course materials and getting updates on an instant interaction arrangement, the systems aiming to provide each student with their individual self interests and learning styles with the characteristics like adaptability (Crampes 1999; Oppermann & Specht 1999) and personalization (Crampes 1999; Oppermann & Specht 1999) are also hard to achieve. The examples analyzed shows that mobile agents are used both for advancing search mechanisms and gaining adaptation in distance education. In a learning resource service system, unlike traditional search method, mobile agents can support a flexible autonomous search method. With the help of mobile agents, we can search the learning resource more neatly and intelligently. Also, the usage of the emerging mobile agent technology can facilitate better interaction opportunities among students and teachers, and adapt to the needs of individual students (Kinshuk & Patel, 2002). 32

52 3.5.1 PIAVEE Platform Independent Agent-based Virtual Educational Environment (PIAVEE) is a prototype of a virtual education environment using agent technology as the management system and implemented using a combination of software technologies (Chhetri, Krishnaswamy, Markham, Hurst, and Casey, 2004). The primary development language is Java. The mobile agent toolkit being used in the implementation is Grasshopper (Bäumer, Breugst, Choy. and Magedanz, 1999). Within the overall operation of PIAVEE there are two phases of use: the first provides facilities for the insertion and indexing of educational resources, while the second provides search and retrieval facilities. PIAVEE aims to embody two principles: pedagogical soundness and technological innovation and also to provide an environment that transcends the simple storage of educational material to provide also a framework for intelligent information retrieval and personalization. PIAVEE is dynamic in its support for collating learning materials in a virtual learning environment and has minimal overhead for the teacher and also provides support for collation of materials for the student. It also enables reuse in the context of curriculum development activities. Overall view of PIAVEE is shown at Figure 6. 33

53 Figure 6: Overall View of PIAVEE (Chhetri et al, 2004) Repository Management System, based on a group of mobile agents, controls the development, access and update of the database. Importantly, below the Repository Manager is the Content Search & Management System that maintains dynamic supervision of links to items, reducing the likelihood of broken links (Chhetri et al, 2004) Campus Campus (Westhoff and Unger, 1998), implemented with the help of the Agent Application Programming Interface (AAPI) package, supports the communication and co-operation between a distance teaching university and its students in an Internet environment. AAPI supports the design and implementation of systems of mobile, autonomous agents and is based upon decentralised control structures. 34

54 While students usually connect quite rarely to the Internet, they can continuously be represented in the Internet by their individual autonomous agents which can fulfil a variety of tasks for their owners. According to their tasks, the agents migrate through the network, collect information for their owners, communicate and co-operate with other agents, until they are finally picked up again by their owners as soon as they reconnect to the network. Besides minimising the student access times to the network, cooperation and communication between agents may stimulate co-operation and communication between students as well. Campus is modeled as a two layered network at Figure 7. While the outer layer contains all computers which are rarely connected to the network, the inner layer contains the computers which are almost permanently connected to the network. Computers in the outer layer send their agents to computers in the inner layer, let them perform their tasks and pick them up again, if necessary via reverse routing. The inner layer provides various service, information and chat booths, where agents on behalf of their owners can. Each of these booths contains one context where travelling agents can dock. Campus contains a variety of protocols for communication and co-operation between agents and booths. Figure 7: The two layered network of Campus (Westhoff et al, 1998) 35

55 3.5.3 TILE TILE (Hong, Kinshuk, He, Patel, and Jesshope, 2001), The Technology Integrated Learning Environments, is developed at Massey University. TILE is a software research project that provides an integrated system for the management, authoring, delivery and monitoring of education at a distance. One of the main aims of the TILE is to provide adaptive learning environment to the students. Mobile agent technology is applied in the TILE project for providing this aim. Mobile Agent picks up data from client side and moves to other side to perform all the processes, then returns back with information needed. As seen in Figure 8, a mobile agent interacts with client side inference engine to pick up data, which relies on individual student model at client side. Then mobile agent moves to the host (or server) side. At host side, mobile agent performs all the processes needed, such as updating the partial individual student model based on summary of client side student model (based on the data brought by mobile agent), interacting with group student model to determine if that student model also needs to be updated. After mobile agent finishes all actions at host side, it gathers all the information it needs, and returns to the client side. Then it updates the client side individual student model. This mobile agent approach works even in the intermittent connectivity between client and host because mobile agent can be dispatched when the connection is available and then the agent works autonomously without requiring continuous connection (Hong, Kinshuk, He, Patel, and Jesshope, 2001). 36

56 Figure 8: Implementation of mobile agents technology in adaptation mechanism (Hong et al, 2001) 3.6 JADE Framework. In this study, JADE (Bellifemine et al, 2007) is chosen as the agent development environment. JADE is a FIPA-compliant java agent development environment developed. It is used to create multi-agent systems applications. JADE provides a set of tools that are used for debugging and deploying distributed agents. JADE have some add-ons. IPMS and WSIG are add-ons of JADE add-ons IPMS Mobility service in JADE known as intra-platform mobility is based on Java RMI calls. It is the default option in JADE service architecture. But the service only provides a migration between the containers (Bellifemine et al, 2007). Therefore, Jade add-ons, Inter-Platform Mobility Service (IPMS), is integrated with MODA MAS System so that UlakAgent is transformed into a mobile agent having the ability of migration between the platforms. JADE and also IPMS provide weak inter-platform mobility (Figure 9). JADE Mobility support Push cache transfer protocol (PCTP) (Figure 10) and On-demand transfer protocol (ODTP) (Figure 11). The ODTP and PCTP is a protocol that used to transfer the agent code, data and state. In ODTP, only the data and state are firstly 37

57 transferred. If the code or some other agent resources are transferred they are needed. The PCTP allows the use of a code caching mechanism so that the agent code that is already present in the remote host will not be transferred (Cucurull, Martí, Navarro, Robles, Overeinder, and Borrell, 2009). Figure 9: Equivalent algorithms using strong and weak mobility (Cucurull et al, 2009) 38

58 Figure 10: Instance push transfer protocol diagram (Cucurull et al, 2009) The agent code is packed into a Java JAR file when transferring over the PCTP protocol. The Jar file is migrated to remote hosts. On the other hand, it is sent as individual Java classes when transferring over the ODTP protocol. The agent data are packed as bytecode in the two protocols. The bytecode in package is generated by the Java Serialization mechanisms. In the weak migration, the state of agent is not migrated to remote host. Because of using the code cashing mechanisms in both protocols, there are some advantages, saving up network bandwidth and reducing code retrieval delays, etc. According to the migration conditions, agents choose the appreciate protocol at its lifetime (Cucurull et al, 2009). 39

59 Figure 11: On-demand transfer protocol diagram (Cucurull et al, 2009) WSIG The WSIG, Web Service Integration Gateway, is the JADE add-on that provides the bidirectional interconnectivity between agents and Web services. It allows the invocation of Web services from JADE agents and JADE agent services from Web service clients (JADE Board, 2008). The WSIG is also a web application that supports the standard Web service technologies, WSDL, SOAP, and UDDI. WSIG has two main elements (Figure 12): WSIG Servlet WSIG Agent. The WSIG Servlet is the front-end towards the Internet world and is responsible for Serving incoming HTTP/SOAP requests Extracting the SOAP message Preparing the corresponding agent action and passing it to the WSIG Agent 40

60 Figure 12: WSIG Architecture (JADE Board, 2008) Moreover, once the action has been served, it acounts for Converting the action result into a SOAP message Preparing the HTTP/SOAP response to be sent back to the client The WSIG Agent is the gateway between the Web and the Agent worlds and is responsible for Forwarding agent actions received from the WSIG Servlet to the agents actually able to serve them and getting back responses. Subscribing to the JADE Directory Facilitator (DF) (Bellifemine et al, 2007) to receive notifications about agent registrations/ deregistrations. 41

61 Creating the WSDL corresponding to each agent service registered with the DF and publish the service in a UDDI registry if needed (Bellifemine et al, 2007). Two main processes are continuously active in the web applications integrated with WSIG: The process responsible for intercepting DF registrations/deregistrations and converting them into suitable WSDLs. As mentioned, this process is completely carried out by the WSIG Agent. The process responsible for serving incoming web service requests and triggering the corresponding agent actions. This process is carried out jointly by the WSIG Servlet (performing the necessary translations) and the WSIG Agent (forwarding requests to agents able to serve them) (Bellifemine et al, 2007). In this study, WSIG is used for remote invocation of Web services by UlakAgent, JADEAgent, and of JADE agent services by Web services (for retrieving of course contents from remote System over its MODA). 42

62 CHAPTER 4 4 UlakAgent In this study, we used agent technology to support the learner in virtual learning environment. We proposed an agent that perform search and collect learning contents in a global manner, namely UlakAgent. UlakAgent is a mobile agent that moves to each MODA system in the community. Instead of user, the agent searches the desired content by travelling each system. It collects the appropriate contents and also prepares search results according to user profile. It acts as a research assistant of LMS user. UlakAgent gets the search criteria and visits each LMS to collect appropriate learning content from other LMS repositories and return home with the results. In order to construct the MODA community, some changes have been made on MODA system and solutions in demand have been tried to be put forward by making use of additional technologies. MODA, the changes made on MODA system, the MODA community that has been formed, and the technologies used in MODA community are explained in the following sections. 4.1 MODA MODA (Serce, and Alpaslan, 2008), a multi-agent system, has been developed to provide adaptiveness in learning management systems (LMS). MODA is a conceptual framework for adaptive learning systems and this framework is based on the idea that adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of learners and the content type of learning material are used to match the learner to the most suitable content (Serce, and Alpaslan, 2008). 43

63 As Kinshuk and Patel have declared, one of the problems existing in the web-based distance education systems is the inadequate adaptation to individual students (Kinshuk and Patel, 1997). In fact, this is an objective that is predicted in also distance learning rather than a problem. Some adaptive systems have been developed in order to present a solution for this problem and to reach the objective. In the research that he made on adaptive systems, Brusilovsky (Brusilovsky, 1999) has described several systems, such as InterBook (Brusilovsky, Schwar, & Weber, 1996), ACE (Specht, 2000), and ILESA (Lpez, Milln, Prez-de-la Cruz, and Triguero, 1998). The common point of these studies which employ different adaptive technologies each is the fact that they are embedded in the environment they have been developed in. Namely, they are the ones which can only be applied to the related systems. It is previously stated that different tractable technologies have been used in these adaptive systems. The adaptive technologies includes ITS (Intelligent Tutoring Systems) including curriculum sequencing, intelligent analysis of student s solutions, interactive problem solving support; adaptive hypermedia technologies involving adaptive presentation and adaptive navigation support; web-inspired technologies like student model matching. When looked from this angle, it is seen that the existing adaptive systems appear along only with the computer-based learning environment in which they have been developed and that they generally ensure adaptiveness with a single adaptive technology. MODA has been designed both as a system independent of LMSs and as a structure that can use different adaptive technologies (to provide adaptiveness). Here the main goal is to bring LMS in the faculty of adaptiveness without making much change in the system of LMS by applying the intended adaptive technology on the intended LMS (the ones already present). In short, MODA Adaptive System can be a plug-in for any LMS The Conceptual Framework of MODA The framework is based on the idea that the adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of 44

64 learners and content type of learning material are used to match the learner to the most suitable content (Serce, and Alpaslan, 2008). The MODA conceptual framework consists of learner profile, course content profile, the matching strategies between learner and course content profiles (Table 3), and the initialization and update strategies of the profiles. The framework is based on the idea that the effectiveness of the adaptiveness is highly dependent on how much we know about the learner and how much the available content fits to the learner profile. Therefore, the learner and the course content were modelled. The matching process is between the style of the learner and the type of the content. The aim of the matching process is to find the appropriate content using the content types in respect of the learner profile. Table 3: Thirty Content Types used in the framework (Serce, 2007) Both the course content profiles and the student profiles are situated in one single center that is, MODA. As a result, for different MODA platforms to work simultaneously with diverse LMSs will be feasible, and sharing data among those platforms will be done independently from the LMS Learner Profile Learner profiles are one of the most important components that can have the feature of adaptability. The better molded the student profiles are, the easier it will be to differentiate students from one another and deliver a learning environment suitable for individuals. 45

65 MODA knows the learner by keeping a variety of information about the learner, such as, learning styles, domain knowledge, progress, preferences, goals, interests, etc. In MODA, the learner is modeled according to three factors which are behavioral factors, knowledge factors, and personality factors (Sumner, 2006). The entire functions a student performs on LMS are transmitted to MODA system and is saved on the personal student profile. These students' information is kept under control by LearnerProfileAgent in the MODA system Course Content Profile In MODA, under the light of the learning styles descriptions and content models used in literature, 30 different course content types have emerged, given in Table 3. For each and every course content, the types and the space it occupies are kept in MODA. Course content profile is controlled by the LearnerProfileAgent in the MODA system Learner-Course Content Matching Mechanism The adapted content is the content that is best matched with the learner profile. In MODA, there is a matching mechanism between the learner profile and the course content profile. In this mechanism, Euclidian distance is used to find a matching score based on the normalized distance (Serce, 2007). Variations between a student profile and all of the content profiles in hand is estimated and listed. The one with the least variation is considered as the most suitable content The Multi-Agent System Architecture MODA was developed as a multi-agent system plug-in to work with an LMS. In the system, there are three main modules: LMS, MODA and LMS-MODA interface module (See Figure 13). LMS can be any LMS providing online learning services to learners. MODA is the multi-agent system. It has several agents that perform the adaptive services required by LMS. LMS-MODA interface is the communication platform of these two separate modules. A socket-based communication protocol is developed to provide the communication between any LMS and MODA. The protocol 46

66 supports some data packets, such as NULL, COURSE-CONTENT-KEY-LIST, LOGIN, GET-ADAPTIVE-CONTENT (Serce, 2007). Figure 13: The Architecture of MODA (Serce, 2007) MODA Agents MODA system has seven learning agents: LMSInterfaceAgent, LearningAgent, ContentAdapterAgent, CourseProfileAgent, LearnerProfileAgent, ResearcherAgent and AgentManager (Serce, 2007). The descriptions and roles of each agent are as follows: LMSInterfaceAgent is the communication party with the LMS. It behaves as the MODA server. It receives MODA request from the LMS part and provides request to the LearningAgent. It receives results from the LearningAgent and provides MODA Respond message to the LMS. 47

67 LearningAgent is the central agent which is responsible for management of the other agents. ContentAdapterAgent is responsible for finding the most appropriate content for the learner using the learner profile. This agent communicates with the LearnerProfileAgent to receive the learner profile information. After classification and matching, it sorts and filters the course contents. It communicates with the LearningAgent to send adapted course contents back. This agent also interacts with the ResearcherAgent to provide adapting search results. CourseProfileAgent initializes and updates the learner profile. The agent deals with the course profile classification of the course content types. It provides course profile information requested by the other agents. LearnerProfileAgent initializes and updates the learner profile. This agent updates the learner profile using learner actions. It provides the learner profile information requested by the other agents. ResearcherAgent receives search results, communicates with ContentAdapterAgent and receives the adapted content. This agent sends adapted search results back to LearningAgent. The agents in MODA were developed as JADE agents (See Figure 13). JADE (Java Agent Development Framework) (Bellifemine, Poggi, and Rimassa, 2001) is a software development framework aimed at developing multi-agent systems and applications conforming to FIPA standards for intelligent agents. Figure 14 shows the communication and dependency between the agents (Serce, 2007). 48

68 Figure 14: The Acquaintance Model of MODA Agents (Serce, 2007) LMS-MODA Communication Protocol In order to achieve the modularity of MODA System, a protocol is developed to provide the communication between LMS and MODA. Any LMS providing necessary information with the required format becomes an adaptive learning management system, when it establishes a communication with MODA. It is a TCPbased protocol providing communication between MODA and LMS (Serce, 2007). The developed protocol uses the TCP socket for reading/writing the necessary information. The data formats are also defined to realize the communication over the sockets. The formats are used in the exchanging data between the systems during either requesting data or responding a request. LMSInterfaceAgent, one of the MODA agents, behaves as a communication server that provides the communication between LMS and MODA. It receives the requests from the LMS and provides the responses back (Serce, 2007). 49

69 Figure 15: Request Packet Structure (Serce, 2007) Figure 16: Response Packet Structure (Serce, 2007) 50

70 Communication is performed through data packets. A packet can be either a request or a response packet. The packet structure of request and response are provided in the protocol. Figure 15 and Figure 16 show the structure of the request and response packets, respectively (Serce, 2007). LMSInterfaceAgent in MODA listen the requests from the integrated LMS. Four requests are defined in MODA communication, Login, Get Adapted Content, Log Learner Action, and Search. It receives the requests and provides the responses back. Figure 17 depicts the request and response messages exchanged between LMS and MODA (Serce, 2007). Figure 17: Request and Response Data in Communication Scenarios between LMS and MODA (Serce, 2007) 51

71 The protocol defines the data files exchanged during the communications. For each request from the LMS, one data file is defined in protocol. There are four main data files defined in the protocol: NULL, COURSE-CONTENT-KEY-LIST length (Figure 18), LOGIN, GET-ADAPTIVE-CONTENT (Figure 19) (Serce, 2007). Figure 18: The Data File Structure for Course Content Key List (Serce, 2007) Figure 19: The Data File Structure for Course Content List (Serce, 2007) 52

72 4.2 UlakAgent UlakAgent is developed as JADE mobile agent. By having all of the advantages of mobile agents (see section 3.3.4), it fulfills its duty while circulating inside the community we created. In this study, a new action named as research action has been added to the MODA system. It provides the opportunity of transferring both the keyword information and also the request to search. UlakAgent has been designed to assist the users when searching for content. Lets assume that the user has requested the following commands: find the content suitable for my profile in which also includes the keyword java, then list the results in descending order correspondingly. This demand from the user transmitted to the MODA via Research action. Research action receives the keyword send by the LMS, and forwards it to LMSInterfaceAgent. Thus, initiates the research process and calls out for the LearningAgent. Upon receiving the search keyword, LearningAgent forms an assistant UlakAgent to carry out the task. This particular UlakAgent starts searching with the keyword java. UlakAgent reaches the MODA Community Registration List. UlakAgent will complete its mission by circulating the defined MODA systems one by one. There are two main assignments in all of the systems it scannes: To find the course content including the search keyword. To get the profile information of those contents. At the very end of the list the UlakAgent provides, there stands the information of the local system. After completing its research in Remote system, UlakAgent returns to the local system, and finalizes the task by doing the same procedures here in local system. UlakAgent which fulfills its search task, informs LearningAgent about the course content it summoned from the systems it circulated. Upon receiving that information, LearningAgent erases UlakAgent from the MODA system. 53

73 By using the final result of the course content list and the profile information, LearningAgent executes the procedures of listing the results according to the user profile. The course content list arranged in order is initially forwarded to LMSInterfaceAgent. After that it is transferred to LMS as the result of research action. The list received by LMS includes the web service address information which helps the course content to be seen in the local system. When a user would like to see the content information of the course, CommunityAgent steps in. 4.3 Changes in MODA System for Exposing MODA Community As mentioned in the previous chapter, when MODA was first designed the agent to carry out the search was named ResearcherAgent. That particular agent was doing the search locally throughout the system it had been situated in. The necessity of making changes in the construction of information and in some of the descriptions have arisen because of the fact that the process of searching will both be carried out in and outside of the installed MODA community. Primarily, in this chapter, these changes will be discussed The Acquaintance of MODA Agents in MODA Community The acquaintance models simply define the communication links that exist between agent types. MODA has an agent having searching behaviour, ResearcherAgent. This agent tries to find available content resources in local content repository of the active LMS. The interaction between the ResearcherAgent and the other agents of MODA is given in Figure 14. Apart from the interaction exemplified in the Figure 20, UlakAgent, when assigned as the global search agent, has interacted with the CourseProfileAgent inside the system it was located. This is because UlakAgent is obtaining the profile information of the course materials after the search done in the remote systems by using this interaction. Contrary to the Figure 20 showing UlakAgent's run on the local MODA system, with the additional interaction on remote MODA systems; the acquaintance models it was included in were different in the Figure

74 Figure 20: The Acquaintance Model of MODA Agents in Community Search Process with UlakAgent in MODA Community MODA has a search process that realizes the search in a local system. A similar search process act has been developed for UlakAgent. Throughout this process, UlakAgent browses all of the MODA systems registered in the community and the steps between 10 and 15 are repeated in the number of the MODA systems registered there. All of these procedures are held in local MODA system. Each repetition action is done in the MODA system the UlakAgent is located in. This search process the UlakAgent is in charge of is carried out as shown below. 1. Learner searches for a keyword in LMS 2. LMS sends the search keyword to LMSInterfaceAgent 3. LMSInterfaceAgent receives the search keyword. 4. LMSInterfaceAgent sends the search keyword to UlakAgent 5. UlakAgent receives the search keyword 55

75 6. UlakAgent gets the next information of the remote MODA System from the list which keeps the MODA Systems registered in the community. 7. UlakAgent moves itself to other MODA platforms by using the address provided in the info. 8. UlakAgent reaches the related web service programmed for search in remote sites. 9. UlakAgent receives the search results. 10. UlakAgent sends all available search results information to CourseProfileAgent 11. CourseProfileAgent receives available search results 12. CourseProfileAgent classifies the contents and prepares search result content profiles 13. CourseProfileAgent sends search result content profiles to UlakAgent 14. UlakAgent receives the search results with content profiles and inserts into the other search results. 15. UlakAgent sends all search results with content profiles to LMSInterfaceAgent 16. LMSInterfaceAgent sends all search results with content profiles to LearningAgent 17. LearningAgent receives the search results 18. LearningAgent sends search results to ContentAdapterAgent to perform adaptation 19. ContentAdapterAgent receives all available search results with its profiles 20. ContentAdapterAgent requests learner profile information from LearnerProfileAgent 56

76 21. LearnerProfileAgent receives request for learner profile 22. LearnerProfileAgent prepares the profile 23. LearnerProfileAgent sends the learner profile information to ContentAdapterAgent 24. ContentAdapterAgent receives the learner profile 25. ContentAdapterAgent receives the content profiles 26. ContentAdapterAgent calculates the matching scores between the learner profile and each content profiles 27. ContentAdapterAgent sorts and filters the matching scores 28. ContentAdapterAgent sends best matched content information to LearningAgent 29. LearningAgent receives resulting adapted search results list 30. LearningAgent sends adapted search result list to LMSInterfaceAgent 31. LMSInterfaceAgent receives search result list 32. LMSInterfaceAgent sends search results list to LMS 33. LMS receives adapted content list and displays the search results in adapted way. An additional information belonging to each and every course content is given in the result list reached at LMS. The listed items are the name of the LMS (OLAT, Docebo, and Moodle), the web-service address of the WSIGAgent to be used to bring the course content, the name of the course, and finally its short description. With the information gathered, LMS displays the results of the search. 57

77 4.3.3 Research Action Scenario between LMS and MODA in MODA Community A communication protocol designed for the purpose of creating a MODA system working independently from LMSs has been mentioned in section If a LMS can interact with a MODA system through a TCP-based protocol, than this LMS becomes an adaptive learning management system (Serce, 2007). Because of the UlakAgent added to the MODA MAS system, Research Action concept has been also subjoined to these communication scenarios (login, get adapted content, log learner action, search) mentioned in section (Figure 21). When a learner performs a research action using the tools of LMS, LMS sends the search keyword to MODA. By using this keyword, MODA takes all of the search results both from all of the MODA systems registered in the community and from itself. MODA compares this list with the learner s profile, and performs sorting and filtering. Then, the filtered and sorted list with some additional information is sent back to the LMS. Finally, LMS displays the adapted search results and the additional information to the learner. These additional information are MODA-LMS system name, course name and its short description, information on the web-service address, which is required to bring back content, all belonging to the listed course resources. 58

78 Figure 21: Request and Response Data in Communication Scenarios between LMS and MODA in MODA Community The request and response messages exchanged between LMS and MODA was first given in Figure 17. This appendix mentioned in the communication scenario will be presented as in Figure Data Files in MODA Community MODA communication procol has basicly four main data files exchanged during the communications. As it has been putforward above the information transferred from 59

79 MODA to LMS with the research action is showing inconsistencies with the scenario given in section (4.1.3). To transmit the distinct information, a new data file named COURSE-CONTENT-LIST which is very similar to a COURSE-CONTENT-KEY- LIST has been designed. Its structure is depicted in Figure 22. Figure 22: The Data File Structure for Course Content Information List in MODA Community 4.4 CommunityAgent It is an agent used in order to transfer the results of the content belonging to a course material found after a research carried out in the remote systems. As it is mentioned in the section 3.7.2, WSIG provides the bidirectional interconnectivity between agents and web services. One of the information UlakAgent collect is the web service addresses of the WSIGAgent working in the remote system (Figure 22). Obtaining the course content is actualized by calling these web addresses. This web address triggers the WSIGAgent through WSIGServlet. Web service support is given via WSIGAgent and WSIGServlet. A web service to be used by WSIG, named CourseFunctions, has been developed. With the help of this web service CommunityAgent is called for duty. CommunityAgent has only one task to fulfill: to call for the web service (GetCourseContentWS) in the LMS -which is 60

80 integrated with the MODA system CommunityAgent is incorporated into- after being triggered via WSIGAgent, and to transfer the content to WSIGAgent as the final step. WSIGAgent converts this result into a SOAP message and delivers the message to the initial calling point as a response. All in all, LMSs that are unaware of one another has started to share course content information inside the community. 4.5 MODA Community with UlakAgent In this study, a MODA community was constructed with three learning management systems (Olat (Olat, 2009), Moodle (Moodle, 2009), and Docebo (Docebo, 2009)) that are the open source applications, and the mobile agent had its search travel in this community. Each LMS in the community was integrated with a MODA System that uses two JADE add-ons as shown in Figure 23. These add-ons are IPMS and WSIG. WSIG IPMS JADE Framework MODA Figure 23: MODA System UlakAgent searches content in local content repository of LMS and communicates with ContentAdapterAgent and receives the adapted content. This agent sends adapted search results back to LearningAgent. There is already a researcher agent in 61

81 MODA structure. However, this agent only searches in local. The purpose of the current study is to make this agent mobile agent so that it will go to other MODAintegrated LMSs and do search there and come back home with the findings. We name the union which is integrated with LMSs and is composed of MODA systems talking with one another as MODA Community. The one which primarily ensures communication in this union is UlakAgent. UlakAgent is a JADE agent which has intersystem mobility feature with IPMS, used to provide platform-toplatform mobility for JADE agents (Bellifemine et al, 2007). It can carry itself to other MODA systems in the Community. Through this feature, it realizes its searching function by circulating all community. The scenario given in next chapter explains the behavior of UlakAgent. WSIG add-on provides support for invocation of JADE agent services from Web service clients (Bellifemine et al, 2007). A WSIG Agent named CommunityAgent on MODA is developed for reaching the content of any search result through other LMS. It can call the any web-service defined on related LMS. WSIG Agent can be invoked by clicking the results shown on home LMS. The MODA community is developed to share the huge amount of course materials that existing repositories of LMS have. Of course, these course materials will be presented to user after adapting. To provide these aims independently, MODA Community is supported with the usage of a web-service. In Figure 24, we show the Architecture of MODA Community. Each LMS system in Community provides the web-service for searching the course resources (SearchCourseResourceWS) and getting the content of specific resource (GetCourseContentWS). While the searching service is invoked by UlakAgent, getting content service is called by user in other LMS. 62

82 DoceboWS Docebo LMS System MODA User Client OlatWS MODA OLAT LMS System MODA MoodleWS Moodle LMS System User Client User Client Figure 24: Architecture of MODA Community In MODA community, we assume that each integrated MODA system has also a WSIG Agent to directly communicate between any LMS and MODA on other LMS System. 4.6 Communication in MODA Community with UlakAgents In this study, LMS systems are assumed to be closed systems. Any LMS system is not aware of others. Therefore, their communication with each other is entirely made with MODA Community. The hypothesis that the system is independent of the LMSs has been attempted to be maintained and four different sorts of communication have been developed. Each type of communication has been developed with different technologies. 63

83 4.6.1 Communication between LMS Environment in Local and MODA in Local Primary objective of MODA was to make LMS systems adaptive. Accordingly, communication protocol mentioned in section was developed and messages which come via this protocol were delivered to LMSInterfaceAgent. Also, the results which were produced in accordance with determined works were delivered via the same protocol to LMS by this agent. User activities trigger the communication. When a user logins to a relevant LMS, consequently, he will also be connected to MODA. As a result MODA systems will be informed of which parameters to use in the other communication steps. These parameters are the web service information held by LMS, and cover which type of the LMS it is. As it mentioned previously, each and every LMS offers two different web services, SearchCourseResourceWS and GetCourseContentWS Communication between MODA in Local and MODA in Remote During this communication, UlakAgent has the ultimate control. UlakAgent which was triggered with research action visits all MODA systems one by one. In, its current system, it makes necessary searches (SearchCourseResourceWS) and takes the profiles of courses which was founded due to the search, and moves to the next system. Finally, it returns to the local system and transmits the results by LMSInterfaceAgent to LMS which it is connected to. Among the results, next to each and every course profile there stands both the origins of the LMS such as OLAT, Moodle, Docebo, and also the web service address which calls the WSIGAgent used in order to reach the course content. The assigned WSIGAgent interacts with the CommunityAgent that calls the web service named GetCourseContentWS. After CommunityAgent obtains the course content, it sends the content to WSIGAgent. The related contents are finally delivered to LMS system. IPMS, a JADE add-on, was activated for this communication. 64

84 4.6.3 Communication between MODA in Local and LMS Environment in Local In MODA community, each LMS services the web services. UlakAgent implements its search task by using this web service after moving itself to the target system. Each MODA system has the necessary parameters to connect to this web service, ie. the address of the SearchCourseResourceWS service provider, common authentication information Communication between LMS Environment in Local and MODA in Remote After the search operation, the results sorted by user profiles are listed on the user s screen. When clicking on one of remote results, taking the course context are done with this communication. The results are containing a web service URL to trigger WSIGAgent in remote MODA system. After invoking, WSIGAgent activates CommunityAgent. It takes the content with the calling the web service, GetCourseContentWS, from LMS and returns the content as service result to WSIGAgent. For this communication, WSIG, a JADE add-on, was activated. WSIGAgent comes with WSIG and it reorganized in accordance with given task, calling the web service, GetCourseContentWS. 4.7 Web Service The W3C (World Wide Web Consortium) defines a Web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-processable format (specifically WSDL). Other systems interact with the Web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web related standards (World Wide Web Consortium, 2009). There are the standards in the Web services framework. Service descriptions, communication protocols, and service discovery. 65

85 Figure 25: Use of SOAP and WSDL in a Web Service architecture (Halling- Brown, 2006) Service descriptions are defined by WSDL (Web Service Description Language) which is an XML-based language used to describe the Web Service and some of its behaviors. It specifies the operations offered by a service, the mechanisms to access Web service, and the location at which service is available. Also it copes with asynchronous interactions (Figure 25). The WSDL document is built using network services definition standard elements: Types a container for data type definitions using some type system (such as XSD). 66

86 Message typed definition of the data being communicated. Operation description of an action supported by the service. Port Type set of operations supported by one or more endpoints. Binding a concrete protocol and data format specification for a particular port type. Port a single endpoint defined as a combination of a binding and a network address. Service a collection of related endpoints. In Web services framework, UDDI (Universal Description, Discovery and Integration) commits the service discovery. It is commonly used to register and discover Web Services (Hao, Junliang, and Bingyu, 2007). UDDI registry contains the links to WSDLs of web services published in the Internet (Figure 25). SOAP (Simple Object Access Protocol) is the communication protocol in the Web services framework. It is used for exchange of information between the client and web service (Figure 25) Web Services in MODA community There is a TCP-based protocol providing communication between MODA and LMS. Because of this protocol, LMSs are independent of the presence of MODA. There is no need to make difference in LMSs structurally other than the commands that are used to send and receive the necessary information at this protocol between MODA and LMS. MODA can be considered as a plug-in which provides adaptiveness to LMSs. UlakAgent needs to access LMS sources to carry out a search in any MODA system. For this purpose, by using the given searching keyword, a web service called SearchCourseResourceWS which carries out a search in LMS was designed and developed. Accordingly, the fact that MODA has a structural dependence other than ensuring LMS adaptiveness was prevented and it is benefit from the properties of 67

87 web-service that is the software system designed to support interoperable machineto-machine interaction over a network. The MODA community created solely for this study includes tgree open source LMS: OLAT, Moodle, Docebo Learning Management System. Two web-services named as SearchCourseResourceWS and GetCourseContentWS have been developed for the three LMS. As mentioned before, SearchCourseResourcesWS is a web service utility which is called by UlakAgent to carry out a research task with a keyword. However, GetCourseContentWS is used by CommunityAgent. The results of the search are listed with the web-service address of the WISAGagent to the user. Whenever a user clicks on a resource item in the remote system, the system triggers the WSIGAgent first. Upon receiving course key information, it activated CommunityAgent. The activated CommunityAgent initiates the production of course content information by streching out to GetCourseContentWS web-service utility of the related LMS. The obtained content is displayed through the related LMS to the user. 68

88 CHAPTER 5 5 UTILIZATION OF ULAKAGENT In this study, there are three LMSes registered to MODA Community. Table 4 shows the example MODA Community for demonsration. After LMSInterfaceAgent receives the Search request from integrated LMS (for example, start point of migration is Moodle LMS) (Figure 26), UlakAgent searches the content in local repository with search key, then it moves to MODA environment integrated on Docebo LMS. Search process is done by using the web-service defined on DocebeWS. UlakAgent invokes the defined web-service, and obtains the search results. After receiving the results, course profile information is taken from CourseProfileAgent. UlakAgent again migrates to other registered LMS, Olat LMS. UlakAgent repeats the search process again on OLAT LMS and it returns home with search results and their course profiles. When the user clicks any result received from the others, WSIG Agent on related MODA is invoked. It calls the getting content service. Finally, user reaches the course content that is appropriate for her learning profile. Table 4: Example MODA Community MODA System Name IP Address HostName Local/Remote OlatMODA ModaServerOlat Remote DoceboMODA ModaServerDocebo Remote MoodleMODA ModaServerMoodle Local 69

89 The interoperability of the MODA Community was proven by using the demo contents. At example study, we chose Java term for testing search mechanism. After search request invoked by Moodle user was received by MODA System (Figure 26), LMSInterfaceAgent was forwarded the search task to UlakAgent. After this, it will finalize the tasks at each LMS. They are to migrate to other registered MODA in the community, to search keyword in the integrated LMS, and to get the results with course profile to starting point. As shown at Figure 27, with one click, the matched appropriate contents of all LMS in the community was listed. Figure 26: Moodle LMS integrated with MODA. 70

90 Figure 27: Some Search Results in Moodle LMS for Search Key Java. Figure 28: Search output in Moodle MODA System during the search process in community (Local System) 71

91 Figure 29: Search output in Moodle MODA System during the search process in community (Remote System-1) Figure 30: Search output in Moodle MODA System during the search process in community (Remote System-2) 72

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