Contribution To The Modeling And Implementation Of Multi-Agent Based E-Education System (Mage)

Nowadays there is an increasing demand for the e-Education market since the e-Education paradigm has the potential to revolutionize the way of learning by making it individual rather than institution-based, eliminating clock-hour measures in favor of performance and outcome measures, and emphasizing customized learning solutions rather than one-size-fits-all instruction regardless of geographical, temporal, physical, social, and economical constraints. Typically, an e-Education system is a high dynamical, open, unpredicted from system engineering view. in such setting, in addition to the common features of distributed systems such as concurrency, distributed, hypermedia, etc., the e-Education systems have the new features of autonomy, evolutionary life-cycle, collaboration, etc. Consequently, software engineering of such systems is confronted with a number of challenges, such as to deal with service-oriented computing, dynamic integration of autonomous components, distributed and mobile computing, etc.Although the benefits and potentials of the new generation of e-Education are obvious and exiting, unfortunately, so far the great potential of e-Education has been far from being taken full of advantage. Currently most of today's e-Education systems are dominated by the objectivist school and the use of technology as a substitute for a teacher delivering instruction. Current approaches to the online learning environment usually transfer traditional classroom instruction to an online setting, recasting reading materials as web-based materials. Apparently, the current e-Education does not seem to fulfill its promise to become the most important learning paradigm, especially in the context of the increased role of continuous and life-long learning. From the learner perspective, they often complain about the lack of flexible performance tools in support of personalized and tailored learning, value-added reflection, mutual simulative knowledge sharing, on-demand expertise finding, just-in-time peer help as well as efficient and timely tutor guidance. From the tutor perspective, the main drawback of current e-Education systems is that they tend to require more effort in terms of authoring learning materials and preparing tests or examinations than their classical counterparts. The necessity of mastering technology-intensive teaching tools and the lack of the tutor's computer literacy often make tutors reluctant to participate in online teaching activity. Consequently, it is obvious that on one hand, we need to provide learner with more intelligent learning environment that supports various customized learning services as needed, on the other hand, we need innovative mechanism to alleviate tutor workload in terms of facilitating the development of learning contents and test/exam by hiding as much technique details as possible. To address these issues mentioned above, in this dissertation, we launched a joint initiative named MAGE between HUST and ENIM under the support of DUO-France The eventual goal is develop an intelligent, flexible, personalized and open e-Education environment in order to provide an efficient mechanism to personalize the learner's learning process and the teacher's pedagogic process, diversify the learning paradigms and facilitate the development of teaching and learning materials. To achieve such goal, we explored, and adopted a series of innovative methodologies, theories, algorithms, and technologies derived from multiple disciplines such as Multi-Agent System (MAS), Learning Object (LO), Cognitive Theory (CT), Genetic Algorithm (GA), eXtensible Markup Language (XML), J2EE and so on. In particular, we, in this dissertation, concentrate on the approach of MAS as a container and supporting environment to integrating and encapsulating the above mentioned technologies and methodologies, as well as to modeling and implementing several typical e-Education applications at different levels and different contexts in terms of content authoring, individual and collective learning, expertise peer help finding, and test generation, delivery, assessment in distributed learning environment after deliberately taking into consideration the obvious advantage of MAS in terms of both its property such as autonomy, proactiveness, social ability and reactivity, and its distinctive features with regard to modularity, abstraction, parallel computation, robustness, scalability, legacy systems encapsulation, reliability, extensibility, robustness, maintainability, flexibility and reusability.To efficiently built an open, adaptive and personalized MAS based e-Education system, this thesis proposed an LTSA-compliant and MAS-based e-Education architecture—MAGE. This architecture consists of numerous agents, which perform different specific tasks on behalf of different learners, resources, applications or even computing by cooperation, negotiation, communication among them. In this way, we achieved a rather complex, dynamical, open, self-organizing and adaptive multi-user, multi-agent based e-Education system. In particular, our focus is concentrated on the following aspects:In the domain of the course authoring, the key issue is how to develop instructional materials of high quality that could be reused and applied to different contexts. Unfortunately, these instructional contents are, traditionally, expensive and time consuming to produce. To this problem, this thesis put forward an architecture of multi-agent enabled course authoring model based on e-Education object (MEEOCAS), involving the proposition of the concept model and the definition, structure and package model of the EEO. Under support of this subsystem, the course designers may conveniently develop their courses through assembling the ready-made learning objects (i.e., EEOs) instead of creating them from the scratch. To enhance the flexibility, this framework also supports several services such as subscription, searching, and registration and publishing of learning objects.As far as adaptive and personalized learning is concerned, this thesis proposed a MAS based integrated framework in support of adaptive and active learning in both individual and collective learning spaces. The distinct advantage of the proposed framework consists in the efficient integration of the two adaptive mechanisms by virtue of the cooperation, negotiation and communication among multi agents. In the adaptive individual space, the key issue is how to dynamically generate personalized learning path consisting of domain concepts and present associated learning objects catering for a learner's knowledge state and learning preference. As to this, this thesis put forward an efficient searching algorithm for the presentation generation based on the proposed domain ontology model. In the collective learning space, our focus is on the issue how to find appropriate help resources (e.g. peer learners, learning materials, or other applications) and how to dynamically build a tailored learning group on behalf of learners in a distributed network according to their need. In this regard, this thesis proposed two corresponding architectures: One is the peer help system, another is architecture of the learning group forming system, in which, individual learners can establish a .collaboration profile. indicating the characteristics of the group they would like to participate. The proposed collective learning architecture is based on several agents which perform functions such as seeking for potential collaboration partners, expressing which collaboration services are to be used, and monitoring collaborative learning activities.With regard to the e-assessment, the traditional computer based evaluation mechanisms rely predominately on the client-server model. Such mechanisms usually do not scale well and do not fully support features like automatic test generation, evaluation of subjective questions, delivery of dynamic content, off-line examinations, flexible communication between online evaluation components, and proactive event notification etc. to address such issues, this thesis put forward an innovative holistic solution to modeling large-scale on-line assessment system by applying the new generation of mobile agent based distributed computing paradigm. In particular, the most significant innovative point consists in that we proposed and designed an innovative model of automatic test generation by seamlessly integrating genetic algorithm, mobile agent, and MAS. Since mobile agents are autonomous and dynamic entities that have the ability to migrate between various nodes in the network, they offer many advantages over traditional design methodologies like reduction in network load, overcoming network latency and disconnected operations etc.Eventually, in order to verify and validate the feasibility and efficiency of the models proposed in this thesis, we implemented and simulated part of the models with the JADE framework. especially, we implemented three typical applications proposed in this thesis. First, we implemented the simplified prototype of GAMASTP for the purpose of synthetically revealing how to concretely implement a complex multi-agent system, which is concerned with several key issue: how to implement test ontology and apply to the communication among agents; how to design and implement agent behavior model according to the previous models; how to deploy agents over different network nodes. the second application is implemented for the purpose of how the learner model agent updates the learner model upon receiving the refresh data as well as how to answer any questions from external agents, this example also showed the application of interactive protocols such as FIPA request and FIPA query. The third example is used to implement part of the peer help system aiming at demonstrating the process how to find appropriate competent peer learners. The simulation results show the feasibility and efficiency of the models proposed in this dissertation.