Preface

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With the rapid development of computer network and information technologies, especially the Internet and World Wide Web, university and college programs offered in distributed environments are alternative forms of education for those students who are best served by flexible location and time schedules. The situation in which distributed education is primarily used in selective situations to overcome problems of scale (not enough students in a single location) and rarity (a specialized subject not locally available) is being changed. There are three major upcoming trends:

• Multimode Integration. The boundary between information technology (IT) application in education and distance education will become blurred. Distinctions between different forms of educational telecommunications have fallen away. New wireless bandwidth services further help to blur the distinctions. Moreover, educators must help all learners become adept or at least get familiar with distributed interaction by using multiple technologies, for skills of information gathering from remote sources and of collaboration with dispersed team members are central to the future workplace as learning to perform structured tasks quickly was central to the industrial revolution.

• Learner-Centered Environments. With the advances in natural language processing, reusable learning objects, and agent technology, learners will simply talk to their computers, describe the desired learning goal, and piece together applications by sending intelligent agents to grab suitable learning objects from learning objects repositories around the globe, and insert into their own plug-and-play learning environments. Thus, instruction will be learner-centered, collaborative global learning, with learners engaged through technologies, and with teachers taking on the role of facilitators. Students will take increased responsibility for their own learning, defining their own learning agendas, using educational resources.

• Service-Oriented Institutions. The quality of services of distributed learning institutions will become a key competitive advantage for e-learning institutions. The services include at least three levels: pedagogical services, learning services, and infrastructure.

Nevertheless, most of today’s learning environments are focused mainly on infrastructure and neglect the personalized pedagogical services and learning services aspects of the institution. According to Jafari (2002):

Although new versions (of Course Management Systems, CMS for short) include easy-to-use Web authoring tools, most offer passive services. As a result, some instructors spend more time teaching a distance-learning course than teaching the same course in a classroom setting. This problem results mostly from the time-consuming operational nature of online courses. It is not unfair to call the typical CMS a “dumb software environment.”…We need smart learning environments that offer personal services with capabilities tolearn, reason, have autonomy, and be totally dynamic.

Some of the challenges associated with distributed learning include inadequate end-user quality of service (QoS), inadequate materials, lack of interactivity, and shortcomings in learning paradigms (Vouk et al., 1999). For example, in most existing Web-based distributed-learning systems, course materials are arranged by the course authors in order to cover one or more topics and convert them in interactive linked HTML pages. The course materials are then placed online to make them downloadable or visible to the students. The students can use them only by following the path established. Besides, as the instructors and tutors are not always available online, the need for assistance and interactions between students is particularly salient. Research shows that in terms of course design, development, and maintenance, as the number of online learners increases, serious problems in balancing cost, quality, and efficiency in course development and maintenance emerge.

Interactivity is another key issue in distance education. Web-based educational platforms available on the market, such as WebCT, LearningSpace, Top Class, ToolBookII, or Web Course in a box, provide different Webbased educational environments, each with its specific features. Nevertheless, they all share some underlying common features, as the aim is the same for all: to deliver learning content through a Web environment. Students’ interest in the learning process is drastically reduced when the level of interactivity is low.

To overcome these challenges or deficiencies in distributed learning, the authors of the chapters in this collection will discuss emerging technologies and develop models and tools to automate or semiautomate educational services.

Due to its inherently distributed nature, a distributed-learning environment can be supported and managed by a set of autonomously cooperating software agents that communicate intelligently with one another. They can interact with human users at the right time, with the right information. Much experimental research has shown that intelligent software agents, such as interface agents, information agents, and collaborative agents, have great potential to reduce information workload and to automatically perform many knowledge- and labor-intensive tasks for users. For example, these agents, with the functions of motivation, learning facilitation and collaboration, and so forth, serve asstudents’ assistants, companions, and tutors. Implementing agent-supported distributed learning has become a strategy of many institutions striving for success in basic education, higher education, continuing education, and training. They have incorporated intelligent agents into a wide range of educational systems, from intelligent tutoring systems (ITSs) to computed-supported collaborative learning (CSCL) environments.

The agent-oriented approach has been used for solving the complexity of distributed-learning environments and enhancing the functionality of WBE systems.

The use of agents as an abstraction tool, or a metaphor, for the design and construction of systems provided the initial impetus for developments in the field. On the one hand, agents offered an appropriate way to consider complex systems with multiple distinct and independent components. On the other hand, they also enabled the aggregation of different functionalities that have previously been distinct (such as planning, learning, coordination, etc.) in a conceptually embodied and situated whole. Thus, these notions provide a set of technology areas that relate directly to these abstractions in the design and development of large systems, of individual agents, of ways in which agents may interact to support these concepts, and in the consideration of societal or macrolevel issues, such as organizations and their computational counterparts. Current efforts span diverse areas including agent-oriented software engineering, agent architecture, mobile agent systems, agent infrastructure, and electronic institutions (Luck et al., 2003).

Furthermore, in a distributed collaborative learning environment where users are geographically distributed and collaborate through a Web-based learning environment, an agent can facilitate collaboration processes, such as participation, coordination, teacher intervention, and group interaction.

Recently, several strands of research in artificial intelligence (AI), Web languages and technologies, and multiagent systems are being brought together and unified in exciting and interesting ways. This development makes Webbased distributed learning more practical and attractive by deploying agents supporting the users and computer programs of distributed-learning systems.

Multiple agent systems (MASs) improve the education system by making it viable in open knowledge domains, where ITSs were earlier applied with less success. Moreover, the MAS architectures have great potential to solve many of the current problems of (Web-based) distributed-learning systems. MAS can be used to adapt and personalize the distributed-learning environment by using machine learning tools and can simulate an expert when performing pedagogical tasks.

Challenges and Opportunities

At present, educational agents exist in academic and commercial laboratories but are not widely available in real-world applications. To make the move from the laboratories to real-world applications happen, we need to solve a number of technological issues for research and development:

First, if agent technologies are to be effective, software engineering issues need to be carefully considered:

• How can multiagent architectures designed for maximum effect?

• Can such architectures be used effectively to support and enhance existing work practices?

• What kinds of agents and MASs are effective?

• How can such systems be designed to successfully complement people’s existing practices and preferences?

• On which conceptual design approach should the agents be based?

• How should we design the functionality and human-agent interaction in distributed-learning environments?

• How should we design an experimental study to assess the impact of pedagogical agents on these environments?

Second, we need to increase the quality of agent software to industrial standards and provide effective agent standards to allow open system development.

Third, in addition to standard language and interaction protocols, agent societies for distributed learning will require the ability to collectively evolve language and protocols specific to distributed-learning applications and to the agents involved.

Fourth, we need to have a greater understanding of how agents for distributed learning and educational resource information systems interact.

Fifth, we need Web standards that enable structural and semantic description of information access at a higher level.

Sixth, we need to create common ontologies, thesauri, and knowledge bases, formally describe information, and potentially have a reference architecture to support the higher-level services.

Seventh, we need to develop agents’ ability to understand learners’ and educators’ requirements and to adapt to changes in distributed learning environments.

Finally, we need to ensure confidence and trust in agents. A user must have confidence that an agent or agent system, which represents them within an open system, will act effectively on his or her behalf—it must be at least as effective as the user would be in similar circumstances. Moreover, agents must be secure and tamper-proof and must not reveal private information inappropriately. Besides, if a user is to trust the outcome of an open agent system, the user must have confidence that agents representing other parties or organizations will behave within certain constraints.

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