Organization of This Book

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This book reports on the most recent important advances in agent technologies for distributed learning. It is organized into 10 chapters. A brief description of each chapter is provided below.

Chapter 1 introduces the design and implementation of a multiagent system based on a human collaborative online learning environment (COLE). Silva de Azevedo (Paran Federal Center for Technological Education, Brazil) and Scalabrin (Pontifical Catholic University of Paran , Brazil) discuss the concept of human collaboration and the ways that project-based learning (PBL) and portfolios can improve the social competencies of distributed learners. It presents the system analysis for agent systems (SAAS) method, a way for identifying services and agents.

Chapter 2 presents intelligent agents facilitating distributed collaborative learning. It covers agent design issues and implementation details. Chen and Wasson (University of Bergen, Norway) provide different support to users (including students and instructors). They have combined awareness information and advice, agent regulation, students’ self-regulation, and instructor regulation. The performances of these agents have been evaluated in various scenarios, both in asynchronous and synchronous collaborative environments. They received positive feedback from students and instructors.

Chapter 3 explores the challenges, issues, and solutions associated with satisfying requirements for privacy and trust in agent-supported distributed learning (ADL). Korba et al. (National Research Council of Canada) discuss an often-ignored area—that of building trustworthy user interfaces for distributed-learning systems.

Chapter 4 by Yang (National Research Council of Canada) first addresses the issue of the importance of intelligence in MAS-based distributed-learning environments (DLEs). Then it stresses that there are three main intelligent competencies in MAS-based DLEs: intelligent decision-making support, coordination and collaboration of the agents in MAS, and student modeling for personalization and adaptation in learning systems. It also describes in detail how to apply relevant AI techniques, including the introduction of AI techniques, their state-of-the-art application in the e-learning domain. Finally, future trends in the research and development of intelligence for MAS-based DLEs are discussed.

In Chapter 5, Chen and Ding (University of Houston, USA) first discuss how agent technology can be used in an educational system and then focus on how knowledge management techniques play an important role in agent-based tutoring systems.

In Chapter 6, Ally (Athabasca University, Canada) provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and style. He argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module.

In Chapter 7, Lin et al. (Athabasca University, Canada) discuss the concept of distributed-learning environments and the rationale for using intelligent software agents in such environments. Lin et al. propose a new approach to designing and developing adaptive distributed-learning environments by integrating Agent Technology and Web Services Technology.

In Chapter 8, Esmahi and Lin (Athabasca University, Canada) describe a multiagent system for delivering adaptive e-learning and provide a discussion on three issues related to personalization in e-learning: technology advancement and the shift in perception of the learning process, one-size-fits-all versus personalized services, and the adaptation process. Finally the authors provide an overview of most known implemented systems for adaptive e-learning, as well as a detailed description of the architecture and components of the proposed multiagent framework.

Chapter 9 by Lin (University of Houston–Downtown, USA) introduces the Gamma language as a formal language for the specification of agent systems. Through case studies of various agents for distributed learning, Lin demonstrates the feasibility and benefits of using Gamma as a specification language for multiagent systems, in light of how architectural design can be streamlined succinctly. A case study is also done in specifying a multiagent-based e-learning system for course material maintenance.

In Chapter 10, Shih et al. (Tamkang University, Taiwan) present the preliminary results of an ongoing distance-learning research project—developing a system based on virtual reality (VR) technology and agent technology, which enables online discussions via different real-time communication channels. The system has a generic interface, which includes five scenes of a virtual university, as well as a set of plug-and-play communication agent tools. An intelligent agent maintains each user. Shih et al. implemented a generic user inter- face as well as a state machine engine, which runs a specification language for the system.

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