Improving Mobile Learning Environments by Applying Mobile Agents Technology

Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Corporate Author
Commonwealth of Learning (COL)

PCF3 // Availability of high bandwidth infrastructure such as GPRS, 3G and UMTS networks, advances in wireless technologies (Chen and Nahrstedt, 2000; Chiang, et al., 1998; Johnson and Maltz, 1996; Chen and Lai, 2000; Lin and Liu, 1999) and popularity of handheld devices (Microsoft, 2001) provide a new way for education by extending the learning time and space. One important emerging application of handheld devices is mobile learning (Sharples, 2000). With the new paradigm “anytime, anywhere computing”, e-learning has been extending to mobile learning (m-learning) (Lehner and Nösekabel, 2002). // In recent years, numerous efforts have been making in the direction of using handheld devices for educational purposes (m-learning project, 2003; MOBIlearn project, 2003; Chen, et al., 2002; Chang and Sheu, 2002; Becta Report, 2003; Megan Fox, 2003). However, while mobile devices are approaching ubiquity today, the mlearning industry is still in its infancy. The current research base is insufficient to wholly explore the value of wireless internet learning devices (Roschelle, et al., 2002). The m-learning has a number of common deficiencies, such as access to course materials is slow; courseware does not adapt to individual students; the real time interaction between student and system is hard to achieve because of the connection unreliability and bandwidth limitations. // Intelligent Agents are one of the ʺhotʺ topics in Information Systems Research and Development at the moment. The last ten years have seen a marked interest in agentoriented technology and a distinct trend has evolved to the research and development work on intelligent agents. This trend relates to the diversification in the types of agents being investigated and most popular types include user interface agents, information agents, multi agent system, and mobile agent and so on. Intelligent agents, and in particular, mobile agents (Cardelli, 1995; White, 1996; Gray, 1997; Aglet system, 2003; Bee-gent and Plangent, 2003; Minar, 2000), have huge potential to address those deficiencies. In this paper, we shall demonstrate how mobile agents can address the problems that limit the potential mobile learning environment development.

New Zealand