Personalization oflearning on the Web is currently one of the most important issues in the area of e-learning, It involves multiple paradigms, such as context, methodology, and so on. To support personalized learning on the Web requires the coordinated efforts from both teaching and learning. We incorporate the software agents with different expertise into the learning processes to assist individual learners in creation of personally learning experiences by the association of the agents with the Internet-related programs used by the teacher and the learner on the learning. In this paper the learning methods on the Web are explored and a model is then constructed to describe the Web based learning environment. An architectural framework of a multi-agent system to facilitate personalized learning on the Web is presented. After that the supports for personalized learning by the agents in the architecture are analyzed. At the final some conclusions are presented.

The rapid evolution and ubiquitous use of mobile devices is an historical opportunity to improve experiential interactivity in education practices to support "deep" learning. A major barrier to the widespread adoption of mLearning in higher education is that of cost. Usage charges and the cost of mobile hardware are key issues. Opportunities to overcome this barrier include the high rate of ownership of mobile phones by university students and technological solutions such as packet transmission technologies. The paper introduces mInteract, a system which uses packet technology (mobile WAP/WML) to build no-to-low cost interactivity into learning spaces. The online tool supports active experiential learning transactions for both student and teacher. In 2008 mInteract was trialled in a subject with large numbers. Focus group feedback is presented that indicates high levels of engagement with both users and non-users of the tool.

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In a review of the journal Simulations and Gaming (Sage Publications) we found less than 10% of all articles made reference to requirements of the person/s facilitating a simulation or game. It seemed that many writers did not regard facilitation as sufficiently important, or were unaware that its nature can be problematic. Until events necessitated critical re-analysis of our practice, we were similarly unconcerned about our own facilitation skills. Once we began examining facilitation processes, new insights into the facilitation role emerged. These insights especially concern the way in which personal preferences appear to have a major influence on choices and behaviours when facilitating experiential learning activities. Two sets of choices emerge as particularly relevant. The first concerns choices about the type of simulation or game chosen; the second concerns the preferred facilitation style and observable behaviours. We first wrote about these in 1998 (Leigh and Spindler 1998) and have continued our explorations in subsequent papers (Leigh 2003a, Leigh 2003b, Leigh and Spindler-in press). In this chapter we briefly describe our earlier work, and extend the proposition that personal teaching and learning philosophies often have a greater influence on choices and actions than requirements of specific educational outcomes. For example, given similar learning outcomes, someone who sees learning as a highly structured process requiring tight control is likely to choose a quite different approach to someone who regards learning as an emergent process dependent on interactions among learner, processes and content. ISAGA 2003 we used a collaborative research strategy to explore these propositions. We had developed instruments to assist in identifying philosophical stances, preferences for simulations and games formats, and facilitation practices. During the workshop participants were able to use these instruments to identify personal patterns among these factors. There was sufficient support for our propositions to encourage further research. Defining simulations and games When discussing definitions and types of simulations in use around the world it is easy to see that the choices are immensely varied. What 'are' and 'are not' simulations, how to manage, design, learn from and behave in simulations-are all subject to debate. While preparing this paper one of us was invited to complete two electronic surveys about the field. Neither site provided a definition of 'simulation', both apparently assuming that anyone completing the survey shared