A New Cloud-Based Adaptive Mobile Learning System Using Multi-Agent Paradigm

Background

Several studies in the literature have implemented an M-learning system based on multi-agent architectures such as (Henry & Sankaranarayanan, 2010; Sun et al., 2005; Chi-Wing et al. 2001; Meere et al. 2010; Esmahi & Badidi, 2004; Glavinic et al. 2007). Wang et al. (2008) used intelligent agents to adjust learning content dynamically according to the learning progression. In addition to intelligent agents and multi-agent systems, mobile agent technology has opened up a new interesting research area in the Mobile Learning environment (Wang et al., 2008). Esmahi & Badidi, (2004) proposed a multi-agent system to provide adaptive M-learning services by the adapting learning tasks and personalization of course content based on learner’s model, learning style, and strategy. The authors used both stationary and mobile agents to deliver M-learning services to users. Their approach has the advantage of being more flexible and scalable, as mobile agents offer dynamic adaptation of either the course content or the interface (Esmahi & Badidi, 2004).