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In recent years, studies about mobility in distributed systems have been stimulated by the proliferation of portable electronic devices (for example, cell phones, handheld computers, tablet PCs and notebooks) and the use of interconnection technologies based on wireless communication (such as WiMAX, WiFi, and bluetooth). This mobile and distributed paradigm is called Mobile Computing (Diaz, Merino, & Rivas, 2009; Satyanarayanan et al., 2009). Moreover, the improvement and proliferation of Location Systems (Hightower & Borriello, 2001; Hightower & Smith, 2006) have motivated the adoption of solutions that consider the user’s precise location for the provision of services, Location-Based Services (Dey et al., 2010; Vaughan-Nichols, 2009).
The adoption of these technologies combined with the diffusion of sensors enabled the availability of computational services in specific contexts – Context-aware Computing (Baldauf, Dustdar, & Rosenberg, 2007; Dey, 2001; Hoareau & Satoh, 2009). The idea consists in the perception of characteristics related to the users and their surroundings. These characteristics are normally referred to as context, i.e., any information that can be used to describe the circumstances concerning an entity. Based on perceived context, the application can modify its behavior. This process, in which software modifies itself according to sensed data, is named Adaptation (Satyanarayanan, 2001). In this scenario, the Ubiquitous Computing initially introduced by Abowd and Mynatt (2000), Satyanarayanan (2001), and Weiser (1991) is becoming reality.
The application of mobile and ubiquitous computing in the improvement of education strategies has created two research fronts called Mobile Learning and Ubiquitous Learning. Mobile learning (m-learning) (Tatar, 2003) is fundamentally about increasing learners' capability to carry their own learning environment along with them. M-learning is the natural evolution of e-learning, and has the potential to make learning even more widely accessible. However, considering the ubiquitous view, mobile computers are still not embedded in the learners' surrounding environment, and as such they cannot seamlessly obtain contextual information.
On the other hand, Ubiquitous Learning (Barbosa et al., 2007; Lewis et al., 2010; Ogata & Yano, 2009; Ogata et al., 2010; Rogers et al., 2005; Yin, Ogata, & Yano, 2004; Yin et al., 2010) refers to learning supported by the use of mobile and wireless communication technologies, sensors and location/tracking mechanisms, that work together to integrate learners with their environment. Ubiquitous learning environments connect virtual and real objects, people and events, in order to support a continuous, contextual and meaningful learning. A ubiquitous learning system can use embedded devices that communicate mutually to explore the context, and dynamically build models of their environments. It is considered that while the learner is moving with his/her mobile device, the system dynamically supports his/her learning by communicating with embedded computers in the environment. The opportunities made available by the context can be used to improve the learning experience.
This learning scenario is attractive, but is not easily implemented. We are investigating how to better match people's expectations for such a system. In our point of view, ubiquitous learning environments should support the execution of context-aware, distributed, mobile, pervasive and adaptive learning applications.
GlobalEdu is a model created to support ubiquitous learning. The model is structured into layers and can also be coupled with a ubiquitous computing middleware. This article proposes the GlobalEdu Model and its integration with two ubiquitous middleware projects: ISAM (Augustin et al., 2004) and LOCAL (Barbosa et al., 2007).