Towards a Small-Scale Model for Ubiquitous Learning

Towards a Small-Scale Model for Ubiquitous Learning

Jorge Luis Victória Barbosa (University of Vale do Rio dos Sinos (UNISINOS), Brazil) and Débora Nice Ferrari Barbosa (Feevale University, Brazil)
DOI: 10.4018/978-1-4666-4542-4.ch004
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Abstract

The ever-increasing use of mobile devices allied to the widespread adoption of wireless network technology has greatly stimulated mobile and ubiquitous computing research. The adoption of mobile technology enables improvement to several application areas, such as education. New pedagogical opportunities can be created through the use of location systems and context-aware computing technology to track each learner's location and customize his/her learning process. In this chapter, the authors discuss a ubiquitous learning model called LOCAL (Location and Context Aware Learning). LOCAL was created to explore those aforementioned pedagogical opportunities, leveraging location technology and context management in order to support ubiquitous learning and facilitate collaboration among learners. This model was conceived for small-scale learning spaces, but can be extended in order to be applied to a large-scale environment. Initial results were obtained in a real scenario, attesting the viability of the approach.
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Introduction

In recent years, there has been a continued research effort in the field of mobility in distributed computer systems. This is mainly due the widespread availability of portable electronic devices (such as mobile phones, handheld computers and netbooks) and of interconnection technologies based on wireless communication (like bluetooth and Wi-Fi). This mobile and distributed paradigm is called Mobile Computing (Satyanarayanan, 1996; Satyanarayanan et al., 2009; Diaz, Merino & Rivas, 2009). Moreover, the diffusion of wireless communication technologies enables mobile devices to provide computational services in specific contexts (Context-aware Computing (Abowd, et al., 1999; Baldauf, Dustdar & Rosenberg, 2007; Hoareau & Satoh, 2009)). Adaptation related research brought the possibility of continuous computational support, anytime, anywhere (Ubiquitous Computing (Satyanarayanan, 2001; Weiser, 1991)). In turn, Location Systems (Hightower & Borriello, 2001) are enabling the use of this kind of computing in accordance with the physical location of users.

Location, as was shown by Hightower and Gaetano (2001), is an important topic related to mobile and ubiquitous computing. Hightower, LaMarca and Smith (2006) demonstrate that the precision obtainable in today's location methods (such as A-GPS and cellphone antenna triangulation) already allows for the implementation of commercial applications. Moreover, the widespread adoption of wireless hotspots suggests that in the near future this precision will only grow, giving way to Location Based Services (Vaughan-Nichols, 2009; Dey et al., 2010).

Based on today's technology, we can imagine a scenario where society would be permeated by mobile devices, always connected to a communication network, and with precise location data always available. The data would be used in order to provide customized services, depending on the physical location, context and the needs of each particular application. In this scenario, ubiquitous computing would be greatly stimulated, as precise location methods would always be available. This will cause a significant impact in education (Ubiquitous Learning (Barbosa et al., 2006; Ogata & Yano, 2009)).

To take advantage of ubiquitous computing, a new educational model should finally arise. This model should allow the construction of learning programs related to dynamic information obtainable from the learners' own physical context, establishing links between contexts and pedagogical goals and resources. This will be a key element to facilitate collaboration among learners with related interests. Towards this scenario, several approaches are being researched, like (Ogata & Yano, 2009; Barbosa, Geyer & Barbosa, 2005; Yau et al., 2003).

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