207 Nuevas Ideas en Informática Educativa, TISE 2011 Towards context-aware and mobile e-learning applications Windson Carvalho Universidade Federal de Ceará, Brasil windcarvalho@gmail.com Márcio Maia Universidade Federal de Ceará, Brasil marcioefmaia@gmail.com Rossana Andrade Universidade Federal de Ceará, Brasil rossana.de@terra.com.br José Valdeni de Lima Universidade Federal de Rio Grande do Sul, Brasil valdeni@inf.ufrgs.br José Celso Freire Junior UNESP, Brasil jose.celso.freire@gmail.com Edgar Marcal Universidade Federal de Ceará, Brasil edgarmarcal@gmail.com Jérôme Gensel Laboratoire d'Informatique de Grenoble, Francia Jerome.Gensel@imag.fr Jaime Sánchez Universidad de Chile, Brasil jsanchez@dcc.uchile.cl ABSTRACT This papers introduces the Learning While Moving (LWM) project, discusses some mobile and ubiquitous learning applications, and propose to extend previous models and even create a new context model for the domain of u-learning applications. KEYWORDS Context-awareness, ubiquitous learning, mobile learning. INTRODUCTION In this decade, the progress of miniaturization of computational devices (e.g., sensors and mobile phones), and the increasing availability of wireless communication resources, seem to bring us towards a real ubiquitous computing world similar to the one described by Weiser et al. [1]. In Weiser’s vision, the computational devices are embedded in a ubiquitous environment allowing users to access information naturally, anywhere, anytime, and with multiple types of interaction (e.g., vision, voice). Then, an important characteristic of such a ubiquitous environment is referred to as context-awareness. Dey and Abowd [2] define the notion of context as “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant for the interaction between a user and an application, including the user and the applications themselves”. In other words, a context-aware system captures and makes use of user’s context information in order to adapt its behavior or to provide personalized content and services accordingly. Not surprisingly, the progress made in mobile and ubiquitous technologies has also had an impact in the field of e-learning. Thus, research issues have progressed from web-based learning to mobile learning (m-learning) [3][4]. Real mobile learning case studies, such as those described by Crawford [5], have shown that the introduction of mobile computing in the classroom promotes the motivation to learn, the collaboration and the communication between students. The integration of context-awareness and sensor-based technologies into m-learning systems increases the possibilities of interaction between the students, the objects of the real world, content of the lecture itself. In this new e-learning sub- domain, called ubiquitous learning (u-learning), the learning system is able to capture the situational context of the students and to guide them in their learning activities with adapted digital support [6]. Although researchers have recognized the great potential of context-aware u-learning, only few practical applications have been implemented. This occurs mainly due the insufficient experience on the development of context-aware u-learning environments and the designing of learning digital lectures that can take benefits from context-awareness. In this project, we intend to assist the development of this kind of systems by proposing a ubiquitous learning environment and a reuse-oriented software process. Together, they should facilitate the developments of these systems. The main goal of the Learning While Moving (LWM) project is to gather and merge expertise from all partners in order to construct a Ubiquitous Learning Environment (ULE) that makes easier the creation of u-learning lectures. Figure 1 shows an overview of this proposal. U-learning is an approach that places the student in a series of designed lessons that combine both real and virtual learning environments. Hence, the ULE system should be able to acquire precise contextual information about the student, specially, location and spatial relationships with real world objects. The acquired context will be used by a Lecture Adapter entity that will change the course according to the pedagogical workflow defined by the teacher combined with the gathered contextual information.