Research interests

Nowadays, technology-enhanced learning (TEL) systems must have the capability to reuse learning resources and web services from large repositories, to take into account the context and to allow dynamic adaptation to different learners based on substantial advances in pedagogical theories and knowledge models (Balacheff, 2006). This is particularly true of mobile learning, where context is variable. The reuse of learning resources and web services requires interoperability at a semantic level. In other words, it is necessary to have a semantic web approach to design TEL systems. Moreover, knowledge models and pedagogical theories can be fully represented by means of a semantic web approach. In the mobile learning area, a number of terms are commonly used; mobile, pervasive and ubiquitous learning systems (Brodersen, Christensen, Gronboek, Dindler, & Sundararajah, 2005; Hundebol & Helms, 2006; Sharples, 2005; Thomas, 2007). In computer science, mobile computing is mainly about increasing our capability to physically move computing tools and services with us. The computer becomes an ever-present device that expands our capabilities by reducing the device size and/or by providing access to computing capacity over the network (Lyytinen & Yoo, 2002). In mobile computing, an important limitation is that the computing model does not change while we move. This is because the device cannot obtain information about the context in which the computing takes place and adjust it accordingly. In pervasive computing, the computer has the capability to inquire, detect and explore its environment to obtain information and to dynamically build environment models. This process is reciprocal: the environment also does it and becomes “intelligent”. In ubiquitous computing, the main goal is to integrate large-scale mobility with pervasive computing functionalities.

In our work, we consider that mobile, pervasive and ubiquitous learning systems have the properties of mobile, pervasive and ubiquitous computing systems respectively. We focus our attention on pervasive learning systems. Mobile learning is not just about learning at anytime, at any place and in any form using lightweight devices, but learning in context and across contexts. It is best viewed as providing mediating tools in the learning process (Sharples, 2006). Many definitions of pervasive learning are given in the literature (Bomsdorf, 2005; Hundebol & Helms, 2006; Jones & Jo, 2004; Thomas, 2007). One useful definition is that a “pervasive learning environment is a context (or state) for mediating learning in a physical environment enriched with additional site-specific and situation dependent elements – be it plain data, graphics, information -, knowledge -, and learning objects, or, ultimately, audio-visually enhanced virtual layers” (Hundebol & Helms, 2006). One could consider pervasive learning as an extension to mobile learning where the roles of the intelligent environment and of the context are emphasized (Laine & Joy, 2008). In pervasive learning, computers can obtain information about the context of learning from the learning environment where small devices, sensors, pads, badges, large LCD screens, people, and so on, are embedded and communicate mutually. The physical environment is directly related to learning goals and activities. The learning system is dynamically adapted to the learning context. Consequently, a pervasive learning system needs to have an appropriate software architecture to support these features.

In the workplace context, learning can occur in purposeful situations in which there is an explicit goal to learn as well as in incidental situations in which there is no explicit learning goal or interest. Working involves an activity or a related set of activities that require effort and are aimed at achieving one or more objectives. Learning emphasizes what a learner knows or is able to do, while, in contrast, working is related to performance improvement (Michael-Spector & Wang, 2002). In other words, when performing a work task, it often happens that learning also occurs. The performance and quality of work may also be enhanced following learning experiences. Working activities are mainly about solving problems, and in knowledge-intensive organizations this implies continuous learning. Carrying out the particular working task is the priority; learning is just a means (Farmer, Lindstaedt, Droschl, & Luttenberger, 2004). The distinction between learning and working activity is blurring, working being a way of learning, and vice versa. Simon (2007) asserts that traditional methodologies such as formal classroom teaching and even Internet based, content oriented courses and programs have their place at the worksite. Nevertheless, these approaches are generally inflexible to the demands of contextualised, learner centred, performance related challenges (Simon, 2007). Thus, learning processes need to be embedded in organizations, so that learning becomes pervasive and a natural part of work. A particular architecture is required to facilitate the redefinition of learning to mean a work activity and to provide an infrastructure for seamless work-learning integration (Simon, 2007). In such framework, situated learning can be used, where the location, time, environment and tasks, etc. are taken into account. It provides the right learning support at the right time according to the situation parameters and to the goals in the working context. Situated learning increases the quality of learning and is attractive for learning at the workplace and for work-learning integration (Oppermann & Specht, 2006).

In the p-LearNet project, a pervasive learning system aims to integrate context-aware corporate learning and working activities within the e-retail framework (retail activities through shops and hypermarkets). In such a framework, we are interested in the following learning issues: the combination of formal learning (formal classroom at the workplace, etc.) and work-learning integration, integration of mobile devices in broader lifelong learning and working scenarios, learning in context, seamless learning across different contexts and context-as-construct (Balacheff, 2006; Sharples, 2006; Vavoula & Sharples, 2008). In such a framework, we focus on a scenario-based approach for TEL system design. Scenarios are used to describe the learning, working and tutoring activities to acquire some domain knowledge and know-how, solve a particular problem or support working activities. Scenario analysis reveals that learning and working situations can be modeled by an explicit hierarchical task model because working and learning activities are well structured and stable. In pervasive learning systems, activities, represented by tasks, can be achieved in different ways according to the current situation. Methods associated with tasks enable us to provide different ways to carrying out those tasks. Activities need to have access to supporting resources or web services. Thus, a context-aware and adaptive mechanism is necessary to select relevant methods associated with a task and their corresponding resources and web services. For a particular couple (Task, Method), resources and web services may also be selected according to the current situation.

In pervasive computing, the computing device has to seamlessly and flexibly obtain information about its environment in which the computing takes place and to adjust itself accordingly. From a software architecture viewpoint, a pervasive learning system has to be flexible enough to reuse learning components (learning resources or learning web services) which are not known in advance and discovered on the fly. A service oriented architecture (SOA) approach facilitates the deployment of an adaptive learning environment based on the aggregation and orchestration of the services needed by an organization. This approach can be effective for pervasive learning systems if one provides for continuous adaptation based on the available services and other contextual information.

Our work is to propose an adaptive and context-aware model of scenarios for a pervasive learning system supporting working and learning activities. The pervasive learning system architecture is based on a service oriented architecture to meet pervasive computing requirements. Web services are retrieved and orchestrated, and can be used for different working and learning activities. Thus, the scenario model can invoke web services to undertake activities. The scenario model and the web service retrieval and orchestration are based on a semantic web approach which enables us to represent the explicit common knowledge of the communities of practice involved in the p-LearNet project. The scenario model is based on a hierarchical task model having the task/method paradigm. An activity, represented by a task, may have several associated methods. A method represents a way of performing a task in a particular situation. The context-aware and adaptive mechanism can be viewed as the selection of the relevant content (methods or web services) for a given task according to the current working and/or learning situation. This mechanism is based on matching content description to the current situation for filtering, annotation and ranking. Content and situation need to have corresponding features for adaptation purposes. Methods are described by contextual features while web services are described by metadata. Situations are described according to a context model. For managing web services, we also define the service requirement specification for web service retrieval. Moreover, a pervasive learning system architecture is proposed to facilitate its design and execution in a workplace environment, based on Open Services Gateway initiatives (OSGi) and Universal Plug and Play (UPnP). SOA enables us to design an architecture that is able to inquire, detect and explore its environment to obtain information and to dynamically build environment models. As web services are closely related to learning needs by means of the scenario model, we can provide the right learning support according to the current situation and deal with pervasive computing issues.