Ebook: The Learning Grid Handbook

Grid technologies are rising as the next generation of Internet by defining a powerful computing paradigm by analogy with the electric Power Grid. A Grid user is able to use his private workplace to invoke applications from a remote system, use the system best suited for executing that application, access data securely and consistently from remote sites, exploit multiple systems to complete economically complex tasks or to solve large problems that exceed the capacity of a single system.

Grid could be used as a technology “glue” providing users with a uniform way to access resources by means of several devices. These technologies can provide, in a natural way, a support for Technology Enhanced Learning (TEL) by enabling new learning environments based on collaboration, real direct experience, personalisation, ubiquity, accessibility and contextualisation.

Nevertheless, to be effectively used in TEL, Grid must be complemented with other elements like semantics and educational modelling leading to the concept of “Learning Grid” as defined in the homonymous Special Interest Group (SIG) of the European Network of Excellence “Kaleidoscope: Shaping the Scientific Evolution of Technology Enhanced Learning”.

The key challenge that Kaleidoscope is facing is the scientific and structural integration of the European TEL research. In this context, the Learning Grid SIG aims at contributing to the achievement of an improvement in TEL practices through the definition of open, distributed and pervasive environments for effective human learning, taking into account that effective learning requires an active attitude from learners and that learning is a social and collaborative activity requiring a technology that allows for active and realistic experiments, personalization, knowledge creation and evolution, as well as autonomous and dynamic creation of communities.

The research program of the Learning Grid SIG encompasses the definition of challenging scenarios for distributed service-oriented TEL, the analysis of technologies for creating distributed service-oriented TEL environments as well as the analysis of languages and frameworks for the dynamic composition of distributed TEL resources and services. Starting from that, the purpose of this book is to update, harmonize and consolidate obtained research results.

The first section of the book is about the concept of Learning Grid and related technologies. The first chapter gives a formal definition of the Learning Grid as an enabling architecture based on Grid, Semantics and Educational Modelling allowing the definition and the execution of TEL experiences obtained as cooperation and composition of distributed heterogeneous actors, resources and services. This chapter also presents an abstract architecture for the Learning Grid, a set of suitable learning approaches and some related research work.

The second chapter analyses and compares existing languages for the dynamic composition of distributed learning resources and services in a Learning Grid. It is worth noting that the application of such languages is important where heterogeneous services have to be dynamically composed according to learners' needs and preferences and to teacher defined learning methods and strategies. Based on that, the third chapter gives an overview of the main approaches to tackle the problems of semantic description and matchmaking of Learning Grid Services.

The fourth chapter concludes the first section with the definition of challenging application scenarios in the TEL domain that can be approached with a Learning Grid. The purpose is to understand and argue about the potential advantages of adopting a Learning Grid and to show why its features are paramount to improve personalisation and knowledge construction in the learning process as well as communication and collaboration inside learning groups.

The second section of the book is purposed to share research results obtained by European research groups connected with the Learning Grid SIG in their own projects on the field.

Chapter 5 is centred on some of the results of the ELeGI project: after having presented a theoretical learning model which allows to generate personalised learning experiences and to dynamically adapt them during the learning process, a Learning Grid software architecture created modifying the Intelligent Web Teacher (IWT) innovative learning platform and conceived for supporting the execution of the processes defined in the learning model, is described.

Chapter 6 presents an overview of Gridcole, a grid service-based tailorable collaborative TEL system enabling the integration of tools requiring supercomputing capabilities or specific hardware resources in order to support collaborative learning situations in which these types of tools are required and, at the same time, able to interpret collaboration scripts created by educators in order to guide participants during the realization of collaborative learning situations.

Chapter 7 presents a grid-based Virtual Observatory built in the AstroGrid project and explores how such a grid can be used as an e-learning platform for a wider audience. Moreover, in order to facilitate teacher interaction with remote students and to increase the interactivity level of the virtual classroom, the integration of Augmented Reality applications is also discussed.

Chapter 8, starting from the consideration that mobile and grid technologies provide complementary features that can address the requirements of next generation e-learning applications, presents some of the results of the Akogrimo project designed to build a platform based on both mobile and grid technologies and, in particular, discusses an m-learning scenario built on such platform.

Chapter 9 presents L4All: a system, coming from the homonymous project, able to record and share learning trails through educational offerings with the aim of facilitating progression of lifelong learners from Secondary Education, through to Further Education and on to Higher Education. The focus of the chapter is on the description of the service-oriented architecture of the system enabling a comprehensive set of educational activities.

Chapter 10 presents some results of the SeLeNe project which investigated the feasibility and designed tools to support learning communities (self e-learning networks) by matching learners' needs with educational resources potentially available on the Web. The focus is on the description of the personalisation services designed and developed using a combination of Web Service and Semantic Web technologies.

Chapter 11 presents a grid-based approach to capture and structure the information generated by collaborative learning activities in order to extract relevant knowledge and to provide learners and tutors with efficient awareness and feedback as regards group performance and collaboration. The authors demonstrate how a grid-based approach can considerably decrease the time of processing group activity log files and thus allow group learners to receive selected knowledge even in real time.

Chapter 12 presents a Virtual Scientific Experiment framework designed and developed on top of a grid infrastructure for running interactive virtual laboratory experiments for distributed student communities with visualization capabilities. The architecture is based on Web Services standard protocols such as WSDL and WS-Notification as implemented in the WSRF specification. The chapter also presents system evaluation results from a distributed pool of students showing the added value of the framework in enhancing distance-learning programs and Virtual Classes with extensible collaborative and interactive Virtual Laboratories sessions.

Chapter 13 presents ActiveMath, a Web-based TEL environment for mathematics, and discusses it from service and semantics perspectives. The service-oriented architecture of the system is described and how component services interplay with the domain and the pedagogical semantics used is shown. Details on the knowledge representation for mathematics are provided and how it is used to represent learning material and learning goals is explained as well as how a course generator assembles sequences of learning objects to fulfil these goals.

Saverio Salerno, Dept. of Information Engineering and Applied Mathematics, University of Salerno

Over the last few years, Technology Enhanced Learning (TEL) needs have been changing in accordance with ever more complex pedagogical models as well as with technological evolution, demanding for high dynamic and configurable environments for running multiple teaching and learning scenarios. Grid technologies have started to be very popular even in education due to the advantages that they offer being based on a secure, flexible and coordinated way of sharing resources over Internet as well as on its enormous capability of information processing. A Grid may facilitate learning processes in allowing each learner to collaboratively use the resources already existing online, by facilitating and managing dynamic communication with other people and agents, through the implementation of dynamic Virtual Organizations allowing to share learning resources. Nevertheless, in order to be effectively used in TEL, Grid must be complemented with other technologies bringing to the concept of “Learning Grid” whose description is the object of this chapter.

This chapter analyses existing languages for the dynamic composition of distributed resources with particular emphasis on e-learning objects and services that may be exploited in a Learning Grid. The application of such languages has a paramount importance where heterogeneous services are distributed on the Grid and have to be dynamically composed according to learners needs and preferences and to teacher defined learning methods and strategies. The paper first explores education modelling languages, i.e. languages thought to manage workflows of learning activities (so learning-oriented but not specifically service-oriented); then it deepens services composition languages, i.e. languages thought for static or dynamic composition of Web Services of any nature (so service-oriented but not specifically learning-oriented). In both cases, the description of languages and related tools is followed by a comparison and by the definition of extensions needed in order to be fully exploitable in a Learning Grid environment.

Learning Grid services are the fundamental component of learning systems based on Grid Technology and represent functionalities that can be easily reused without knowing the details of how services have been implemented. Semantic Web Service technology promises to automate web service discovery, composition and integration, tasks that currently need to be performed manually despite the quickly increasing number of online service. A number of approaches have been proposed to tackle the problems of semantic description and matchmaking of Learning Grid Services. This article gives an overview of those recent research efforts.

This chapter analyses the adoption of the Learning Grid for the development of challenging Application Scenarios in the eLearning domain. The Application Scenarios described in this chapter create a breakthrough in current learning practices. Instead of adopting a traditional information transfer paradigm, the proposed scenarios, in fact, promote and support a learning paradigm centred on the learner and focused on knowledge construction using experiential based and collaborative learning approaches in a contextualised, personalised and ubiquitous way. The purpose of our analysis is to understand and argue about the potential advantages of adopting the Learning Grid for the proposed scenarios. Preliminary findings show that Learning Grid can be considered an enabling technology for the presented scenarios since its features (e.g. dynamicity, adaptiveness, support for Virtual Organisation creation and management, advanced mechanisms for resources and services discovery on the basis of Quality of Services) are key to improve personalisation and knowledge construction in the learning process as well as communication and collaboration inside learning groups.

This paper is centred on the main results of the ELeGI Integrated Project with respect to formal learning approaches. The ELeGI project provided the infrastructure for convergence of the technological solutions and the pedagogical models well defined in literature. The paper first presents the theoretical learning model which allows to automatically generate a personalised Unit of Learning and to dynamically adapt it during the learning process according to the learner's behaviour, preferences and learning styles. The ELeGI formal Learning Model drives the design of the ELeGI formal learning software architecture, conceived for supporting the execution of the processes defined in the learning model. The design methodology followed results as an interesting case from the software engineering point of view. A prototype of the ELeGI formal architecture is the ELeGI formal learning software platform. The most important one is IWT-GA that is the re-engineered version of the commercial e-learning solution Intelligent Web Teacher (IWT) integrated with the GRASP middleware, a service oriented Grid middleware.

Gridcole is a grid service-based tailorable collaborative learning system that provides two significant features. First, it enables the possibility of integrating tools that require supercomputing capabilities or specific hardware resources in order to support the collaborative learning situations in which this type of tools is required. Furthermore, it can interpret collaboration scripts created by educators in order to guide participants during the realization of collaborative learning situations. This chapter provides an overview of the system and describes a sample collaborative learning situation that can be supported by Gridcole with the purpose of illustrating its capabilities.

Developing e-Learning applications concerns issues of human communication and facilitating technology. One of the key research issues in Computer-Mediated Communication (CMC) is the participation of remote audience in a communicative activity. This is particularly important in learning contexts. In the two studies reported here, we discuss systems infrastructure by presenting the concept of the Virtual Observatory through the AstroGrid project. We explore how such a Grid can be used in the future as an e-Learning service platform and as a tool for wider audiences that require access to documents and similar information resources. However, an integrated e-Learning environment has to provide access to people (teachers and students) as well. In order to explore how the two kinds of facility may be integrated, we discuss the design of communication tools that provide access to both people and information. We also present Augmented Reality (AR) applications that facilitate teacher interaction with remote student audience by increasing the interactivity of the virtual classroom. Studies of virtual classrooms have identified limitations of computer-mediated learning environments since they do not provide sufficient contextual information to support communication. The virtual information space is critically dependent on the visualization aspects of the user interface. This has been designed with additional functionality to enable the lecturer to navigate the remote information space.

The next generation of e-Learning will be represented by mobile learning (mLearning). This consideration is justified by the observation that the next dimension of learning and training is the development of wireless communication and wireless learning in our society over the coming years. Future is becoming ever more wireless and at the eLearning domain there is an increasing effort to put in place wireless solutions to replace the wired computer scenarios of today's eLearning. At the same time, there is a significant effort to introduce an enhanced learning approach, based on a paradigm that focuses on the learner and on new forms of learning: the learner will have an active and central role in the learning process and the learning activities will be aimed at facilitating the learner's construction of knowledge and skills. Grid technologies can have a leading role in this approach since they are able to provide, in the next generation, the suitable features to achieve this vision. Mobile and Grid Technologies provide complementary features that can address the requirements of next generation eLearning applications and therefore the merging of both Technologies is a relevant challenge to be accomplished. The result of Akogrimo project will be a platform based on both mobile and Grid technologies. This platform will be able to support different application domains and it will be validated through an eLearning application as well. This paper describes a hypothetical mLearning scenario and how Akogrimo will support it [1].

L4All is a system that records and shares learning trails through educational offerings with the aim of facilitating progression of lifelong learners from Secondary Education, through to Further Education and on to Higher Education (HE). The focus is on helping those post-16 learners who have traditionally not participated in HE. L4All allows learners to access information and resources registered with the system by their providers, to plan their own learning, and to maintain a record of their learning. Tutors are able to register recommended learning pathways through courses and modules, thereby encouraging progression into HE. The system allows learners to share their learning plans and experiences with other learners (if they wish) in order to encourage collaborative formulation of future learning goals and aspirations.

This chapter describes personalisation services for self e-learning networks. A self e-learning network consists of web-based learning objects that have been made available to the network by its users, along with metadata descriptions of these learning objects and of the network's users. The proposed personalisation facilities include: querying learning object descriptions to return results tailored towards users' individual goals and preferences; the ability to define views over the learning object metadata; facilities for defining new composite learning objects and automatically deriving their descriptions; and facilities for subscribing to personalised event and change notification services. The personalisation facilities are realised using a combination of Semantic Web technologies including RDF/S, RQL, RVL, and RDF ECA rules.

Constantly embedding information and knowledge about group activity into on-line collaborative learning is a challenging yet one of the latest and most attractive issues to influence learning experience in a positive manner. The possibility to enhance learning group's participation by means of providing appropriate knowledge is rapidly gaining popularity due to its great impact on group performance and outcomes. Indeed, by storing parameters of interaction such as participation behaviour and giving constant awareness and feedback of these parameters to the group may influence group's motivation and emotional state as well as enhance the learners' and groups' problem solving abilities. This implies a need to capture and structure the information generated by group activity and then to extract the relevant knowledge in order to provide learners and tutors with efficient awareness and feedback as regards group performance and collaboration. To that end, we first identify and define the main types of information generated in on-line group activity and then we propose a process for efficiently embedding this information and the knowledge extracted into collaborative learning applications. However, in order to provide learners with effective knowledge, it is necessary to process large and complex event log files from group activity in a constant manner, and thus it may require computational capacity beyond that of a single computer. To that end, in this chapter we show how a Grid approach can considerably decrease the time of processing group activity log files and thus allow group learners to receive selected knowledge even in real time.

E-learning technologies have matured to a point where distance learning classes are commonly offered from many leading Universities around the world. A major challenge in such distributed classrooms is the formation of virtual communities among the participating students, enhancing the overall learning experience. Shared virtual laboratories offer the possibility of forming such virtual communities as students form lab teams to run the same interactive simulation and in the course of such experiments learn to interact and understand each other better. We have designed and implemented a Virtual Scientific Experiment architectural framework on top of a Grid infrastructure for running interactive virtual laboratory experiments for such distributed student communities with visualization capabilities. The architecture is based on Web Services standard protocols such as WSDL and WS-Notification as implemented in the WSRF specification. For the first concrete instantiation of this architecture, we ported a stand-alone Wireless Sensor Network simulator written in Java in our Grid-based architecture and extended it to allow for initial collaborative parameter setup and on-the-fly visualization of the simulation execution and interaction with it, a capability not present in the original simulator. We report on results from running such simulations on a local Grid infrastructure. System evaluation results from a distributed pool of students show the added value of our system in enhancing distance-learning programs and Virtual Classes with extensible collaborative and interactive Virtual Laboratories sessions.

ACTIVEMATH is a Web-based learning environment for mathematics. In this article, we discuss ACTIVEMATH from a service and semantics perspective. We describe the service-oriented technologies of ACTIVEMATH and how the services interplay with the domain semantics (mathematics) and the pedagogical semantics used, e. g., for course generation. More specifically, we provide details on the knowledge representation for mathematics we use in ACTIVEMATH and how it is used to represent learning materials. We also elaborate on the representation of learning objects from a pedagogical point-of-view, that is, how we capture the instructional semantics. We then show how learning goals are represented and how a course generator assembles sequences of learning objects to fulfil these goals. Some tools in ACTIVEMATH are client-based and require different techniques. We use our assembly tool as an example to illustrate how ACTIVEMATH integrates client-based tools.