The next generation GRID for effective human learning Technology-enhanced learning and access to cultural heritage Enabling Technologies for future learning scenarios: The Semantic Grid for Human Learning Pierluigi Ritrovato Research & Technology Director Centro di Ricerca in Matematica Pura ed Applicata ELeGI Scientific Coordinator The 2nd International Workshop on Collaborative and Learning Applications of Grid Technology and Grid Education May , 2005, Cardiff, UK

Overview Background and motivations The ELeGI Project Some characteristics of future learning scenarios Building our vision The Semantic Grid for Human Learning Scenario Conclusions and future works

Background and motivations Knowledge is changing our society and our lifestyle It is the new cornerstone around which education (and not only education) should be re-thinked Information transfer based learning approaches are no more suitable Learner’s passivity instead of activity and dynamicity learner has no way to impact the learning process Too effort on defining and providing ‘collective inputs’ (e.g. the educational contents) of the learning process No personalization, difficulties to put the ‘single learner’ feedbacks in the learning process, no contextualization Uniformity of learning outcomes All have to learn everything in the same way

Background and motivations Current e-Learning solutions Mostly e-Learning solutions are based upon the previous approach They are distance learning solutions and provide a ‘digitalization’ of the previous approach e-Learning becomes an activity in which teachers produce, and students consume, multimedia books on the Web Missing specific didactical models Not any support of pedagogical aspects There are also some e-Learning solutions not so tied to the Info transfer paradigm supporting key aspects of the learning process collaboration, course personalization, virtual experiments … These solutions present a common issue: they are mainly focused only on a single aspect of the learning process What happens if my pedagogical needs change? Do I have to change my e-Learning solutions?

Background and motivations It is time to change According to us, it is time to make a process innovation in defining and developing e- Learning solutions that should support a learning process: driven by the pedagogical needs of the learner in which the learner is a central and active figure in which the learning outcome (e.g. the knowledge creation) occurs through social interactions and active experiences and it is used as a feedback to refine the process itself

The ELeGI Project The Project Vision To produce a breakthrough in current (e) Learning practiceswith the creation of a distributed and pervasive environment based on Grid technology for effective human learning where To produce a breakthrough in current (e) Learning practices with the creation of a distributed and pervasive environment based on Grid technology for effective human learning where  learning is a social activity consumed in communications and collaborations based dynamic Virtual Communities  learners, through direct experiences, create and share their knowledge in a contextualised and personalised way

The ELeGI Project How we conceive the Learning Contextualised learning the understanding of concepts through direct experience of their manifestation in realistic contexts (e.g. providing access to real world data) Social learning the user’s mental processes are influenced by social and cultural contexts Collaborative learning more than a simple information exchange – peers interactions, conversation tracks, knowledge reconstruction Personalised learning guarantee the learner to reach a cognitive excellence through different learning path tailored on learner’s characteristics and preferences

Some characteristics of future learning scenarios Distributed architecture and deployment environment Service Orientation teaching and training are conceived as support services The learning process is enabled or enhanced by a combination of services course material retrieving and packaging, tutoring, virtual meeting, … Community, Conversation and collaboration based Is central for all kinds of formal and informal learning Is crucial in “learning by being told” but also in the coaching of skill acquisition Learner autonomy Freedom of decisions Flexibility Control of time, space, place, devices, … Dynamicity the learner can influence the process The social and context aspects influence the learner High demand of interoperability access to Resources on heterogeneous environments Security and Trust AAA protocols, confidentiality, privacy

Building our vision Enabling technologies To build future learning scenarios we need a technology allowing: autonomous and dynamic creation of communities active and realistic experiments personalization knowledge creation and evolution … to reach all the features of the previous slide! Currently, we have different enabling technologies allowing, more or less, to create our vision Distributed Middleware, Web Services, Agent, Semantic Web, Grid …

Building our vision Enabling technologies Distributed middleware not Service Oriented: stable reference models for distributed architecture, many facilities also domain specific but … Too tied to a product vision while we are closer to a service one Method based not Message based: it need a lot of effort to implement a composition based paradigm useful to create personalized learning experiences (re-)using data, units of learning, knowledge and tools distributed across different organizations Web Service: service based, aiming to provide interoperability among distributed loosely coupled components, good to implement a composition based paradigm but … It is generally based upon a stateless model while the state is fundamental in conversational processes It need effort to implement resource management and discovery mechanisms, information and knowledge management, resource sharing and other important features of the proposed learning process and of a Virtual Learning Community

Building our vision Enabling technologies Agent: good for personalization and contextualization, communities creation, goal oriented but … They have to be reinforced with mechanism to discover, acquire, federate, and manage the capabilities/resources/contents needed to create/delivery the personalized learning experiences Semantic Web: knowledge management and formalization, knowledge based communities and interactions but … They need effort to define advanced algorithm for resources reservation to support efficient resources management allowing 3d simulations and immersive VR

Building our vision The Grid added value Grid technologies: Rely upon a dynamic and stateful service model (e.g. WSRF or WS-I+) and this affects also the development of learning scenarios (need for state in conversational processes) Are key technologies to build the VO paradigm (VO are the right place for carrying out collaborative learning experiences) As we will see later, are the most suitable to build IMS LD Complaint Framework (our learning process is pedagogical driven) Provide the scale of computational power and data storage needed to support realistic and experiential based learning approaches involving responsive resources, 3d simulations and immersive VR Are demonstrating their effectiveness for implementing e-Science infrastructure for sharing and manage data, applications and also knowledge Through the virtualisation and sharing of several kind of resources facilitate the dynamic contexts generation The dynamic service discovery and creation will allow the true personalisation Grid are becoming a glue among different technologies like Agent, Semantic Web, Web Service that, as standalone solutions, provide only partial benefits to our learning vision … and our purpose is not to spend effort to fill the gaps

Building our vision IMS-LD and support for pedagogies The pedagogical support is a key factor that distinguishes our learning approach with respect to other relevant learning initiative We need to catch all the pedagogical features identified and not to customize our solution for a single pedagogy IMS-LD is focused on the modeling of learning and teaching practices that go beyond simple traditional web-based LO’s delivery the learning activities, which can be defined as interactions between a learner and an environment to achieve a planned learning outcome the learning approaches, involving selection and orchestration of the activities on the basis also of the pedagogies

Building our vision Drawbacks of IMS-LD In any case, IMS-LD presents some drawbacks LD scenarios implement domain-dependent pedagogies (early binding of learning objects) Learning processes cannot be really adaptive with respect to learner profiles (execution flows are pre- arranged at IMS-LD design-time) If the context (didactic domain + didactic model + learner model) of a learning scenario changes I need new contents and services suitable for the new context … and I have to bind them statically! LD scenarios don’t exploit the advantages of a dynamic distributed environment in which I can dynamically find and bind new contents and services

Building our vision Improving IMS-LD for our needs Our solution to the previous drawbacks is to provide: extensions for IMS-LD in order to define domain- independent pedagogies a knowledge model able to describe educational domains and learners using respectively ontologies and learner profiles a set of algorithms for automatic building of personalized learning paths pulled out from ontologies using learner profiles and target concepts a set of algorithms able to “join” personalized learning paths with domain-independent pedagogies obtaining an abstract unit of learning (no binding with real learning objects) where each concept in learning path is explained using the selected pedagogy an abstract unit of learning run-time model interacting with a Grid system able to provide (at run-time) a late binding with desired learning objects and services

Building our vision The Grid added value to learning scenarios Grid technologies provide advanced mechanisms for automatic discovery and binding of new suitable contents and services as well as self-adaptive mechanisms when deploying the LD scenarios and, obviously, the learning activities composing a scenario Grid provides dynamicity and adaptiveness to LD scenarios

The Semantic Grid for Human Learning The Semantic Grid for Human Learning can be defined as a domain verticalization of the Semantic Grid improved with tools, services, languages, standards and technologies for the Education WSRF+WSRP for enhancing the underlying service model (dynamic, stateful and presentation-oriented) Semantically enriched services typical of the learning domain IWT Grid-Aware Base Services providing functionalities typical of a Learning Management System IWT Grid-Aware Learning Services providing high-level functionalities for a personalized learning experience Driver Service: WSRP compliant services providing the full management (creation, delivery, update) of a didactical resource IMS-LD for creating learning scenarios able to catch all the identified pedagogical features User Centric Portal implementing the behaviour of a WSRP consumer allowing an easy customization and administration of community portals And, obviously, the standards, specifications and technologies providing the foundation of the Semantic Grid (e.g. Data Services, OWL, OWL-S, …)

The Semantic Grid for Human Learning This is a work in progress

Scenario Extending IMS-LD with IMS-MD attributes A simple inductive didactic method

Scenario Abstract Unit of Learning: Plunging didactic methods into didactic domains Explanation of some Calculus concepts by inductive method

Scenario Running unit of learning

Conclusion and future works Assessment with other initiative OKI: open specifications that describe how the components of a learning environment communicate with each other and with other campus systems Sakai: Collaboration and Learning Environment by exploiting the OSIDs defined in the frame of OKI and Grid Service- Oriented portals (OGCE) Commonalities between Sakai and our solution: service concept and SOA, Grid Service Oriented portal based on the portlet concept, some lowest level and higher level OSIDs find a mirror in our IWT Grid-Aware Base and Learning Services and other OSIDs overlap with some Grid standards and specification (SQL OGSA DAI) Differences between Sakai and our solution: We aims to support pedagogies, we are less content oriented, and our solution is more focalized on knowledge management and collaboration through social interactions (not only collaborative tool)

Conclusion and future works Assessment with other initiative JISC ELF: it is part of a wider e-Learning programme focused on four themes: e- learning and pedagogy; technical framework and tools for e-learning; innovation and distributed e-learning ELeGI project is very close to the e- Learning programme of the JISC (We have the focus on the same themes) We have identified a technology and also a set of services specific for a VLC while ELF is yet neutral form these viewpoints

Conclusion and future works Clear benefits for educational community can come from a well defined Grid based strategy and Grid community must start to fill the gaps among powerful general visions (like the Semantic Grid) and practical requirements of many e-Research and e-Business communities We have discussed of how to customize the Semantic Grid vision for the Education & Training and we hope that similar efforts related to other fields of e-Research may arise Very next steps: To complete the development of IWT Grid-Aware To refine the Semantic Grid for Human Learning Architecture To define more complex future learning scenarios

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