The ELeGI Project Contact Person: Pierluigi Ritrovato Research & Technology Director Centro di Ricerca in Matematica Pura ed Applicata ELeGI Scientific Coordinator email: ritrovato@crmpa.unisa.it

Overview ELeGI The ELeGI architecture The Project Vision The Approach Goals SEES and Demonstrators The ELeGI architecture Grid technologies Virtual Learning Communities Learning Services Knowledge and Didactic Models E-learning model Context-based ontology IMS-LD The proposed scenario: Physics course in the Open University The context Scenario Set-Up Scenario Execution The Grid added value to ELeGI

To produce a breakthrough in current The Project Vision To produce a breakthrough in current (e)Learning practices with the creation of an open, Grid based, distributed and pervasive environment where effective human learning is the result of a social activity through communications and collaborations learners will create their knowledge through direct experience in a contextualised and personalised way and share it with others in dynamic Virtual Communities

The ELeGI approach The Learning GRID Infrastructure SEES & Demos Design and Implementation of Service Oriented infrastructure Pedagogical and Usability Evaluation The Learning GRID Infrastructure GRID Technologies Dissemination Training Exploitation Standardisation SEES & Demos Didactical Models Knowledge Repres. Convers. processes Enhanced Presence

ELeGI Goals To create new potential for ubiquitous and collaborative human learning, merging experiential, personalised and contextualised approaches To define and implement an advanced service-oriented Grid based software architecture for learning. This objective will be driven by the pedagogical needs and requirements elicited from Service Elicitation and Exploitation Scenarios (SEES) To validate and evaluate the software architecture and the didactical approaches through the use of SEES and Demonstrators

SEES and Demonstrators Informal Learning Alphabetisation for Durable Development Learning and Training of Researchers in Organic Chemistry e-Qualification by Open Universities Formal Learning Masters in ICT with remote teaching and tutoring activities Physics course in the Open University Demonstrators Virtual Scientific Experiments for teaching high level mathematical courses Learning services for Accountancy and Business Finance Learning services for Mechanical Engineering

ELeGI Architecture Infrastructure Services Application Layer E-Learning Application Contents & Services Orchestration Learning Services E-Learning Layer Course Management Services Learning Metadata Services Didactical Model Mangm. Services Support Services Knowledge Management Services Learner Profile Management Services Personalization Services Ontology Management Services Security Semantic Discovery& Semantic Annotation Services Communication & Collaboration Services VLC Services Trust Services Negotiation Services Billing Services VLC Management Services Member Profile Management Services Policy Services Grid Layer Execution Management Services Accounting Services Self Management Services Security Services Data Services Information Services Monitoring Services Resource Management Services Core Services Infrastructure Services

Infrastructure Services Grid technologies Execution Management Infrastructure Services Monitoring Services Information Services Resource Management Security Services Accounting Services Access to Learning Object Repository Self Management Grid It facilitates the realization of ubiquitous computing concept It allows the virtualization and sharing of several kind of resources facilitating the dynamic context generation The Grid technologies are considered the natural evolution of distributed systems and the Internet It facilitates the creation of emerging challenging learning scenarios through dynamic VO It provides services and advanced mechanisms for automatic discovery and binding of new suitable contents and services Enabling the creation of dynamic, distributed and heterogeneous Virtual Learning Communities

Virtual Learning Communities (VLC) VLC Layer provides general and re-usable services for the lifecycle management of virtual communities. Communication & Collaboration Services VLC Management Services Member Profile Management Services Policy Services Discovery& Semantic Annotation Services Negotiation Services Billing Services Trust Services Discovery and Semantic Annotation Services offer semantically-enabled registries and key features to publish service descriptions support basic ontology management such as editing, browsing, mapping, consistency and validation, versioning; capture annotation and dynamically link resources based on those annotations; take advantage from the semantic enabled registries to enable more sophisticated discovery Communication/Collaboration Services support synchronous and asynchronous interaction (email, forum, instant messaging, chat, …) support different media formats (text, image, audio, video, and their combination) support many communication models (one-to-one, one-to-many, broadcast, many-to-many) Billing Services charge the use of services and resources prepare and send bill VLC Management Services provide administration utilities for the management of the Virtual Community virtual community definition and creation member registration/deregistration … Trust Services provide basic trust capabilities support recommendation support delegation Policy Services allow the management of: role of the community members privilege of the community members policy to access/use resources Negotiation Services allow negotiation of the agreement on the provision of a service support Quality of Services Member Profile Management Services allow the management of the profile information of the Community Members support information privacy

Learning Services The e-Learning services facilitate and manage the learning process. Personalization Services dynamically adapting and delivering of the learning resources personalize the learning paths according to learner profile and needs (i.e. Adaptive Learning Path Generation Services that allow to automatically produce a personalized learning path for each learner) Support Services Alert Services Help Services providing help features to assist learners in achieving their learning objectives Assessment Services, providing online facility to check learning progress during and at the end of the course e-Portfolio Services, supporting the management and assessment of artefacts created by learners Reporting Services, providing facilities for producing standardized and automated reports on data … Ontology Management Services extend the ontology services provided by the lower VLC sub-layer for learning domain. Learner Profile Management Services allow the management of learner profile information: Student Cognitive State Learning Preferences allows automatic update as a consequence of the new learning experiences performed Contents & Services Orchestration searching and collecting dynamically contents and services composition and orchestration of a didactical course (contents and services) use the didactical and knowledge models deliver contextualised learner services Didactical Model Management Services provide operation to manage the didactical models: create, edit, validate, browse, … Course Management Services access and manage courses, modules, and other units of learning administration utilities (assignment management, student/staff management, assignment/submission evaluation, …) Learning Metadata Services provide metadata services for learners and learning resources, including Resource registration (i.e. providing metadata), Metadata management, Search and evaluation. Contents & Services Orchestration Course Management Services Learning Metadata Services Didactical Model Management Services Learner Profile Personalization Services Ontology Management Services Support Services

Knowledge and Didactic Models The general e-learning model allows the construction of context-based and personalised learning paths Extensibility and flexibility Implication of the student Formulation of an initial open theoretical model aimed to create personalised learning path based on the assumption that «Learning does not occur because of one specific type of interaction, but because of the availability of all of them. One type of interaction, or one type of agent, being selected depending on the needs of the learner at the time when the interaction is looked for, as well as of the specific characteristics of the knowledge at stake.» (Balacheff, 2000) So we have studied how the knowledge representation can take into account the context in which the learning occurs and the characteristics of the involved learner

E-learning model Didactic Transposition From the knowledge to the concrete knowledge From the concrete knowledge to the contextualised didactic knowledge From the contextualised didactic knowledge to the personalised didactic knowledge In the following we will define a theoretical model that, taking advantage of the cited three models, allow IWT to built a “Unit of Learning”, where with this term we mean a whatever delimited educational object, such as a course, a module, a lesson, etc. (IMS Learning Design Specification). The presented model takes particular care of the didactic transposition and the devolution. From the knowledge to the concrete knowledge: the domain expert, thanks to the directions derived from the institutions (e.g. ministerial programmes), selects from the knowledge coming from the research the knowledge as teaching object described by a meta-ontology. From the concrete knowledge to the contextualised didactic knowledge: starting from the meta-ontology, the Domain Expert builds a particular “view” of the meta-ontology, that we will call Generic Contextualised Ontology (GCO), taking into account the context in which the teaching/learning process occurs. From the didactic contextualised knowledge to the didactic personalised knowledge: if learning is individual, the system automatically creates what we call Specific Personalised Ontology (SPO), obtained by specialising the meta-ontology on the basis of the characteristics of the single learner (SM) in the framework of the fixed context.

E-learning model Didactic Transposition Definition of the Target of Learning Definition of the sequencing of Elementary Metadata Concepts (ECM) Definition of the Unit of Learning Definition of the Target of Learning: once an ontology (GCO or SPO) has been defined, through the definition and retrieval of the Target of Learning (TL), the teacher defines the set of elementary concepts (atomic) representing the learning objectives; such concepts contain all the information (metadata) deriving from the ontology we have defined in the previous passage (which depend on the SM and DM). Definition of the sequencing of ECM: as the elementary metadata concepts composing the TL have been fixed, through the Propaedeutic relationship (that we believe compulsorily present into the ontology), the system automatically determines the order for the acquisition of the elementary concepts, thus producing a sequencing. Definition of the Unit of Learning: applying a proper algorithm of choice of Learning Object (LO) associated to the EMC, the LOs are then defined for. each elementary concept and the LOs sequence is therefore fixed in accordance with the sequencing defined before, which ends with constituting the Unit of Learning to be delivered to the learner.

Context-based ontology The Generic Contextualised Ontology (GCO) will keep the same base structure of the meta-ontology but will bring with itself some metadata, derived from the Context, that will describe one or more families of concepts.

IMS-LD: our way to define learning scenarios Describe and implement learning activities based on different pedagogies, including group work and collaborative learning Coordinate multiple learners and multiple roles within a multi-learner model, or, alternatively, support single learner activities Coordinate the use of learning content with collaborative services Support multiple delivery models, including mixed-mode learning IMS Learning Design also enables: Transfer of learning designs between systems Reuse of learning designs and materials Reuse of parts of a learning design, e.g. individual activities or roles Internationalisation, accessibility, tracking, reporting, and performance analysis, through the use of properties for people, roles and learning designs

Scenario Description: Physics course in the Open University Purpose: Collaborative/Social Learning in Physics Course at HOU (Hellenic Open University) Target Group: HOU students Main Characteristics: students perform experiments/ simulations and construct knowledge through the exchange of data and knowledge Type of learning: formal (but highly diverse student population) Type of services needed: Virtual Experiments/ Virtual Communities Support

The context Physics Course: 4-year course leading to a Bachelor Degree in Natural Sciences 12 modules + 3 laboratory (3 modules related to Physics: 7 text books suitable for Open and Distance Learning) Student attendance: > 2500 students Permanent Academic Staff (Prof., Ass. Prof.) Tutors (Phd holders) Students organized in classes based in specific cities Physics Lab DMSC Lab

The context: City coverage Teaching method: Text books Synchronous & Asynchronous collaboration tools (…but mainly email/WWW is used) Class meetings (a form of social learning) Assignments (4-6 per module) Class/student distribution

The context: User Needs Knowledge construction : Perform experiment (visualisation of data sets and output) Search for resources and/or share results Access supporting educational material Perform on-line test/essay Virtual Communities support (social learning): Collaborate using asynchronous sharing services (e.g. sharing documents, knowledge, VSE results etc.) Collaborate using synchronous sharing services during an experiment (with other students and/or the tutor)

Scenario Setup Legend Super Node (Patras) Super Node (Athens) Nodes (Iraklion, Piraeus, Ioannina, Volos, Thessalonica, Xanthi) Backbone : GUNet (155 Mbps)

Course Personalization service Scenario Execution Web GUI (WSRP) Resource “Z” Data layer Resource “Y” Data layer Course Personalization service Resource “X” Data layer Data layer Localization Service Invoke the Localization Service in order to find the list of Course Services

Find the list of the drivers which are able to delivery the Resource Scenario Execution Locator Service Find the list of the drivers which are able to delivery the Resource UDDI Data Layer Asks for a Personalized Learning Path Request the delivery of the Course Course Driver Instance Course Personalization service Course Personalization service Invoke the IS in order to create a Corse Driver Instance Personalized Learning Path Requests the delivery of Resource “X” Instantiates a suitable driver for Resource X Builds Web GUI for delivery of Resource X Retrieve LO Data Layer (Learning Object Repository) Instantiates a suitable driver for Resource “Y” The Course Personalization Service, on the basis of the Student Model and the Ontology, generates the personalized learning path The Course Driver Service contacts the Data Layer to retrieve the Student Model and Ontology and it invokes the Course Personalisation Service Obtained the Learning Path, the Course Driver is able to find and create an instance of a Driver service able to manage the resource of the Course The Client interacts with the Instantiator Service to create a new Course Driver Instance The Client interacts with the Localization Service to find a list of Course Services Data Layer (Learning Object Repository) Retrieve LO Builds Web GUI for delivery of Resource “Y”

The Grid added value to ELeGI (1) 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 state management in conversational processes) The key technologies to build the VO (Virtual Organization) paradigm (VO are the right place for carrying out collaborative learning experiences) Provide dynamicity and adaptiveness to LD scenarios (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 (Virtual Reality)

The Grid added value to ELeGI (2) Grid technologies: Are demonstrating their effectiveness for implementing e-Science infrastructure for sharing and manage data, applications and also knowledge Through the virtualization and sharing of several kind of resources facilitate the dynamic contexts generation The dynamic service discovery and creation will allow the true personalisation

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