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A Grid Based Software Architecture for Delivery of Adaptive and Personalized Learning Experiences Authors: Angelo Gaeta , Matteo Gaeta and Pierluigi Ritrovato Presenter: Vinay Macherla Introduction ELeGI project.

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a grid based software architecture for delivery of adaptive and personalized learning experiences

A Grid Based Software Architecture for Delivery of Adaptive and Personalized Learning Experiences

Authors: Angelo Gaeta , Matteo Gaeta

and

Pierluigi Ritrovato

Presenter: Vinay Macherla

introduction
Introduction
  • ELeGI project.
  • Radically advance the effective use of technology enhanced learning.
  • Knowledge construction in an individualized way using collaborative learning approaches, Leverage on Grid technologies.
  • Address issues relating to formal and informal learning.
creation and delivery of uol
Creation and Delivery of UoL

ELeGI approach consists of:

  • Definition of a general Learning Model.
  • Generate a Unit of Learning (UoL) and to dynamically adapt it during the learning process according to the learner’s behaviour.
  • Operational process and Theoretical learning model
operational process to build an adaptive uol
Operational Process to Build an Adaptive UoL

Knowledge Building : formalization of knowledge related to domain.

UoL Building : Learning objectives to be achieved, identifying target concepts and building a skeletal structure for the learning experience.

UoL Delivery : Runtime execution of UoL, discovery of resources satisfying metadata specifications.

virtual scientific experiment model
Virtual Scientific Experiment Model
  • The VSE model fits learning aspects within a constructivistic vision: the role of the learner, the importance of context and collaboration.
  • VSE model macro phases:
    • Presentation
    • Practical situation
    • Abstract situation
    • Institutionalisation
contd
Contd.
  • Presentation Phase: Provides description of the didactic experience the student is about to start.
  • Practical Situation: Learner lives the experience.
  • Abstract Situation: Extrapolates abstract model representation
  • Institutionalization: Learner approved procedural and semantic correctness of concepts.
elegi architecture
ELeGI Architecture

Application Layer

E-Learning Application

Contents & Services Orchestration

Learning Services

E-Learning Layer

Course Management

Services

Didactical Model

Mangm. Services

Learning Metadata

Services

Support Services

Knowledge Management Services

Learner Profile

Management Services

Personalization

Services

Ontology Management

Services

Security

Semantic

Communication & Collaboration Services

Discovery&

Semantic

Annotation

Services

Trust

Services

Negotiation

Services

VLC 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

Monitoring

Services

Resource

Management

Services

Information

Services

Core Services

Infrastructure Services

contd9
Contd.
  • Environment Management Services: provides services and tools to support the creation, operation, and maintenance of a learning community.
  • Learning Services: provides services and tools to support the execution of the three processes of the Learning Model.
  • Application Layer: uses the services provided by the underlying layer to implement application in the e-learning domain.
grid technologies

InfrastructureServices

Access to Learning

Object Repository

Information Services

Monitoring Services

Accounting Services

ResourceManagement

Security Services

Execution Management

Self Management

Grid technologies

Grid

It facilitates the realization of ubiquitous computing concept

The Grid technologies are considered the natural evolution of distributed systems and the Internet

It allows the virtualization and sharing of several kind of resources facilitating the dynamic context generation

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
Virtual Learning Communities (VLC)

VLC Layer provides general and re-usable services for the lifecycle management of virtual communities.

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

Member Profile Management Services

  • allow the management of the profile information of the Community Members
  • support information privacy

Negotiation Services

  • allow negotiation of the agreement on the provision of a service
  • support Quality of Services
learning services
Learning Services

The e-Learning services facilitate and manage the learning process.

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

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

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.

Didactical Model Management Services

  • provide operation to manage the didactical models:
    • create,
    • edit,
    • validate,
    • browse,

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

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)

Ontology Management Services

  • extend the ontology services provided by the lower VLC sub-layer for learning domain.
knowledge and didactic models
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

e learning model
E-learningmodel

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
e learning model15
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
context based ontology
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 to define learning scenarios
IMS-LD: 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
Scenario Description:Physics course in the Open University

Collaborative/Social Learning in Physics Course at HOU (Hellenic Open University)

Purpose:

Target Group:

HOU students

students perform experiments/ simulations and construct knowledge through the exchange of data and knowledge

Main Characteristics:

formal (but highly diverse student population)

Type of learning:

Type of services

needed:

Virtual Experiments/ Virtual

Communities Support

the context
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
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
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)
slide22

Scenario Setup

  • Legend
  • Super Node
  • Super Node
    • Nodes
    • Backbone : GUNet (155 Mbps)
scenario execution

Data layer

Resource “Z”

Data layer

Data layer

Resource “Y”

Course Personalization service

Localization Service

Web GUI

(WSRP)

Data layer

Resource “X”

Scenario Execution

Invoke the Localization Service in order to find the list of Course Services

scenario execution24

Locator

Service

UDDI

Data Layer

(Learning Object

Repository)

Data Layer

(Learning Object

Repository)

Data Layer

Course Driver Instance

The Course Driver Service contacts the Data Layer to retrieve the Student Model and Ontology and it invokes the Course Personalisation Service

The Course Personalization Service, on the basis of the Student Model and the Ontology, generates the personalized learning path

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

Course Personalization service

Course Personalization service

Request the delivery of the Course

Find the list of the drivers which are able to delivery the Resource

Requests the delivery of Resource “X”

Scenario Execution

Asks for a Personalized Learning Path

Invoke the IS in order to create a Corse Driver Instance

Personalized

Learning Path

Instantiates a suitable driver for Resource X

Builds Web GUI for delivery of Resource X

Retrieve LO

Instantiates a suitable driver for Resource “Y”

Builds Web GUI for delivery of Resource “Y”

Retrieve LO

the grid added value to elegi 1
The Grid added value to ELeGI (1)
  • Grid technologies:
    • Rely upon a dynamic and stateful service model and this affects also the development of learning scenarios
    • Provide dynamicity and adaptiveness to LD scenarios
    • Provide the scale of computational power and data storage needed to support realistic and experiential based learning approaches involving 3d simulations and Virtual Reality
the grid added value to elegi 2
The Grid added value to ELeGI (2)
  • Grid technologies:
    • Are demonstrating their effectiveness for implementing e-Science infrastructure for sharing and manage data
    • 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
conclusions
Conclusions
  • Effective re-use of resources: exploiting the ELeGI software architecture it is possible to re-use all the building blocks of a UoL
  • Extensibility wrt services integration