The 2nd International Workshop on
Collaborative and Learning
Applications of Grid Technology and
Grid Education
The next generation GRID for effective human learning
May 9 - 12, 2005, Cardiff, UK
http://www.elegi.org
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
Technology-enhanced learning and access
to cultural heritage
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 eLearning 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 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 prearranged 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 domainindependent 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
LRT – IMS-MD
(educational.learningresourcetype)
IL – IMS-MD
(educational.interactivitylevel)
IT – IMS-MD
(educational.interactivitytype)
Act1
Act22
Act
Role-part1
Role-part22
Role-part
environment
environment11
Activity1
http://www.crmpa.it/
Fruition of a LO with:
learning-contents/
- LRT = “Experiment”
calculus-domain/lo/limit- IL = “Very low”
example.html
- IT = “Expositive”
….
Learner
Learner
Activity
Activity22
environment22
environment
Learner
Learner
http://www.crmpa.it/
of a LO with:
Fruition
LRT = “Narrative text”
-learning-contents/
IL = “Very low”
-calculus-domain/lo/limitIT = “Expositive”
-theory.html
….
….
Scenario
Abstract Unit of Learning:
Plunging didactic methods into didactic domains
Explanation of some Calculus concepts by inductive
method
unit of learning
Pedagogy applied to
limit concept
Activity1
Pedagogy applied to
derivative concept
1
- LRT = “Experiment”
- IL = “very low”
- IT = “Expositive”
- t. concept = limit
3
2
Activity2
- LRT = “Narrative text”
- IL = “very low”
- IT = “Expositive”
- t. concept = limit
Activity1
- LRT = “Experiment”
- IL = “very low”
- IT = “Expositive”
- t. concept = derivative
4
Activity2
- LRT = “Narrative text”
- IL = “very low”
- IT = “Expositive”
- t. concept = derivative
Scenario
Running unit of learning
Search
Delivery
Localization Service
UoL Delivery Service
UDDI
……..
Query LOs
Delivery
RepositoryN
Query LOs
Delivery
UoL Delivery
Service
Repository1
3
2
1
UoL Player
WSRP Consumer/
Producer
Enhanced IMS-LD Engine
IMS-LD interpreter
Services Connectors
Proxy and adapters for
WSRF WSRP and other
technologies
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 ServiceOriented 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: elearning and pedagogy; technical framework
and tools for e-learning; innovation and
distributed e-learning
ELeGI project is very close to the eLearning 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
Thank you very much
for your attention
Contacts:
ritrovato@crmpa.unisa.it