Universidade Federal do Rio Grande do SulInstituto de Informática

Intelligent

Ricardo Azambuja Silveirasilveira@inf.ufsc.br

Eduardo Rodrigues Gomesergomes@inf.ufrgs.br

Rosa Maria Vicarirosa@inf.ufrgs.br

Federal University of Santa CatarinaDepartment of Informatics and Statiscs

(Florianopolis, Brazil)

Intelligent Learning Objects: an Agent Approach to Create Reusable Intelligent Environments With Learning Objects

Agenda

1. What is a learning object

2. What is an intelligent learning object

3. Why should we mix intelligent agent and learning object

4. How ILOs interact with each other

What is a Learning object

• A learning object is an entity of learning content which can be used several timesin different courses or in different situations.

• The use of reusable learning objects improves quickness, flexibility and economyin creating learning environments.

• A learning object must be modular, discoverableand interoperablein order to be reused.

• LEGO is the most known metaphor to define learning objects

What makes something to be formally a Learning Object?

• Organizations such as IMS Global Learning Consortium, IEEE, ARIADNE, and CanCore, have contributed significantly by defining index standardscalled metadata.

• Metadatastructures containinformation to explain:

– whatthe learning object isabout;

– how to search, access, and identify it;

– how to retrieveeducational content according to a specific demand.

Rose Colored

Iron

Formal description

(for learning purposes)

Object

Some problems with learning objects

• A lot of work has to be done to use learning objects:– Build educational environmentin which they can work

– Search and locatethese objects

– Arrange them in a proper order, according to the design of the course where they are used in

– In certain cases - when the object is a Flash animation or a chunk of streaming media, for example – we have to install and configure the appropriate toolsand viewing software

Although all of this seems to be easier to do, we need smarter learning objects

The SCORM reference model

• SCORM ® (Sharable Content Object Reference Model)– SCORM: is a model that references a set of interrelated technical

specifications and guidelines, like the IEEE 1484 LOM standard and the IMS Content Sequencing standard, designed to meet the requirements for learning objects.

– SCORM model explores the object oriented paradigm of software programming and software engineering

– In this model an object has attributes and behaviorscontrolled by function calls (methods)

– A Learning Message System(LMS) uses this method to deal with the object

functions

attributes

OBJECT

LMS

What is a software agent

• An agent is a software piece that works in a continuousand autonomousway in a particular environment, generally inhabited by other agents, and able to interfere in that environment, in a flexible and intelligent way, not requiring frequent human intervention or guidance.

• A multi-agent system can be viewed as weakly-coupled network of problem-solver automats that work together to solve problems which transcend their individual skill.

• This problem-solvers are autonomous and heterogeneous, and need coordination and communication to help each other.

Pedagogical agents

• Multi-Agent systems usually are very appropriate to design solutions for problems that need to be handled in a cooperative approach

• Researches in Intelligent Tutoring Systems and Intelligent Learning Environments point out the use of agent’s based architectures.

• This approach provides student-modeling mechanisms that give more power to the learning environments to perform more versatile and adaptable teaching strategies.

Why should we mix intelligent agent and learning object?

• More powerful communicationmechanisms– Agents use high level communication languages – Agent Communication Languages (ACL) provides more powerful

semantics in communication using formal protocols and formal content languages (onthologies).

• More powerful knowledge representationmechanisms– Agents can use mental states (beliefs, desires and intentions) as logical

formalism to represent the world

• More powerful learning capability– Agents can learn by its own experience

• More autonomy (activation / stop, and integration of ILO)– Agents can make decisions on the fly according to its knowledge

But multi-agent based learning environments is not yet heaven

• Pedagogical agent is not easy to implement

• They usually are part of ad-hoc architectures

• They usually become obsolete

Joining pedagogical agents and learning objects

What is an Intelligent Learning Object

• An Intelligent Learning Object is an agent that plays the role of a learning object.

Multi-Agent Systems Learning Objects

Intelligent Learning Objects•

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ILO general architecture

• Intelligent Learning Objects (ILO) agents - are agents that play the learning objects role;

• Learning Manager System (LMS) agent - is the agent that plays the Learning Management Systems role;

• Intelligent Learning Objects Repository (ILOR) agent - is an abstraction of the Learning Objects Repository

ILO architecture

• The Intelligent Learning Objects are designed as a Pedagogical agent– implements similar SCORM APIspecification

– performs messages sending and receiving, and performs agents’specific task, according to its knowledge base.

Agent

FIPA Agent PlatformFIPA Agent Platform

Any A

gent

Pedagogical Agent

Manifest File

XML Sequencing Rules

XML ContentPackaging Information

XML Metadata

Agent

HTMLFile

HTMLFile

FlashAnimation

FlashAnimation

GIF ImageGIF ImageManifest File

XML Sequencing Rules

XML ContentPackaging Information

XML Metadata

Manifest File

XML Sequencing Rules

XML ContentPackaging Information

XML Metadata

AgentAgent

HTMLFile

HTMLFile

FlashAnimation

FlashAnimation

GIF ImageGIF Image

ILO Life-Cycle

• As the agent receives a new FIPA-ACL message it processes the API function according to its content, performing the adequate behavior and act on the Learning Object (external environment).

• According to the agent behavior model, the message-receiving event can trigger some messages sending mental model updating and particular specific agent action on the Learning object

• Agent’s knowledge base reflects the metadata of the learning object

New message processing

Behavioral rules setting

Particular and communication

actions

Mental model updating

Planning

Agent Cycle

Object Metadata

Object files

Mensagem Knowledge

BaseIntention

Beliefs

Desires

Cognitive model

Skills

Learning object

Agents Communication Framework

• Communication is the exchange of declarative statements• FIPA reference model:

– Using FIPA-ACL– Using FIPA-OS (or JADE) implementation– Defining ILOs' interaction protocols and dialogs– Defining an ILOs' Ontology

Interoperability problems

ILOs Ontology

• Concepts– Concept (metadata :content string)

• there is a data model which contains metadata information about the educational content of the ILO,contained in the :content parameter

– Concept (dataModel :content string):• there is a data model which contains information about the interaction

between a student and an ILO

– Concept (learner :name string :id string :data-model string):• there is a student with the name defined in :name, which has an unique

identifier contained in :id. The information about the interaction between this student and an ILO is contained in :data-model.

– Concept (ilo :agent-id string :metadata string :location string):• there is the ILO which metadata information is defined in :metadata.If

this agent is operating in the agent society in a certain moment, the :agent-id parameter has its unique identifier. In the other case, :location has a reference for the location where the agent is found

ILOs Ontology

• Actions– Action (send-metadata):

• used when an agent needs the metadata information of ILO. This action does not have parameters

– Action (send-learner):• be used when an agent needs information about the student. This action

does not have parameters.

– Action (search-ilo:metadata <metadata>): • used when an agent needs to have the ILOR sending information about

ILOs that satisfy the criteria defined in the :metadata parameter.

– Action (get-learner-lms :learner string :ilo string): • used when an agent needs to have the LMS sending information about

the student :learner related to the ILO :ilo.

– Action (put-learner-lms :learner <learner> :ilo <ilo>):• used when an agent needs to have the LMS storing the information

about a student :learner related to the ilo :ilo.

ILOs Ontology

• Actions– Action (put-learner-ilo :learner <learner>):

• used when an agent needs to have the ILO evaluating the information about a student :learner.

– Action (activate :ilo <ilo>): • used when the ILO needs to have its status changed to activated in the

ILOR’s list of activated ILOs.

– Action (deactivate:ilo <ilo>):• used when the ILO needs to have its status changed to deactivated in the

ILOR’s list of activated ILOs.

Dialogues

• Registering• Request ILO metadada

• Request learner information

Phase Sender Receiver Performative Content1 Agent ILO Request ( action <AID> <send-metadata> )2 ILO Agent Agree ( <phase 1 content> )

ILO Agent Not-understood ( <phase 1 content> )

ILO Agent Refuse ( <phase 1 content> <reasons> )3 ILO Agent Inform ( result <phase 1 content> <metadata> )

ILO Agent Failure ( <phase 1 content> <reasons> )

Phase Sender Receiver Performative Content1 Agent ILO Request ( action <AID> <send-learner> )2 ILO Agent Agree ( <phase 1 content> )

ILO Agent Not-understood ( <phase 1 content> )

ILO Agent Refuse ( <phase 1 content> <reasons> )3 ILO Agent Inform ( result <phase 1 content> <learner> )

ILO Agent Failure ( <phase 1 content> <reasons> )

Test Bed

• An ILO playing the role of a special calculator• An Animated Pedagogical Agent (APA) playing the role of

an LMS and an animated tutor• Help primary school students to learn some fundamental

mathematical properties of multiplication.

LMS Agent as anAnimated Pedagogical Agent

ILO playing the roleof a calculator

Test Bed

Vídeo

Conclusions

• We need to stop thinking of learning objects as just chunks of instructional content and to start thinking of them as small, self-reliant computer programs.

• We purpose to move from the concept of a wrapper to the concept of a smart learning object.

• As a learning object is being created, it should be created in such a way that the objects should initialize by detecting its environmentand the nature of its contents, communicate each other in a high level of abstraction, interact with other kind of agents and promote significant learning experiences by interacting with the students and by cooperating each other.

Universidade Federal do Rio Grande do SulInstituto de Informática

Ricardo Azambuja Silveirasilveira@inf.ufsc.br

Eduardo Rodrigues Gomesergomes@inf.ufrgs.br

Rosa Maria Vicarirosa@inf.ufrgs.br

Federal University of Santa CatarinaDepartment of Informatics and Statiscs

(Florianopolis, Brazil)

Thank you!