Universidade Federal do Rio Grande do SulInstituto de Informática
Intelligent
Ricardo Azambuja [email protected]
Eduardo Rodrigues [email protected]
Rosa Maria [email protected]
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 [email protected]
Eduardo Rodrigues [email protected]
Rosa Maria [email protected]
Federal University of Santa CatarinaDepartment of Informatics and Statiscs
(Florianopolis, Brazil)
Thank you!