Reusing learning material is very important to design learning environments for real-life learning. The reusability of learning objects results from the product of three main features: modularity, discoverability and interoperability. We proposed learning objects built based on agent architectures, called Intelligent Learning Objects (ILO). This paper discusses how the ILO approach can be used to improve the interoperability among learning objects, learning menagement systems (LMS) and pedagogical agents.Education for the 21 st century - impact of ICT and Digital Resources ConferenceRed de Universidades con Carreras en Informática (RedUNCI

Gomes, Eduardo R.

Silveira, Ricardo Azambuja

Vicari, Rosa

Servicio de Difusión de la Creación Intelectual

    Intelligent Learning Objects: An Agent Aproach to Create Interoperable Learning Objects Ricardo Azambuja Silveira1, Eduardo Rodrigues Gomes2 and Rosa Vicari3 1   Universidade Federal de Santa Catarina, Campus Universitário, s/nº - Caixa Postal 476 Florianópolis – SC, Brazil, silveira@inf.ufsc.br 2   Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 - Campus do Vale - Bloco IV  Porto Alegre - RS –Brazil, ergomes@inf.ufrgs.br. 3   Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 - Campus do Vale - Bloco IV  Porto Alegre - RS –Brazil, rosa@inf.ufrgs.br Abstract. Reusing learning material is very important to design learning environments for real-life learning.  The reusability of learning objects results from the product of three main features: modularity, discoverability and interoperability.  We proposed learning objects built based on agent architectures, called Intelligent Learning Objects (ILO).  This paper discusses how the ILO approach can be used to improve the interoperability among  learning objects, learning menagement systems (LMS) and pedagogical agents.  1 Introduction This paper addresses the improvement of interoperability among learning objects in agent-based learning environments by integrating learning object technology and the multi-agent systems approach.  According to Downes [3], Mohan & Brooks [10], and Sosteric & Hesemeier [11], a learning object is a piece of learning content that can be used several times in different courses or in different situations.  According to Downes [3], the cost of developing learning materials for e-learning can be large, but if related courses are taught, the subject matter is similar and the cost of developing learning material can be shared.  The learning object approach promises to reduce significantly the time and the cost required to develop e-learning courses.  The use of reusable learning objects to create learning environments improves speed, flexibility and economy.  2 Ricardo Azambuja Silveira, Eduardo Rodrigues Gomes and Rosa Vicari  A learning object must be modular, discoverable and interoperable in order to be reused [5].  To achieve these features and improve the efficiency, efficacy and reusability of learning objects, many people have dedicated a great effort.  The main focus has been on standardization.  Organizations such as the IMS Global Learning Consortium [7], IEEE [6], ARIADNE [1], and CanCore [2], have contributed significantly by defining indexing standards called metadata (data about data).  Metadata contain the information to explain what the learning object is about, how to search, access, and identify it, and how to retrieve educational content according to a specific demand. Mohan & Brooks [10] point out some limitations of current learning objects.  According to them an instructional designer must carefully examine a large amount of learning object.  The task of finding the right object may be quite time consuming and the learning object metadata are not very useful in supporting pedagogical decisions. On the other hand, the state of the art in intelligent learning environments (ILE) points to the use of architectures based on the multiagent paradigm [8].  Using the multiagent approach can result in faster, more versatile and lower cost systems.  We proposed the intelligent learning object (ILO) approach as a useful way of enabling the reusability of agents making up pedagogical systems. 2 Intelligent learning objects An intelligent learning object (ILO) is an agent that promotes learning experiences to students in the same way as learning objects used to do.  For this reason, an ILO can be seen as a learning object built through the agent paradigm.  The technological basis of this approach is combination of the technologies for learning objects and for multi-agent systems.  As a learning object, an intelligent learning object must be reusable.  The reusability is given as a result of three features: interoperability, discoverability and modularity [5].  In a learning object approach, metadata is used to give discoverability by describing the pedagogical content of the learning object.  To enable the discoverability feature in ILO, we have adopted the IEEE 1484.12.1 Standard for Learning Object Metadata [6].  The modularity of learning objects can be reached with a good pedagogical project.  The design of the pedagogical task of an ILO must be made according to the expertise of some object matter specialists and pedagogical experts.  Some interoperability can be reached by the use of well-known standards.  For this reason, we adopted two learning object standards: a) the IEEE 1484.12.1 Standard for Learning Object Metadata [6]; and, b) IEEE 1484.11.1 Standard for Learning Technology – Data Model for Content Object Communication.  The 1484.11.1 standard is defined for communication of learning objects with Learning Management Systems (LMS).  We use this standard in interactions among ILOs. To assure interoperability among agents we have adopted the concepts defined by FIPA [4].  FIPA is an organization which defines standards to enable interoperability for multiagent systems.  The main concern of FIPA is the definition of standards to enable the communication between agents.  A well-defined Intelligent learning objects   3  communication framework is vital for interoperability among agents.  We have used this reference model to define a communication framework for ILOs.  The ILOs must use this framework in order to communicate with each other.   3 The ILO multi-agent architecture The ILO architecture [12] encompasses three types of agents.  The Intelligent Learning Object Agents are responsible for playing the role of learning objects.  They generate learning experiences for the students in the same sense of learning objects.  The LMS Agents are abstractions of learning management systems.  They are responsible for dealing with the administrative and pedagogical tasks involving a learning environment as a whole, providing access to the ILOs for the students and storing information about the students..  The ILOR Agents are abstractions of learning object repository systems.  They are responsible for storing data to permit a user or an agent to find ILOs.  Figure 1 illustrates the proposed agent society.   Figure 1.  Proposed agent society The students interact with the LMS agent in order to have learning experiences.  The LMS agent asks the ILOR agent to search the appropriate ILO.  The ILO is then responsible for generating learning experiences for the students.  All these communications are performed by message exchanging using FIPA-ACL [4]. 4 Case study We developed an extended FIPA framework composed of a set of Java classes designed to build ILOs as easily as possible and applied this framework to the agent-based learning environment described by Lucas [9].  Such a system is composed of an ILO playing the role of a special calculator and an animated pedagogical agent 4 Ricardo Azambuja Silveira, Eduardo Rodrigues Gomes and Rosa Vicari  (see Figure 2) playing the role of an animated tutor to help primary school students to learn some fundamental mathematical properties of multiplication.     Figure 2.  The APA giving an instruction.  The animated pedagogical agent works like an LMS and the calculator pedagogical agent is a typical ILO.  The communication between the two agents is performed using the FIPA communication framework.  5   Conclusions Intelligent learning objects are able to improve the adaptability, interoperability and interactivity of learning environments built with these kinds of components by the interaction among the learning objects and between learning objects and other agents.  This concept of communication is more robust than a single method invocation as the object-oriented paradigm used to be.  The test bed application showed that the proposed approach of agent-based learning objects promises to be useful to improve the interoperability of learning objects, pedagogical agents and agent-based learning environments.  The pedagogical agents are capable of providing suitable messages to the student and interact with the learning objects in a powerful way.  This interaction provides more reality to the environment, and the student feels that he/she and the agents are active characters in the learning process.  While the development of educational content is converging to the use of standards towards reusability, we are still developing ad-hoc pedagogical-agent-based learning environments.  This is the issue that this paper addressed.  We should start to think about reusability when developing pedagogical agents.  We need to go Intelligent learning objects   5  towards the use of reusable and interoperable pedagogical agents.  The convergence between learning object and agent technologies seems to be promising. Acknowledgement This project is granted by Brazilian agencies: CNPq, CAPES and FAPERGS. References 1. ARIADNE (2004). Alliance of remote instructional authoring & distribution networks for Europe Retrieved July 7, 2005  http://ariadne.unil.ch. 2. CanCore  (2004). Canadian Core About. Retrieved July 7, 2005 from http://www.cancore.ca/about.html. 3. Downes, S. (2001). Learning objects: resources for distance education worldwide. in International Review of Research in Open and Distance Learning, 2(1). 2001 4. FIPA: The foundation for Intelligent Physical Agents (2002). Specifications. retrieved July 7, 2005 from http://www.fipa.org. 5. Friesen, Norm (2001). What are Educational Objects? Interactive Learning Environments, 3 (9). 6. IEEE Learning Technology Standards Committee (2004). Specifications. retrieved July 7, 2005 from http://ltsc.ieee.org. 7. IMS Global Learning Consortium. (2004). Current specifications. retrieved July 7, 2005 from http://www.imsglobal.org /specifications.cfm. 8. Johnson, W. Lewis; Shaw, Erin. (1997). Using agents to overcome deficiencies in web-based courseware. In: World Conference on Artificial Intelligence in Education, AI-ED, 8., 1997. Proceedings… retrieved July 7, 2005 from www.isi.edu/isd/johnson.html. 9. Lucas, J. P.; Widges, B.; Silveira, R. A. (2005). Inserting Animated Pedagogical Agents Inside Distributed Learning Environments by Means of FIPA Specifications. In: Fourth International Joint Conference on Autonomous Agents & Multiagents Systems (AAMAS) – Proceedings of Agent-Based Systems for Human learning Workshop (ABSHL) 2005, Utrecht. 10. Mohan, P.and Brooks, C. (2003). Engineering a Future for Web-based Learning Objects. Proceedings of International Conference on Web Engineering, Oviedo, Asturias, Spain.  11. Sosteric, Mike, Hesmeier, Susan (2002). When is a Learning Object not an Object: A first step towards .a theory of learning objects. International Review of Research in Open and Distance Learning   ISSN: 1492-3831 12. Silveira, R. A., Gomes, E. R, Vicari, R. M. (2005). Inteligent Learning Objects: An Agent-Based Approach of Learning Objects. In Weert, Tom Van, Tatnall, Arthur (Eds.) Information and Communication Technologies and Real-Life Learning.Boston Springer, 1103 - 110.   6 Ricardo Azambuja Silveira, Eduardo Rodrigues Gomes and Rosa Vicari  

Intelligent learning objects: an agent aproach to create interoperable learning objects

SEDICI - Repositorio de la UNLP

  
  
Intelligent Learning Objects: An Agent 
Aproach to Create Interoperable Learning 
Objects 
Ricardo Azambuja Silveira1, Eduardo Rodrigues Gomes2 and Rosa Vicari3 
1   Universidade Federal de Santa Catarina, Campus Universitário, s/nº - 
Caixa Postal 476 Florianópolis – SC, Brazil, silveira@inf.ufsc.br 
2   Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 
- Campus do Vale - Bloco IV  Porto Alegre - RS –Brazil, 
ergomes@inf.ufrgs.br. 
3   Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 
- Campus do Vale - Bloco IV  Porto Alegre - RS –Brazil, rosa@inf.ufrgs.br 
Abstract. Reusing learning material is very important to design learning 
environments for real-life learning.  The reusability of learning objects results 
from the product of three main features: modularity, discoverability and 
interoperability.  We proposed learning objects built based on agent 
architectures, called Intelligent Learning Objects (ILO).  This paper discusses 
how the ILO approach can be used to improve the interoperability among  
learning objects, learning menagement systems (LMS) and pedagogical 
agents.  
1 Introduction 
This paper addresses the improvement of interoperability among learning objects in 
agent-based learning environments by integrating learning object technology and the 
multi-agent systems approach.  According to Downes [3], Mohan & Brooks [10], 
and Sosteric & Hesemeier [11], a learning object is a piece of learning content that 
can be used several times in different courses or in different situations.  According to 
Downes [3], the cost of developing learning materials for e-learning can be large, but 
if related courses are taught, the subject matter is similar and the cost of developing 
learning material can be shared.  The learning object approach promises to reduce 
significantly the time and the cost required to develop e-learning courses.  The use of 
reusable learning objects to create learning environments improves speed, flexibility 
and economy.  
2 Ricardo Azambuja Silveira, Eduardo Rodrigues Gomes and Rosa Vicari 
 
A learning object must be modular, discoverable and interoperable in order to be 
reused [5].  To achieve these features and improve the efficiency, efficacy and 
reusability of learning objects, many people have dedicated a great effort.  The main 
focus has been on standardization.  Organizations such as the IMS Global Learning 
Consortium [7], IEEE [6], ARIADNE [1], and CanCore [2], have contributed 
significantly by defining indexing standards called metadata (data about data).  
Metadata contain the information to explain what the learning object is about, how to 
search, access, and identify it, and how to retrieve educational content according to a 
specific demand. 
Mohan & Brooks [10] point out some limitations of current learning objects.  
According to them an instructional designer must carefully examine a large amount 
of learning object.  The task of finding the right object may be quite time consuming 
and the learning object metadata are not very useful in supporting pedagogical 
decisions. 
On the other hand, the state of the art in intelligent learning environments (ILE) 
points to the use of architectures based on the multiagent paradigm [8].  Using the 
multiagent approach can result in faster, more versatile and lower cost systems.  We 
proposed the intelligent learning object (ILO) approach as a useful way of enabling 
the reusability of agents making up pedagogical systems. 
2 Intelligent learning objects 
An intelligent learning object (ILO) is an agent that promotes learning experiences to 
students in the same way as learning objects used to do.  For this reason, an ILO can 
be seen as a learning object built through the agent paradigm.  The technological 
basis of this approach is combination of the technologies for learning objects and for 
multi-agent systems.  
As a learning object, an intelligent learning object must be reusable.  The 
reusability is given as a result of three features: interoperability, discoverability 
and modularity [5].  In a learning object approach, metadata is used to give 
discoverability by describing the pedagogical content of the learning object.  To 
enable the discoverability feature in ILO, we have adopted the IEEE 1484.12.1 
Standard for Learning Object Metadata [6].  The modularity of learning objects can 
be reached with a good pedagogical project.  The design of the pedagogical task of 
an ILO must be made according to the expertise of some object matter specialists 
and pedagogical experts.  Some interoperability can be reached by the use of well-
known standards.  For this reason, we adopted two learning object standards: a) the 
IEEE 1484.12.1 Standard for Learning Object Metadata [6]; and, b) IEEE 1484.11.1 
Standard for Learning Technology – Data Model for Content Object 
Communication.  The 1484.11.1 standard is defined for communication of learning 
objects with Learning Management Systems (LMS).  We use this standard in 
interactions among ILOs. 
To assure interoperability among agents we have adopted the concepts defined 
by FIPA [4].  FIPA is an organization which defines standards to enable 
interoperability for multiagent systems.  The main concern of FIPA is the definition 
of standards to enable the communication between agents.  A well-defined 
Intelligent learning objects   3 
 
communication framework is vital for interoperability among agents.  We have used 
this reference model to define a communication framework for ILOs.  The ILOs 
must use this framework in order to communicate with each other.   
3 The ILO multi-agent architecture 
The ILO architecture [12] encompasses three types of agents.  The Intelligent 
Learning Object Agents are responsible for playing the role of learning objects.  
They generate learning experiences for the students in the same sense of learning 
objects.  The LMS Agents are abstractions of learning management systems.  They 
are responsible for dealing with the administrative and pedagogical tasks involving a 
learning environment as a whole, providing access to the ILOs for the students and 
storing information about the students..  The ILOR Agents are abstractions of 
learning object repository systems.  They are responsible for storing data to permit a 
user or an agent to find ILOs.  Figure 1 illustrates the proposed agent society.  
 
Figure 1.  Proposed agent society 
The students interact with the LMS agent in order to have learning experiences.  
The LMS agent asks the ILOR agent to search the appropriate ILO.  The ILO is then 
responsible for generating learning experiences for the students.  All these 
communications are performed by message exchanging using FIPA-ACL [4]. 
4 Case study 
We developed an extended FIPA framework composed of a set of Java classes 
designed to build ILOs as easily as possible and applied this framework to the agent-
based learning environment described by Lucas [9].  Such a system is composed of 
an ILO playing the role of a special calculator and an animated pedagogical agent 
4 Ricardo Azambuja Silveira, Eduardo Rodrigues Gomes and Rosa Vicari 
 
(see Figure 2) playing the role of an animated tutor to help primary school students 
to learn some fundamental mathematical properties of multiplication.  
 
 
 
Figure 2.  The APA giving an instruction. 
 
The animated pedagogical agent works like an LMS and the calculator 
pedagogical agent is a typical ILO.  The communication between the two agents is 
performed using the FIPA communication framework.  
5   Conclusions 
Intelligent learning objects are able to improve the adaptability, interoperability and 
interactivity of learning environments built with these kinds of components by the 
interaction among the learning objects and between learning objects and other 
agents.  This concept of communication is more robust than a single method 
invocation as the object-oriented paradigm used to be.  
The test bed application showed that the proposed approach of agent-based 
learning objects promises to be useful to improve the interoperability of learning 
objects, pedagogical agents and agent-based learning environments.  The 
pedagogical agents are capable of providing suitable messages to the student and 
interact with the learning objects in a powerful way.  This interaction provides more 
reality to the environment, and the student feels that he/she and the agents are active 
characters in the learning process.  
While the development of educational content is converging to the use of 
standards towards reusability, we are still developing ad-hoc pedagogical-agent-
based learning environments.  This is the issue that this paper addressed.  We should 
start to think about reusability when developing pedagogical agents.  We need to go 
Intelligent learning objects   5 
 
towards the use of reusable and interoperable pedagogical agents.  The convergence 
between learning object and agent technologies seems to be promising. 
Acknowledgement 
This project is granted by Brazilian agencies: CNPq, CAPES and FAPERGS. 
References 
1. ARIADNE (2004). Alliance of remote instructional authoring & distribution networks 
for Europe Retrieved July 7, 2005  http://ariadne.unil.ch. 
2. CanCore  (2004). Canadian Core About. Retrieved July 7, 2005 from 
http://www.cancore.ca/about.html. 
3. Downes, S. (2001). Learning objects: resources for distance education worldwide. in 
International Review of Research in Open and Distance Learning, 2(1). 2001 
4. FIPA: The foundation for Intelligent Physical Agents (2002). Specifications. retrieved 
July 7, 2005 from http://www.fipa.org. 
5. Friesen, Norm (2001). What are Educational Objects? Interactive Learning 
Environments, 3 (9). 
6. IEEE Learning Technology Standards Committee (2004). Specifications. retrieved July 7, 
2005 from http://ltsc.ieee.org. 
7. IMS Global Learning Consortium. (2004). Current specifications. retrieved July 7, 2005 
from http://www.imsglobal.org /specifications.cfm. 
8. Johnson, W. Lewis; Shaw, Erin. (1997). Using agents to overcome deficiencies in web-
based courseware. In: World Conference on Artificial Intelligence in Education, AI-ED, 
8., 1997. Proceedings… retrieved July 7, 2005 from www.isi.edu/isd/johnson.html. 
9. Lucas, J. P.; Widges, B.; Silveira, R. A. (2005). Inserting Animated Pedagogical 
Agents Inside Distributed Learning Environments by Means of FIPA Specifications. In: 
Fourth International Joint Conference on Autonomous Agents & Multiagents Systems 
(AAMAS) – Proceedings of Agent-Based Systems for Human learning Workshop 
(ABSHL) 2005, Utrecht. 
10. Mohan, P.and Brooks, C. (2003). Engineering a Future for Web-based Learning Objects. 
Proceedings of International Conference on Web Engineering, Oviedo, Asturias, Spain.  
11. Sosteric, Mike, Hesmeier, Susan (2002). When is a Learning Object not an Object: A first 
step towards .a theory of learning objects. International Review of Research in Open and 
Distance Learning   ISSN: 1492-3831 
12. Silveira, R. A., Gomes, E. R, Vicari, R. M. (2005). Inteligent Learning Objects: An 
Agent-Based Approach of Learning Objects. In Weert, Tom Van, Tatnall, Arthur (Eds.) 
Information and Communication Technologies and Real-Life Learning.Boston Springer, 
1103 - 110. 
 
 
6 Ricardo Azambuja Silveira, Eduardo Rodrigues Gomes and Rosa Vicari 
 


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Intelligent learning objects: an agent aproach to create interoperable learning objects

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