Ivan Ganchev, Member, IEEE, Stanimir Stojanov, Member, IEEE, Damien Meere, Máirtín O’Droma, Senior Member, IEEE

ABSTRACT

This paper focuses on the development of InfoStation

multi-agent system architectures facilitating the provision of mobile eLearning (mLearning) services across a University Campus area. Generic models and development approaches are considered. Particular attention is paid to the eContent generation and interpretation, as well as to the specification of the system architecture required to provide the needed flexibility and adaptability according to the requirements of modern mLearning systems. The multi-agent approach for the system implementation is also justified.

1. Introduction

A University Campus InfoStation1 system architecture allowing registered users to access a range of mobile eLearning (mLearning) services from their mobile devices (cellular phones, laptops, PDAs) is perceived as an important evolutionary infrastructural component of a modern eLearning environment. The architecture in the system considered here aims to enhance the mLearning experience and facilitate the provision of various supplementary communications services [1-4]. In its design attention was paid to the following key attributes and requirements, c.f. also [5]: • Ability to adapt rapidly and transparently to changes in

learners’ profiles and their eLearning progress; • Better mechanisms for discovering and comparing

relevant eLearning resources; • Ability to specify the requirements of an instructional

component and delegate to another process (e.g. a software agent) the discovery of a resource satisfying it;

The authors wish to acknowledge the support of the Ireland’s HEA

Targeted Funding Program ‘Technology in Education’ and the Bulgarian Ministry of Education and Science for Research Project “A model and an architecture for mobile user-oriented eLearning services” Ref. No. ВУ-МИ-101/2005.

Dr. I. Ganchev, D. Meere and Dr. M. O’Droma: Telecommunications Research Centre, University of Limerick, Ireland (Ivan.Ganchev@ul.ie; Damien.Meere@ul.ie; Mairtin.ODroma@ul.ie).

Prof. S. Stojanov.: Dept of Computer Systems, Plovdiv University “Paisij Hilendarski”, Bulgaria (e-mail: stani@ecl.pu.acad.bg).

1 The InfoStations are intelligent wireless access points deployed at key points within a University Campus.

• Easy interoperation with both external resources and other learning management systems (LMS). The objective of this paper is to introduce the

InfoStation-based mLearning system architecture, illustrating its flexibility and intelligence, which are enhanced through the utilization of software agents, communicating with the system functional modules. The organization of the paper is as follows. Section 2 considers generic models and approaches for development of eLearning systems. Section 3 briefly presents the proposed multi-agent system structure. Section 4 concludes the paper.

2. eLearning System Development Models and Approaches

2.1 Conceptual Model In general, each eLearning system consists of five

components (Figure 1) the primary ones being: the eContent generator–used by educators and instructors to create electronic content (eContent); and the eContent interpreter–used by learners to interpret the created eContent. The eContent creation and interpretation depends on some important circumstances and limitations, which could be generalized within the following (sub)models: • Domain model – represents the knowledge of a particular

learning area/field. Usually it contains basic (atomic) concepts with different semantic links;

• Pedagogical model – contains information about possible didactical solutions used by the LMS, e.g. to explain the study of a particular eLearning subject. The presentation and control of learning tasks plays an important role in this model;

• User model – stores specific information for individual users, which could be used for personalization of their eLearning process. This information includes the users’ courses/modules, individual preferences, type of mobile devices in use, etc.

PedagogicalModel

Generation eContent Interpretation

User ModelDomain Model

Fig. 1. The eLearning System Conceptual Model.

InfoStation-based mLearning System Architectures: Some Development Aspects

Eighth IEEE International Conference on Advanced Learning Technologies

978-0-7695-3167-0/08 $25.00 © 2008 IEEEDOI 10.1109/ICALT.2008.235

504

In order to meet the eLearning system requirements, these models must be explicitly present with possibilities for support and control at the architectural level.

2.2 Development Approaches Two dominant development approaches lead to two types

of eLearning system architectures: (i) Standardized – independent of the application area and easily adaptable to control different types of business functionality, but with some inefficiency at run-time; (ii) Specialized – developed for particular applications and providing better run-time efficiency for a particular functionality, but with low adaptability and portability.

For the development of our InfoStation-based mLearning system architecture, however, a hybrid approach was followed, whereby a standardized (service-oriented) architecture is expanded with some adaptable components of the three models (domain-, pedagogical-, user-model) for the eContent generation. This is an approach which offers a high level of adaptability and flexibility with the potential for dynamic parameter control during the process of eContent generation and system run-time.

3. Multi-Agent System Architecture – Service Control & Session Management

The software system architecture could be considered as a singular mapping of the conceptual model (c.f. Section 2) onto the InfoStation network architecture. The system software consists of cooperating agents and electronic services (eServices). From an architectural point of view the possibility for a dynamic switching (during the run-time) from one learning area to another is very important. Today’s approaches for the implementation of eLearning models usually utilize ontologies. Depending on their nature, some services could be parameterized by retrieving the parameter values from the user model and pedagogical model. This increases the possibilities for architectural support for mLearning service personalization. The possibility for parameterization, parameter types, allowable values, the time needed to assign these values (during service development or dynamically during the run-time) are defined in the corresponding subject models.

For user session management we use a stereotype approach. A key issue, which needs to be addressed, is that the users of particular services would be known only at the run-time. Hence for real personalization, the architecture must have sufficient flexibility and adaptability. For this, a corresponding stereotype agent is chosen for each new user when s/he first accesses the system. From the chosen stereotype and user’s personal profile, a corresponding personal assistant (PA) is generated as a server-side agent instance. The PA is transferred to the user-operating environment. All subsequent user accesses to the system

are performed through the PA (Figure 2). For further user session management we use a goal-driven approach, whereby the actual learning tasks/goals are defined during the learning process in correspondence with the pedagogical model used. The learning goals are recorded in the actual user profiles. There is a progress and goal completion tracking feature, with tracing possible by the server part of the architecture and/or by the learners’ PAs. This enables users to interrupt/suspend their service sessions (e.g. without achieving a particular learning goal).

Fig. 2. The agent-oriented approach for user session management.

4. Conclusion

The development of InfoStation mLearning multi-agent system architectures has been presented in this paper. Generic models and development approaches have been considered. A system architecture with sufficient flexibility and adaptability has been proposed. A multi-agent system implementation approach has been justified. 5. References [1] I. Ganchev, S. Stojanov, M. O’Droma, D. Meere. “An

InfoStation-Based University Campus System Supporting Intelligent Mobile Services”. Journal of Computers, Vol. 2, No. 3, Pp. 21-33; Academy Publishers (ISSN: 1796-203X), May, 2007.

[2] I. Ganchev, S. Stojanov, M. O’Droma, D. Meere. “An InfoStation-Based Multi-Agent System for the Provision of Intelligent Mobile Services in a University Campus Area”. 3rd IEEE Conf. on Intelligent Systems (IEEE IS’06), London, UK, 4-6 Sept. 2006. Proceeding Book (ISBN 1-4244-0196-8) Pp. 426-431.

[3] I. Ganchev, S. Stojanov, M. O’Droma, D. Meere. “An InfoStation-Based University Campus System for the Provision of mLearning Services”. 6th IEEE Int. Conf. on Advanced Learning Technologies (IEEE ICALT’06), Kerkrade, The Netherlands; 5–7 July 2006. Proceeding Book (ISBN 0-7695-2632-2) Pp. 195-199.

[4] I. Ganchev, S. Stojanov, M. O’Droma, D. Meere. “Adaptable InfoStation-based mLecture Service Provision within a University Campus”. 7th IEEE Int. Conf. on Advanced Learning Technologies (IEEE ICALT’07), Niigata, Japan; 18-20 July 2007. Proceeding Book (ISBN 0-7695-2916-X). Pp. 165-169.

[5] S. Cook, R. Harrison, T. Millea, and L. Sun, "Challenges of Highly Adaptable Information Systems", Applied Software Engineering Research Group, University of Reading. 2003.

505