Combining Agents and Ontologies for Building an Intelligent Tutoring System

Stamatis Panagiotis, Ioannis Panagiotopoulos, Christos Goumopoulos, Achilles Kameas

2015

Abstract

In this paper an approach for building an intelligent tutoring system is presented, based on a multi-agent architecture and combined with ontologies for knowledge representation. The system developed is focused on a bottom up, reactive generation of an active sequence of knowledge units regarding a set of adjustable, high level learning goals. The learning process begins with a set of simple learning goals that require a few learning objects and as the educational process proceeds, the student has to achieve higher learning outcomes that combine other low level outcomes which have been already achieved. The system is able to adapt to student’s learning profile and progress by applying proper learning tactics to prioritize through a weight calculation scheme the sequence of the learning outcomes to achieve. The main components of the system consisting of ontological models of the learner and the subject under study, gateway agents and tutor agents with their core modules (learning space management and learning tactics control) are explained and a detailed description of their interaction is given in the context of an example application. Finally, the advantages of the proposed approach are laid out, especially in the setting of a distance learning education system.

References

  1. Acampora, G., Loia, V., Gaeta, M., 2010. Exploring eLearning Knowledge Through Ontological Memetic Agents. IEEE Computational Intelligence Magazine, vol.5, no.2, pp. 66-77.
  2. Ali, A, P., Dehghan, H., Gholampour, J., 2010. An Agent Based Multilayered Architecture for E-learning system. In Proceeding of E-Learning and E-Teaching (ICELET). Second International Conference on IEEE, pp.22-26.
  3. Bellifemine, F., Caire, G., Pogg, A., Rimassa, G., 2003. Jade-A White Paper. In EXP in search of innovation, vol. 3, no. 3, 2003, pp. 6-19.
  4. Bokhari, M., Ahmad, S., 2014. Multi-agent Based ELearning Systems: A Comparative Study. In Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies.
  5. Bologna Working Group, 2005 A Framework for Qualifications of the European Higher Education Area.
  6. Bishop B., Kiryakov, A., Ognyanoff, D., Peikov, I., Tashev, Z., Velkov, R., 2011. OWLIM: A family of scalable semantic repositories. In Semant. web, vol. 2, no. 1, pp. 33-42.
  7. Dung, Q, P., Florea, M, A., 2011. An Architecture and a Domain Ontology for Personalized Multi-Agent eLearning Systems. In Proceedings of the 2011 Third International Conference on Knowledge and Systems Engineering, pp. 181-185.
  8. Foundation for Intelligent Physical Agents (FIPA). FIPA ACL Message Structure Specification (2002), viewed 27 May 2014, http://www.fipa.org/specs/fipa00061/.
  9. Hammami, S., Mathkour, H., Al-Mosallam, E.A., 2009. A multi-agent architecture for adaptive E-learning systems using a blackboard agent. 2nd IEEE International Conference on Computer Science and Information Technology, pp. 184-188.
  10. Kalou, A., Solomou, G., Pierrakeas, C., Kameas, A., 2012. An Ontology Model for Building, Classifying and Using Learning Outcomes. 12th IEEE International Conference on Advanced Learning Technologies, pp. 61-65.
  11. Krathwohl, D., Anderson, L., (eds), 2001. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives, Abridged Edition.
  12. LTSC Learner Model Working Group of the IEEE 2000. Draft Standard for Learning Technology - Public and Private Information (PAPI) for Learners (PAPI Learner), IEEE p1484.2/d7, 2000-11-28.
  13. McGuinness, D.L, van Harmelen, F., 2004. OWL Web Ontology Language Overview, W3C Recommendation, http://www.w3.org/TR/owl-features/
  14. Nikolopoulos, G., Solomou, G., Pierrakeas, C., Kameas, A., 2013. An Instructional Design Methodology for Building Distance Learning Courses. 7th International Conference in Open and Distance.
  15. Nikolopoulos, G., Kalou, A., Pierrakeas, C., Kameas, A., 2012a. Creating a LO Metadata Profile for Distance Learning: An Ontological Approach. 6th Research Conference, pp. 37-48.
  16. Nikolopoulos, G., Solomou, G., Pierrakeas, C., Kameas, A., 2012b. Modeling the Characteristics of a Learning Object for Use within e-Learning Applications. Proceedings of the Fifth Balkan Conference in Informatics, pp. 112-117.
  17. Nkambou, R., Mizoguchi, R., Bourdeau, J., (eds), 2010. Advances in intelligent tutoring systems. Heidelberg: Springer, 2010.
  18. Noy N., McGuiness D., 2001. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMi-2001-0880.
  19. Panagiotopoulos, I., Kalou, A., Pierrakeas, C., Kameas, A., 2012. An Ontological Approach for Domain Knowledge Modeling and Management in E-Learning Systems. In 1st AI in Education Workshop: Innovations and Applications, pp. 95-104.
  20. Panagiotopoulos I., Kalou, A., Pierrakeas, C., Kameas, A., 2013. Adult student modeling for intelligent distance learning systems. In Special Issue on AIAI 2012 of the International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, vol. 21, nos. 2/3.
  21. Peña, A., Sossa, H., 2010. Semantic representation and management of student models: An approach to adapt lecture sequencing to enhance learning. In Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I, pp. 175-186.
  22. Polson, M., Richardson, J., 1988. Foundations of Intelligent Tutoring Systems. Psychology Press.
  23. Popham, W.J., 2011. Transformative Assessment in Action: An Inside Look at Applying the Process. ASCD, USA.
  24. Smythe, C., Tansey, F., Robson, R., 2001. IMS Learner Information Package Information Model Specification, http://www.imsglobal.org/profiles/lipinfo01.html.
  25. Yaghmaie, M., Bahreininejad, A., 2011. A context-aware adaptive learning system using agents. In Expert Systems with Applications, Vol. 38, Issue 4, pp. 3280- 3286.
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Paper Citation


in Harvard Style

Panagiotis S., Panagiotopoulos I., Goumopoulos C. and Kameas A. (2015). Combining Agents and Ontologies for Building an Intelligent Tutoring System . In Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-107-6, pages 15-24. DOI: 10.5220/0005422900150024


in Bibtex Style

@conference{csedu15,
author={Stamatis Panagiotis and Ioannis Panagiotopoulos and Christos Goumopoulos and Achilles Kameas},
title={Combining Agents and Ontologies for Building an Intelligent Tutoring System},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2015},
pages={15-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005422900150024},
isbn={978-989-758-107-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Combining Agents and Ontologies for Building an Intelligent Tutoring System
SN - 978-989-758-107-6
AU - Panagiotis S.
AU - Panagiotopoulos I.
AU - Goumopoulos C.
AU - Kameas A.
PY - 2015
SP - 15
EP - 24
DO - 10.5220/0005422900150024