A Software System for Online Learning Applied in the Field of Computer Science

  • Gabriela Moise Petroleum-Gas University of Ploiesti Computer Science Department no. 39 Bd. Bucuresti, Ploiesti, Romania

Abstract

The computer-assisted learning is a very modern study area, which can be applied to the learning process. The main objective of this paper is to present a software system for online learning based on the intelligent software agents technologies. The main ideas on which this paper is built are: to any person is associated a learning profile (the idea is based on the existence of multiple intelligences, defined by Gardner [3]); the pedagogical resources can be shaped through educational semantic networks or through conceptual maps; a flexible software system in computer assisted learning must be based on the intelligent agents’ technology. The system dedicated to computer-assisted learning must be adapted to the learning profile of each student.The author presents a flexible online teaching software system, which learns to teach according to the learning profile of each student (the author defines this system in the PhD thesis and includes: intelligent agent structures, reward learning algorithms, algorithms to generate plans for an agent).The application includes two agents: the supervising agent and the pedagogical agent, which determines the optimal pedagogical resources for teaching the course. The application has been designed in Microsoft Visual Studio 6.0 and uses Microsoft Agent Technology, which allows vocal recognition. Also, the Protéjé 3.0 software has been used, software that allows building ontology for computer assisted learning. The system has been experimented on the Graph Theory Course, taught at postuniversitary computer science courses, the results proving the necessity of defining a strategy for selecting the pedagogical resources presented to the students according to their learning profile.

References

[1] Bowling, M., Veloso M., Multiagent Learning Using a Variable Learning Rate, Journal Artificial Intelligence, Vol. 136, pp. 215-250, 2002.
http://dx.doi.org/10.1016/S0004-3702(02)00121-2

[2] Buiu, C., Albu. M., Agenti Software Inteligenti, Editura ICPE, 2000.

[3] Gardner, H., Intelligence Reframed, Multiple Intelligence for the 21st Century, Published by Basic Books, 1999.

[4] Joyce, B, Weil., Calhoun.E., Models of Teaching, Published by Basic Books, 1999.

[5] Leon, F., Sova, I., Gâlea, D., Reinforcement Learning Strategies for Intelligent Agents in Knowledge-Based Information Systems, Proceedings of the 8th International Symposium on Automatic Control and Computer Science, Ia¸si, 2004.

[6] Moise, G., The role of intelligent agents in online learning environment, CBLIS Conference Procedings, pp. 98-105, 2005.

[7] Moise G., A Software System For Online Learning Applied To Higher Education In The Field Of Computer Science, Thesis: Petroleum-Gas University of Ploiesti, 2006.

[8] Müller, Jörg P., The design of intelligent agents: a layered approach, Lecture notes in computer science, Vol. 1177: Lecture notes in artificial intelligence, Springer-Verlag, 1996.
http://dx.doi.org/10.1007/BFb0017806

[9] Rao, A., S., Georgeff, M., P., BDI Agents From Theory to Practice,Proceedings of the First International Conference on Multi Agent Systems ICMAS-95, San Francisco, 1995.

[10] Sowa, J.F. ,Kowledge Representation Logical, Philosophical, and Computational Foundations, Brooks/Cole, Thomson Learning.

[11] Watkins,C. , Learning from Delayed Rewards, Thesis: University of Cambridge, England.

[12] Wooldridge, M., Jennings, R., N., BDI Agents From Theory to Practice,Intelligent Agents: Theory and Practice, Knowledge Engineering Review, Vol. 10 No 2, 1995.
http://dx.doi.org/10.1017/S0269888900008122

[13] http://www.nwlink.com
Published
2007-01-01
How to Cite
MOISE, Gabriela. A Software System for Online Learning Applied in the Field of Computer Science. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 2, n. 1, p. 84-93, jan. 2007. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2340>. Date accessed: 22 jan. 2022.

Keywords

intelligent agent, conceptual map, learning style