Abstract
In e-learning initiatives content creators are usually required to arrange a set of learning resources in order to present them in a comprehensive way to the learner. Course materials are usually divided into reusable chunks called Learning Objects (LOs) and the ordered set of LOs is called sequence, so the process is called LO sequencing. In this paper an intelligent agent that performs the LO sequencing process is presented. Metadata and competencies are used to define relations between LOs so that the sequencing problem can be characterized as a Constraint Satisfaction Problem (CSP) and artificial intelligent techniques can be used to solve it. A Particle Swarm Optimization (PSO) agent is proposed, built, tuned and tested. Results show that the agent succeeds in solving the problem and that it handles reasonably combinatorial explosion inherent to this kind of problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brusilovsky, P.: Adaptive and Intelligent Technologies for Web-based Education. Künstliche Intelligenz, Special Issue on Intelligent Systems and Teleteaching 4, 19–25 (1999)
Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction 6, 87–129 (1996)
De Bra, P., Houben, G.-J., Wu, H.: AHAM: a Dexter-based reference model for adaptive hypermedia. In: Proceedings of the tenth ACM Conference on Hypertext and hypermedia. ACM Press, Darmstadt (1999)
Karampiperis, P.: Automatic Learning Object Selection and Sequencing in Web-Based Intelligent Learning Systems. In: Zongmin, M. (ed.) Web-Based Intelligent E-Learning Systems: Technologies and Applications. Idea Group, London (2006)
De Bra, P., Aerts, A., Berden, B., de Lange, B., Rousseau, B., Santic, T., Smits, D., Stash, N.: AHA! The adaptive hypermedia architecture. In: Proceedings of the fourteenth ACM conference on Hypertext and hypermedia. ACM Press, Nottingham (2003)
Wiley, D.A.: Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. In: Wiley, D.A. (ed.) The Instructional Use of Learning Objects (2000)
ADL: Shareable Content Object Reference Model (SCORM). The SCORM 2004 Overview. Advanced Distributed Learning (ADL) Initiative (2004)
Wilkinson, J.: A matter of life or death: re-engineering competency-based education through the use of a multimedia CD-ROM. In: Proceedings of IEEE International Conference on Advanced Learning Technologies, 2001, pp. 205–208 (2001)
IMS: Reusable Definition of Competency or Educational Objective - Information Model. IMS Global Learning Consortium (2002)
IEEE: Learning Technology Standards Committee (LTSC). Draft Standard for LearningTechnology - Data Model for Reusable Competency Definitions. IEEE (2007)
HR-XML: Competencies (Measurable Characteristics) Recommendation. HR-XML Consortium (2006)
CEN/ISSS: European Model for Learner Competencies. Comité Européen de Normalisation / Information Society Standardization System (CEN/ISSS) (2006)
IEEE: Learning Technology Standards Committee (LTSC). Learning Object Metadata (LOM). 1484.12.1. IEEE (2002)
IEEE: Learning Technology Standards Committee (LTSC). Standard for Learning Technology—Extensible Markup Language (XML) Schema Definition Language Binding for Learning Object Metadata. 1484.12.3. IEEE (2005)
IMS: Reusable Definition of Competency or Educational Objective - Best Practice and Implementation Guide. IMS Global Learning Consortium (2002)
Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1993)
Hinchey, M.G., Sterritt, R., Rouff, C.: Swarms and Swarm Intelligence. Computer 40, 111–113 (2007)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, WA, Australia, vol. 1944, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)
Xiaohui, H., Eberhart, R.C., Yuhui, S.: Swarm intelligence for permutation optimization: a case study of n-queens problem. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 243–246. IEEE Press, Indianapolis (2003)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004)
Robinson, J., Rahmat-Samii, Y.: Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation 52, 397–407 (2004)
Barchino, R., Gutiérrez, J.M., Otón, S.: An Example of Learning Management System. In: Isaías, P., Baptista, M., Palma, A. (eds.) IADIS Virtual Multi Conference on Computer Science and Information Systems (MCCSIS 2005), vol. 1, pp. 140–141. IADIS Press, Virtual (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de-Marcos, L., Martínez, JJ., Gutiérrez, JA. (2008). Particle Swarms for Competency-Based Curriculum Sequencing. In: Lytras, M.D., Carroll, J.M., Damiani, E., Tennyson, R.D. (eds) Emerging Technologies and Information Systems for the Knowledge Society. WSKS 2008. Lecture Notes in Computer Science(), vol 5288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87781-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-87781-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87780-6
Online ISBN: 978-3-540-87781-3
eBook Packages: Computer ScienceComputer Science (R0)