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Designing adaptive learning itineraries using features modelling and swarm intelligence

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Abstract

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user’s success on the course. Therefore, the path can change in order to adapt itself to learners’ preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.

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Notes

  1. IMS Global Learning Consortium. IMS Content Package. Final Specification v.1.2, November 2005.

  2. Advanced Distributed Learning. SCORM 2004 Sharable Content Object Reference Model. ADL, 2004.

  3. IMS Global Learning Consortium. IMS Simple Sequencing version 1.0. Final Specification v.1.0, March 2003.

  4. Open Services Gateway Initiative, http://www.osgi.org.

  5. A bundle is a .zip or .jar archive with a file named manifest.mf which describes the bundle.

  6. E.g., Eclipse Platform Development Environment.

  7. Project of the Eureka-ITEA program supported by the Spanish Ministry of Industry, Commerce and Tourism through the PROFIT program. http://www.passepartout-project.org/.

  8. Project of the Eureka-ITEA program supported by the Spanish Ministry of Science and Technology through the PROFIT program. http://www.itea-osmose.org/.

  9. J-Bones, http://jbones.forge.os4os.org/.

  10. MOSKitt has been used to describe the learning graph using the philosophy of Software Features Modelling. See more at http://www.moskitt.org.

  11. A learning graph must have only one root node. A root node is a node that has no input arc (grade zero).

  12. http://www.moskitt.org.

  13. http://www.atenea-project.org.

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Acknowledgments

This work has been partially supported by the Spanish Ministry of Industry, Tourism and Commerce through the PROFIT project ATENEA (Ref.: FIT-340503-2007-1), by the Spanish Ministry of Science and Education through the project FAMENET-InCare (Ref: TSI2006-13390-C02-02), and by the Andalusian Excellence R&D project CUBICO (Ref: TIC2141).

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Authors and Affiliations

  1. Department of Research and Development, Telvent, Ronda del Tamarguillo 29, 41006, Seville, Spain

    Jose Manuel Marquez Vazquez

  2. Department of Computer Systems and Languages, Universidad de Sevilla, Avenida de la Reina Mercedes s/n, 41012, Seville, Spain

    Juan Antonio Ortega Ramirez

  3. Department of Applied Economics, Universidad de Sevilla, Avenida de Ramón y Cajal 1, 41018, Seville, Spain

    Luis Gonzalez-Abril & Francisco Velasco Morente

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  1. Jose Manuel Marquez Vazquez
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  2. Juan Antonio Ortega Ramirez
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Correspondence to Jose Manuel Marquez Vazquez.

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Marquez Vazquez, J.M., Ortega Ramirez, J.A., Gonzalez-Abril, L. et al. Designing adaptive learning itineraries using features modelling and swarm intelligence. Neural Comput & Applic 20, 623–639 (2011). https://doi.org/10.1007/s00521-011-0524-7

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