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A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual...

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Detalles Bibliográficos
Autores principales: Chrysafiadi, Konstantina, Virvou, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing AG 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607740/
https://www.ncbi.nlm.nih.gov/pubmed/23543890
http://dx.doi.org/10.1186/2193-1801-2-81
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author Chrysafiadi, Konstantina
Virvou, Maria
author_facet Chrysafiadi, Konstantina
Virvou, Maria
author_sort Chrysafiadi, Konstantina
collection PubMed
description In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner’s knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner’s knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.
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spelling pubmed-36077402013-03-27 A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system Chrysafiadi, Konstantina Virvou, Maria Springerplus Research In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner’s knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner’s knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging. Springer International Publishing AG 2013-03-05 /pmc/articles/PMC3607740/ /pubmed/23543890 http://dx.doi.org/10.1186/2193-1801-2-81 Text en © Chrysafiadi and Virvou; licensee Springer. 2013 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Chrysafiadi, Konstantina
Virvou, Maria
A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
title A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
title_full A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
title_fullStr A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
title_full_unstemmed A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
title_short A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
title_sort knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607740/
https://www.ncbi.nlm.nih.gov/pubmed/23543890
http://dx.doi.org/10.1186/2193-1801-2-81
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