Graph theory based model for learning path recommendation

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DOIResolve DOI: http://doi.org/10.1016/j.ins.2013.04.017
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TypeArticle
Journal titleInformation Sciences
ISSN0020-0255
Volume251
Pages1021; # of pages: 12
SubjectComplex task; Educational data minings (EDM); Efficient learning; Learning designs; Learning objects; Learning paths; Design; Graph theory; Recommender systems; Soft computing; Learning systems
AbstractLearning design, the activity of designing a learning path, can be a complex task, especially for learners. A learning design recommendation system would help self-learners find appropriate learning objects and build efficient learning paths during their learning journey. Educational Data Mining (EDM) has provided an impressive amount of novelties related to learning object recommendation systems. However, most of the solutions proposed thus far do not take into account eventual competency dependencies among learning objects and/or are not designed for large repositories of interdependent learning objects. We propose a model to build a learning design recommendation system based on graph theory. From this model, we propose, implement and test an approach using the concept of cliques to recommend learning paths. © 2013 Published by Elsevier Inc. All rights reserved.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology (IIT-ITI)
Peer reviewedYes
NPARC number21269904
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Record identifier66b1c4d6-a766-4588-85ac-ed409c1a93c7
Record created2013-12-13
Record modified2016-05-09
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