Adaptive modelling of users' strategies in exploratory learning using case-based reasoning

Mihaela Cocea, S. Gutierrez-Santos, G. Magoulas

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Abstract

In exploratory learning environments, learners can use different strategies to solve a problem. To the designer or teacher, however, not all these strategies are known in advance and, even if they were, introducing them in the knowledge base would involve considerable time and effort. In previous work, we have proposed a case-based knowledge representation, modelling the learners behaviour when constructing/exploring models through simple cases and sequences of cases, called strategies. In this paper, we enhance this approach with adaptive mechanisms for expanding the knowledge base. These mechanisms allow to identify and store inefficient cases, i.e. cases that pose additional difficulty to students in their learning process, and to gradually enrich the knowledge base by detecting and adding new strategies.
Original languageEnglish
Title of host publicationKnowledge-based and intelligent information and engineering systems: 14th international conference, KES 2010, Cardiff, UK, September 8-10, 2010, proceedings, part II
EditorsR. Setchi, Ivan Jordanov, R. Howlett, L. Jain
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages124-134
Number of pages11
Edition6277
ISBN (Print)9783642153891
Publication statusPublished - 2010
EventProceedings of the 14th International Conference on Knowledge-based and Intelligent Information and Engineering Systems: Part II - Cardiff, United Kingdom
Duration: 8 Sept 201010 Sept 2010

Publication series

NameLecture notes in computer science
PublisherSpringer-Verlag
Number6277

Conference

ConferenceProceedings of the 14th International Conference on Knowledge-based and Intelligent Information and Engineering Systems: Part II
Country/TerritoryUnited Kingdom
Period8/09/1010/09/10

Keywords

  • case-based reasoning
  • exploratory learning environments
  • knowledge base adaptation
  • user modelling

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