Identifying user strategies in exploratory learning with evolving task modelling

Mihaela Cocea, G. Magoulas

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Abstract

In this paper we present work on adaptive identification of learners’ strategies, gradually developing a higher level of adaptation based on evolving models of mathematical generalisation tasks in an Exploratory Learning Environment. A similarity-based classification approach is defined for the identification of strategies, using an initially small number of classes (i.e. strategies). A strategy is composed of several patterns with relations between them. An evolution monitor component observes changes in the environment and triggers a mechanism that builds-up the task model. The task model evolves when new relevant information becomes available by adding a new strategy (class) or a new inefficient pattern, i.e. patterns that make it difficult for the learner to generalise.
Original languageEnglish
Title of host publication2010 5th IEEE International Conference on Intelligent Systems (IS 2010): Proceedings of a meeting held 7-9 July 2010, London, United Kingdom
Place of PublicationPiscataway
PublisherIEEE
Pages13 -18
Number of pages6
ISBN (Print)9781424451630
DOIs
Publication statusPublished - Jul 2010
EventIntelligent Systems (IS), 2010 5th IEEE International Conference - London
Duration: 7 Jul 20109 Jul 2010

Conference

ConferenceIntelligent Systems (IS), 2010 5th IEEE International Conference
Period7/07/109/07/10

Keywords

  • evolution monitor component
  • evolving task modelling
  • exploratory learning environment
  • similarity-based classification approach
  • user strategy identification
  • computer aided instruction
  • pattern classification
  • user interfaces

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