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    Identifying user strategies in exploratory learning with evolving task modelling

    Cocea, Mihaela and Magoulas, George D. (2010) Identifying user strategies in exploratory learning with evolving task modelling. In: UNSPECIFIED (ed.) Intelligent Systems. New York, USA: Institute of Electrical and Electronics Engineers, pp. 13-18. ISBN 9781424451630.

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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.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Sarah Hall
    Date Deposited: 18 Jul 2013 14:00
    Last Modified: 02 Dec 2016 13:23
    URI: https://eprints.bbk.ac.uk/id/eprint/7728

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