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    Context-dependent feedback prioritisation in exploratory learning revisited

    Cocea, Mihaela and Magoulas, George D. (2011) Context-dependent feedback prioritisation in exploratory learning revisited. In: Konstan, J. and Conejo, R. and Marzo, J.L. and Oliver, N. (eds.) User Modeling, Adaption and Personalization. Lecture Notes in Computer Science 6787. Berlin, Germany: Springer Verlag, pp. 62-74. ISBN 9783642223624.

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    The open nature of exploratory learning leads to situations when feedback is needed to address several conceptual difficulties. Not all, however, can be addressed at the same time, as this would lead to cognitive overload and confuse the learner rather than help him/her. To this end, we propose a personalised context-dependent feedback prioritisation mechanism based on Analytic Hierarchy Process (AHP) and Neural Networks (NN). AHP is used to define feedback prioritisation as a multi-criteria decision-making problem, while NN is used to model the relation between the criteria and the order in which the conceptual difficulties should be addressed. When used alone, AHP needs a large amount of data from experts to cover all possible combinations of the criteria, while the AHP-NN synergy leads to a general model that outputs results for any such combination. This work was developed and tested in an exploratory learning environment for mathematical generalisation called eXpresser.


    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 13:01
    Last Modified: 02 Dec 2016 13:23


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