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Context-dependent feedback prioritisation in exploratory learning revisited

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

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.
Original languageEnglish
Title of host publicationUser modeling, adaption and personalization: 19th international conference, UMAP 2011, Girona, Spain, July 11-15, 2011. proceedings
EditorsJ. Konstan, R. Conejo, J. Marzo, N. Oliver
Place of PublicationBerlin
PublisherSpringer
Pages62-74
Number of pages13
Volume6787
Edition6787
ISBN (Print)9783642223624
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Number6787

Documents

  • UMAP.pdf

    Accepted author manuscript (Post-print), 405 KB, PDF document

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