Context-dependent feedback prioritisation in exploratory learning revisited

Authors:
Mihaela Cocea;George D. Magoulas
Affiliations:
School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK and London Knowledge Lab, Birkbeck College, University of London, London, UK;London Knowledge Lab, Birkbeck College, University of London, London, UK
Venue:
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Year:
2011

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Abstract

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.