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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Official URL: http://dx.doi.org/10.1109/IS.2010.5548339

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:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Research Centres and Institutes:	Birkbeck Knowledge Lab
Depositing User:	Sarah Hall
Date Deposited:	18 Jul 2013 14:00
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7728

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