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.

Full text not available from this repository.

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:	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

Statistics

Archive Staff Only (login required)

Edit/View Item