Resource-adaptive Selection of Strategies in Learning from Worked-Out Examples

Peter Gerjets 1 Katharina Scheiter 1 Werner H. Tack 1
1 Collaborative Research Center 378
Abstract : Most tasks can be pursued by using different strategies (Logan, 1985; Reder & Schunn, 1998). In this paper we focus on strategies of learning from worked-out examples. Within a resourceoriented framework these different strategies can be classified according to their costs and benefits. These features may determine which strategy will be selected for accomplishing a task in situations with certain resource limitations. We investigate specific hypotheses about strategic adaptations to resource limitations (e.g., time pressure or lack of prior knowledge) within a hypertext-based learning environment. A comparison of the strategy selection of good and poor learners is used to assess the degree of subjects' resource adaptivity. Ideas for modeling resource- adaptive selection of strategies within the ACT-R architecture are discussed.
Type de document :
Communication dans un congrès
L. R. Gleitman & A. K. Joshi. Twenty Second Annual Meeting of the Cognitive Science Society (CogSci2000), 2000, Philadelphia, United States. Mahwah, NJ: Erlbaum, pp.161-171, 2000
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger
https://telearn.archives-ouvertes.fr/hal-00190438
Contributeur : Jerome Zeiliger <>
Soumis le : vendredi 23 novembre 2007 - 08:46:37
Dernière modification le : mardi 31 octobre 2017 - 14:22:08
Document(s) archivé(s) le : lundi 12 avril 2010 - 04:09:54

Fichier

Gerjets-peter-2000.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00190438, version 1

Collections

TELEARN | TICE

Citation

Peter Gerjets, Katharina Scheiter, Werner H. Tack. Resource-adaptive Selection of Strategies in Learning from Worked-Out Examples. L. R. Gleitman & A. K. Joshi. Twenty Second Annual Meeting of the Cognitive Science Society (CogSci2000), 2000, Philadelphia, United States. Mahwah, NJ: Erlbaum, pp.161-171, 2000. 〈hal-00190438〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

255

Téléchargements de fichiers

164

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD