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

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.

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Peter Gerjets, Katharina Scheiter, Werner H. Tack. Resource-adaptive Selection of Strategies in Learning from Worked-Out Examples. Twenty Second Annual Meeting of the Cognitive Science Society (CogSci2000), 2000, Philadelphia, United States. pp.161-171. ⟨hal-00190438⟩