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Enhancing learning and retention through the distribution of practice repetitions across multiple sessions.
Walsh, Matthew M; Krusmark, Michael A; Jastrembski, Tiffany; Hansen, Devon A; Honn, Kimberly A; Gunzelmann, Glenn.
Afiliação
  • Walsh MM; The RAND Corporation, Pittsburgh, PA, USA. mmw188@gmail.com.
  • Krusmark MA; Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, USA.
  • Jastrembski T; L3Harris Technologies, Wright-Patterson Air Force Base, Dayton, OH, USA.
  • Hansen DA; Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, USA.
  • Honn KA; Sleep and Performance Research Center, Washington State University Health Sciences, Spokane, WA, USA.
  • Gunzelmann G; Elson S. Floyd College of Medicine, Washington State University Health Sciences, Spokane, WA, USA.
Mem Cognit ; 51(2): 455-472, 2023 02.
Article em En | MEDLINE | ID: mdl-36190659
ABSTRACT
The acquisition and retention of knowledge is affected by a multitude of factors including amount of practice, elapsed time since practice occurred, and the temporal distribution of practice. The third factor, temporal distribution of practice, is at the heart of research on the spacing effect. This research has consistently shown that separating practice repetitions by a delay slows acquisition but enhances retention. The current study addresses an empirical gap in the spacing effects literature. Namely, how does the allocation of a fixed number of practice repetitions among multiple sessions impact learning and retention? To address this question, we examined participants' acquisition and retention of declarative knowledge given different study schedules in which the number of practice repetitions increased, decreased, or remained constant across multiple acquisition sessions. The primary result was that retention depended strongly on the total number of sessions in which an item appeared, but not on how practice repetitions were distributed among those sessions. This outcome was consistent with predictions from a computational cognitive model of skill acquisition and retention called the Predictive Performance Equation (PPE). The success of the model in accounting for the patterns of performance across a large set of study schedules suggests that it can be used to tame the complexity of the design space and to identify schedules to enhance knowledge acquisition and retention.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Retenção Psicológica / Aprendizagem Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Retenção Psicológica / Aprendizagem Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article