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Mem Cognit. 2014 Jul;42(5):729-41. doi: 10.3758/s13421-014-0394-1.

Contracting, equal, and expanding learning schedules: the optimal distribution of learning sessions depends on retention interval.

Author information

1
Center for Integrative Research on Cognition, Learning, and Education (CIRCLE), Washington University in St. Louis, One Brookings Drive, St. Louis, MO, 63130, USA, carolina.kuepper-tetzel@wustl.edu.

Abstract

In laboratory and applied learning experiments, researchers have extensively investigated the optimal distribution of two learning sessions (i.e., initial learning and one relearning session) for the learning of verbatim materials. However, research has not yet provided a satisfying and conclusive answer to the optimal scheduling of three learning sessions (i.e., initial learning and two relearning sessions) across educationally relevant time intervals. Should the to-be-learned material be repeated at decreasing intervals (contracting schedule), constant intervals (equal schedule), or increasing intervals (expanding schedule) between learning sessions? Different theories and memory models (e.g., study-phase retrieval theory, contextual variability theory, ACT-R, and the Multiscale Context Model) make distinct predictions about the optimal learning schedule. We discuss the extant theories and derive clear predictions from each of them. To test these predictions empirically, we conducted an experiment in which participants studied and restudied paired associates with a contracting, equal, or expanding learning schedule. Memory performance was assessed immediately, 1 day, 7 days, or 35 days later with free- and cued-recall tests. Our results revealed that the optimal learning schedule is conditional on the length of the retention interval: A contracting learning schedule was beneficial for retention intervals up to 7 days, but both equal and expanding learning schedules were better for a long retention interval of 35 days. Our findings can be accommodated best by the contextual variability theory and indicate that revisions are needed to existing memory models. Our results are practically relevant, and their implications for real-world learning are discussed.

PMID:
24500777
DOI:
10.3758/s13421-014-0394-1
[Indexed for MEDLINE]

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