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.
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The Xs indicate different learning sessions, and Test represents the final test session. Subscripts symbolize the contextual components stored in memory and present at the final test. Hyphens represent time.
Of the 25 participants who did not complete all experimental sessions, six were in the expanding–35-day RI condition, five were in the equal–7-day RI condition, and four were in the expanding–0-day RI condition. The rest were evenly distributed (one or two per condition) across all other conditions, except for the expanding–7-day RI condition, which had no dropouts.
Free-recall performance at the end of the initial learning session also did not differ between the three learning schedules (contracting, M = 53 %, SD = 18 %; equal, M = 51 %, SD = 19 %; expanding, M = 52 %, SD = 18 %), F(2, 207) = 0.13, p = .875, η p 2 = .001.
To revisit, the lags between the initial learning session and Learning Session 2 were 5, 3, and 1 day(s) for the contracting, equal, and expanding learning schedules, respectively. The lags between Learning Session 2 and Learning Session 3 were 1, 3, and 5 day(s) for the contracting, equal, and expanding learning schedules, respectively.
Whenever Levene’s test indicated that the assumption of homogeneity of variances had been violated, the degrees of freedom were adjusted accordingly to account for unequal variances.
We created two meaningful contrasts that were tailored to our hypotheses. The first contrast was contracting = –2, equal = 1, and expanding = 1, and the second contrast was contracting = 0, equal = –1, and expanding = 1.
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The first author acknowledges support from the Ontario/Baden-Württemberg Faculty Mobility Program of the Ministry for Science, Research, and Arts of Baden-Württemberg, Germany, and from a postdoc fellowship from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). We are grateful to the Faculty of Health at York University in Toronto, Canada, for supporting this project with a Minor Research Grant and an SSHRC Small Grant. We thank Masa Calic, Suzette Fernandez, and Ariella Winter for their assistance with data collection, and Tina Weston for helpful comments on a previous draft of this article.
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Küpper-Tetzel, C.E., Kapler, I.V. & Wiseheart, M. Contracting, equal, and expanding learning schedules: The optimal distribution of learning sessions depends on retention interval. Mem Cogn 42, 729–741 (2014). https://doi.org/10.3758/s13421-014-0394-1
- Memory models
- Long-term retention
- Distributed practice
- Learning schedule
- Theory evaluation