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Cited by 196

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Using a model to compute the optimal schedule of practice.

Open access
Pavlik, Philip I., Anderson, John R.·2008·Journal of Experimental Psychology: Applied

Abstract

By balancing the spacing effect against the effects of recency and frequency, this paper explains how practice may be scheduled to maximize learning and retention. In an experiment, an optimized condition using an algorithm determined with this method was compared with other conditions. The optimized condition showed significant benefits with large effect sizes for both improved recall and recall latency. The optimization method achieved these benefits by using a modeling approach to develop a quantitative algorithm, which dynamically maximizes learning by determining for each item when the balance between increasing temporal spacing (that causes better long-term recall) and decreasing temporal spacing (that reduces the failure related time cost of each practice) means that the item is at the spacing interval where long-term gain per unit of practice time is maximal. As practice repetitions accumulate for each item, items become stable in memory and this optimal interval increases.

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Crossref · 10.1037/1076-898x.14.2.101

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Metadata

Authors
Pavlik, P. I., & Anderson, J. R.
Venue
Journal of Experimental Psychology: Applied
Published
2008-06
Volume
14
Issue
2
Pages
101-117
ISSN
  • 1076-898X (ISSN-L)
  • 1939-2192
OpenAlex
W2026480899
Cited by
196
References
6
FWCI
4.06
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Using a model to compute the optimal schedule of practice. https://app.cochat.ai/papers/10.1037/1076-898x.14.2.101
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Pavlik, P. I., & Anderson, J. R. (2008). Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied. https://doi.org/10.1037/1076-898x.14.2.101

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.