Cognitive Science 42 (S3):644-691 (2018)

M. Walsh
University of Notre Dame
The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation. Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought—Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides.
Keywords Computational model  Education  Predictive Performance Equation  Spacing effect
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DOI 10.1111/cogs.12602
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Implicit Learning and Tacit Knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
Maintenance of Knowledge: Questions About Memory We Forgot to Ask.Harry P. Bahrick - 1979 - Journal of Experimental Psychology: General 108 (3):296-308.

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