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The spacing effect stands up to big data

  • A. S. N. KimEmail author
  • A. M. B. Wong-Kee-You
  • M. Wiseheart
  • R. S. Rosenbaum
Article

Abstract

Many studies have shown that repetition of study material with temporal gaps between the repetitions (i.e., spaced in time) is more effective for long-term retention than are repetitions in immediate succession (i.e., massed; Greene, 1989). Although this spacing effect has proven to be robust in the laboratory (Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006), its status in the real world is relatively understudied. Other research has demonstrated the benefit of memory retrieval on subsequent retrieval of the same information (Bjork, 1975, 1988; Roediger & Karpicke, 2006), referred to as the testing effect. However, it is not clear how spacing and retrieval can be optimally combined in order to enhance knowledge retention in a real-world setting. To investigate this question, we analyzed longitudinal data from 10,514 individuals, collected in the context of naturally occurring workplace training. To determine the impact of spaced retrieval on knowledge retention, these data were analyzed using a generalized linear mixed model with the following fixed factors: (1) spacing interval between repetitions of content training (retrieval practice), (2) retention interval, and (3) question format. Random factors included the specific content on which employees were trained, which was clustered by employee and, in turn, by company, resulting in a three-level hierarchy. The results showed a significant interaction between spacing interval and retention interval: the optimal amount of spacing between repeated retrieval events increased as the retention interval increased. These findings are in line with the results of laboratory studies, demonstrating the relevance and transferability of laboratory-based research to real-world contexts.

Keywords

Episodic memory Spacing effect Distributed practice Retrieval practice Learning management system (LMS) Training and development Big data Generalized linear mixed model (GLMM) Real-world data 

Notes

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Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  • A. S. N. Kim
    • 1
    • 2
    Email author
  • A. M. B. Wong-Kee-You
    • 1
  • M. Wiseheart
    • 1
    • 3
  • R. S. Rosenbaum
    • 1
    • 2
    • 4
  1. 1.Department of PsychologyYork UniversityTorontoCanada
  2. 2.Rotman Research InstituteBaycrest Health SciencesTorontoCanada
  3. 3.LaMarsh Centre for Child and Youth ResearchYork UniversityTorontoCanada
  4. 4.Vision: Science to Applications (VISTA) ProgramYork UniversityTorontoCanada

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