Similarity matters: A meta-analysis of interleaved learning and its moderators

Psychol Bull. 2019 Nov;145(11):1029-1052. doi: 10.1037/bul0000209. Epub 2019 Sep 26.


An interleaved presentation of items (as opposed to a blocked presentation) has been proposed to foster inductive learning (interleaving effect). A meta-analysis of the interleaving effect (based on 59 studies with 238 effect sizes nested in 158 samples) was conducted to quantify the magnitude of the interleaving effect, to test its generalizability across different settings and learning materials, and to examine moderators that could augment the theoretical models of interleaved learning. A multilevel meta-analysis revealed a moderate overall interleaving effect (Hedges' g = 0.42). Interleaved practice was best for studies using paintings (g = 0.67) and other visual materials. Results for studies using mathematical tasks revealed a small interleaving effect (g = 0.34), whereas results for expository texts and tastes were ambiguous with nonsignificant overall effects. An advantage of blocking compared with interleaving was found for studies based on words (g = -0.39). A multiple metaregression analysis revealed stronger interleaving effects for learning material more similar between categories, for learning material less similar within categories, and for more complex learning material. These results are consistent with the theoretical account of interleaved learning, most notably with the sequential theory of attention (attentional bias framework). We conclude that interleaving can effectively foster inductive learning but that the setting and the type of learning material must be considered. The interleaved learning, however, should be used with caution in certain conditions, especially for expository texts and words. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Attention
  • Concept Formation
  • Humans
  • Learning*
  • Models, Theoretical
  • Multilevel Analysis
  • Teaching