Studying and Constructing Concept Maps: a Meta-Analysis

Abstract

A concept map is a node-link diagram in which each node represents a concept and each link identifies the relationship between the two concepts it connects. We investigated how using concept maps influences learning by synthesizing the results of 142 independent effect sizes (n = 11,814). A random-effects model meta-analysis revealed that learning with concept and knowledge maps produced a moderate, statistically significant effect (g = 0.58, p < 0.001). A moderator analysis revealed that creating concept maps (g = 0.72, p < 0.001) was associated with greater benefit relative to respective comparison conditions than studying concept maps (g = 0.43, p < 0.001). Additional moderator analyses indicated learning with concept maps was superior to other instructional comparison conditions, and was effective across science, technology, engineering, and math (STEM) and non-STEM knowledge domains. Further moderator analyses, as well as implications for theory and practice, are provided.