A classroom experiment compared rebuilding a whole mind map with rebuilding only its leaves.
A mind map is a thinking tool. It lays out understanding as branches, with a central idea at the root and related concepts spreading outward. People draw them to organise their own thinking, and they share them so others can follow along. The catch is that reading a map someone else built is not the same as understanding it.
The researchers borrowed a technique that already worked for concept maps: the recomposition method. A map is taken apart into its pieces, and the learner has to put it back together. Doing that requires actually knowing how the pieces relate. Applied to a mind map, the full version means decomposing the entire map and rebuilding all of it.
The authors proposed a lighter option. In the Leaves Recomposition method, students rebuild only the leaves, the endpoints at the edge of the map. Their reasoning is neat: to place a leaf correctly, you still need to understand the whole path that runs from the root to that leaf. So the thinking stays, but the typing shrinks.
The team ran comparative experiments between the two methods and measured students before and after. Both groups improved significantly. Just as importantly, the difference between the full method and the leaves method was not statistically significant, so the lighter version gave up nothing on the test scores.
The map scores told a second story. Growth rates were much higher for the leaves method than for the full one, and the questionnaire responses described the leaves version as the easier exercise. Students spent less effort and still built maps that moved closer to the target structure.
Two conclusions follow. Rebuilding a mind map has a measurable learning effect, and that effect survives when the exercise is made lighter. The authors frame this as reducing cognitive load while keeping the learning, which is a rare and useful combination in classroom tool design.
Kodai Watanabe et al. (2023). Application of the Recomposition Method to Mind Map and Experimental Verification of Learning Effect. Lecture Notes in Computer Science.
