Dynamics of second-language learners' semantic memory networks: Evidence from a snowball sampling paradigm

This article provides an analysis of structural changes in second-language (L2)-based semantic memory networks-graphs composed of L2 words as nodes and semantic relations between them as edges, during L2 learning. We used snowball sampling paradigm to create individual semantic networks of participants divided into two groups differing in L2 learning time and then compare their structural characteristics cross-sectionally. The results showed that as L2 learning progresses, semantic memory networks tend to become more connected (by increasing the average node degree), more efficient (by decreasing the average shortest path length), less fragmented (by decreasing the modularity), less centralized (by decreasing the centralization), less dense (by decreasing the density), and no more "small-worlded" (by similar average clustering coefficients and small-world indices). The findings provide quantitative evidence of how the duration of L2 learning shapes the structure of L2-based semantic memory networks generated in the snowball sampling paradigm. (PsycInfo Database Record (c) 2025 APA, all rights reserved).