Leveraging Item Parameter Drift to Assess Transfer Effects in Vocabulary Learning

This study introduces a novel analytic approach for examining transfer effects in vocabulary learning by leveraging item parameter drift (IPD) within longitudinal item-level data. Using the Explanatory Item Response Model (EIRM), the authors demonstrate how changes in item difficulty over time—often treated as a measurement nuisance—can instead reveal meaningful patterns in how students learn different types of vocabulary. Through simulation, the study shows that ignoring IPD can bias estimates and underestimate uncertainty, particularly for growth parameters. The empirical application draws on data from a randomized controlled trial of the Model of Reading Engagement (MORE) intervention, analyzing students’ performance on vocabulary items administered in Grades 2 and 3. Results reveal substantial IPD, with stronger and more persistent treatment effects on untaught (far-transfer) vocabulary words than on explicitly taught words, and effects that persisted over a 12-month follow-up period. By modeling item-level growth, the study demonstrates that MORE fosters durable, transferable vocabulary knowledge and illustrates how EIRM can provide a more fine-grained, generalizable understanding of learning processes than total-score analyses alone.