Longitudinal Effects of Data-Based Instructional Changes for Students With Intensive Learning Needs: A Piecewise Linear–Linear Mixed-Effects Modeling Approach

For students with intensive learning needs for whom standard, validated interventions do not effectively promote academic growth, data-based instruction (DBI) is suggested as an effective, fine-grained approach to individualization. Key to DBI’s success is making instructional changes based on individual students’ progress monitoring data. The purpose of this study was to evaluate the effects of such instructional changes on student early writing outcomes. We applied a piecewise linear–linear mixed-effects (PLME) model to determine student writing growth trajectories before and after teachers introduced instructional changes. Using data from 46 elementary school students with intensive writing intervention needs, results showed that a PLME model with two segmented slopes—before and after the change—best explained students’ observed change in writing scores. Results also showed that a higher level of initial writing skills was associated with higher levels of intercepts and additional growth gains after the instructional change, whereas the type of instructional change was not associated with predicted writing trajectories. We discuss the implications of positive effects of teachers’ individualized timely decisions to change instruction using progress monitoring data as well as unexpected findings and study limitations such as small sample size and inconsistency in results.