Treatment Components and Participant Characteristics Associated with Outcomes in Self-Monitoring Interventions

Self-monitoring is one of the most widely used and widely researched strategies for improving student behavior. However, specific research-based guidance about how to design effective self-monitoring interventions and to whom they should be delivered does not yet exist. To this end, we examined how various treatment components and participant characteristics moderated response to self-monitoring interventions. We included 66 single-case studies on academic engagement and 21 single-case studies on disruptive behavior. These studies included 290 participants with challenging behavior, 183 of whom had a disability. After extracting raw data from original studies, we analyzed data using multilevel modeling for each dependent variable (i.e., academic engagement, disruptive behavior). Across both dependent variables, student age and educational setting impacted treatment effects, as did the inclusion of goal-setting, feedback, and reinforcement. Based on our findings, we describe implications related to designing self-monitoring interventions. We also discuss limitations and future directions.