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.

Data-based individualization (DBI) is a systematic process used to guide teachers in making decisions related to students’ responsiveness to intervention. Whereas this process has been used extensively with academic interventions, far less is known about DBI used within the context of behavioral interventions. In this study, elementary general and special education teachers (a) implemented a technology-based, self-monitoring intervention with students exhibiting challenging behavior; and (b) used DBI to evaluate student progress and make intervention adaptations accordingly. Results of multilevel modeling indicated students improved their positive behavior significantly ( p < .001) from baseline to intervention. For most students, once they began intervention, positive behaviors either maintained or increased gradually when teachers made adaptations to the self-monitoring intervention. In addition to these results, an analysis of the effects of different intervention adaptations (e.g., raising or lowering goals, increasing or decreasing interval length) and visual analysis of individual students’ response are discussed.

Positive behavioral interventions and supports (PBIS) is a three-tiered framework for preventing problem behavior and intervening for students at risk for or with problem behavior (Sugai & Horner, 2006). This research-based framework, which is implemented in more than 23,000 schools across the country, hinges on the core components of systems, data, and practice within and across tiers (Horner, Sugai, & Fixsen, 2017). Tier 1 is a schoolwide, systems-based approach to defining and teaching behavioral expectations, recognizing prosocial behavior, and correcting problem behavior. Tier 1 programs and practices have resulted in higher academic achievement, fewer discipline problems, and improved school climate (Horner et al., 2017). In Tier 2, targeted supports are provided to students at risk for or with low-level problem behavior by matching efficient research-or evidence-based interventions with the corresponding problem behavior. Tier 2 interventions can be delivered in small groups or individually. Research indicates that many students have improved their behavior (e.g., on-task, disruption, inappropriate language) significantly by participating in Tier 2 interventions such as check-in/ check-out (CICO), self-management strategies, and social skills instructional groups (Bruhn, Lane, & Hirsch, 2014). Tier 3 is reserved for students with persistent and resistant challenging behavior and involves intensive, individualized, function-based behavioral interventions. Function-based interventions have demonstrated effectiveness for a range of students across the grade span in both general and special education settings (Gage, Lewis, & Stichter, 2012). In all tiers, data are collected to guide decision making with the goal of improving student outcomes. These data may be reviewed by one school-site team, or there may be a team assigned to monitor data at each tier. These problemsolving teams generally consist of an administrator and a range of teachers from different content areas (e.g., special education, general education, related arts) and grade levels. School counselors, psychologists, behavior specialists, and social workers also may be on the team. In Tier 1, implementation fidelity data (e.g., Schoolwide Evaluation Tool; Sugai, Lewis-Palmer, Todd, & Horner, 2001), schoolwide discipline data (e.g., number of office referrals, location, and time of day of behavior incidents), and behavioral screening data are used to (a) determine areas of effectiveness and those areas needing improved Tier 1 delivery, and (b) identify students who may require additional support at the Tier 2 level. At Tier 2, data from validated assessments (e.g., Strengths and Difficulties Questionnaire [SDQ]; Goodman, 1997) are needed to identify specific behavioral deficits which can be matched to appropriate Tier 2 interventions, and also monitor student responsiveness throughout Tier 2 implementation (McDaniel, Bruhn, & Mitchell, 756984B BXXXX10.

An emerging body of research shows Tier 1 Positive Behavioral Interventions and Supports (PBIS) can be successfully implemented in high schools to improve school climate and graduation rates and reduce problem behaviors. However, high schools are often hesitant to adopt PBIS because of contextual barriers such as school size, organizational culture, and student developmental level. Resistance to high school implementation is also related to teachers perceiving PBIS as less socially valid for high school students. Although previous systematic reviews of Tier 1 have examined implementation and effects, none have exclusively focused on the unique contextual needs related to high school implementation. In this review, we synthesized 16 published research studies conducted at the high school level, described how authors addressed the unique challenges of implementing PBIS in high schools, reported findings related to academic and behavioral outcomes, and made recommendations for future research and practice based on our findings.

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Delivering high rates of opportunities to respond (OTR) has been shown to improve student outcomes for students with challenging behavior. High rates of OTR can maximize student engagement while providing ways for teachers to quickly assess student content mastery. Given the increasing trend of technology use in schools, teachers can leverage technology for delivering OTR. We discuss three technology-based tools teachers can use to enhance OTR delivery with examples of how to incorporate these tools into classrooms.

High school students, particularly those with disabilities, often struggle with academic engagement in general education classes. One empirically supported method for improving academic engagement includes self-regulation interventions. However, individual effects of frequently used self-regulation strategies’ goal setting and self-monitoring remain unknown. To address this issue, we implemented a multiple-probe single-case design with embedded alternating treatments to evaluate the differential impact of behavioral goal reminders and self-monitoring. The sample included three high school students with emotional or behavioral disorders (EBD) in general education classes who also received special education services. Innovative approaches to intervention design included student-determined behavioral goals and self-monitoring procedures informed by triangulated behavioral assessment data. Although visual analysis indicated only moderate improvements in academic engagement, and no functional relations were determined for either the multiple-probe or alternating treatments designs, the intervention was rated with high social validity from teachers and students, and the study represents an exemplar of high-quality special education single-case research. Practical implications, limitations, and directions for future research are discussed.

The purpose of this study was to examine the effects of MoBeGo, a mobile self-monitoring app, on the initial and sustained academic engagement and disruptive behavior of third- to eighth-grade students with challenging behavior. Student–teacher pairs ( N = 57) were randomly assigned to the treatment (MoBeGo) or control (business-as-usual) condition. We conducted systematic direct observation of students’ behavior throughout prebaseline, baseline, intervention, and postintervention conditions of the study. Multivariate multilevel models revealed differential improvement for the MoBeGo group in student outcomes (less disruptive behavior; more academic engagement) from baseline to intervention, as well as successful postintervention effects for disruptive behavior. Limitations, future directions, and implications for practice are discussed.