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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.

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.

School-Wide Positive Behavior Interventions and Supports (SWPBIS) relies on effective implementation of Tier 1 practices to ensure accurate identification of students in need of more intensive supports at Tier 2 or Tier 3. While measures of school-level fidelity are widely used, measures of classroom-level implementation of Tier 1 supports are less common. If classroom levels of Tier 1 supports are variable, schools may identify students for Tier 2 supports when, instead, teachers need support implementing Tier 1 in classrooms. The purpose of this case study was to assess the impact of a self-monitoring intervention, Monitoring Behavior on the Go (MoBeGo), on the academic engagement and disruptive behavior of a middle school student. Initially, the impact of the self-monitoring intervention was inadequate. In the context of evaluating the impact of a Tier 2 intervention, we identified an opportunity to assess whether a class-wide Tier 1 program, Class-Wide Function-Related Intervention Teams (CW-FIT), enhanced the effectiveness of the Tier 2 intervention. Using an A-B-BC-B-BC design, we compared the effectiveness of a Tier 2 intervention alone with a combined Tier 1 + Tier 2 intervention. When the class-wide Tier 1 program was layered on top of the Tier 2 intervention, the student's academic engagement showed an increase in level and stability.

Check-in check-out (CICO) is a widely implemented program aimed at increasing prosocial behavior. Aligned with the logic of Tier 2 supports, CICO has a standard protocol meant to be implemented in a standardized fashion across all students. However, as CICO is not effective for all at-risk students, it is quite common to find researchers implementing adapted versions of CICO. The purpose of this review was to explore the types of, rationale for, and timing of adaptations made to CICO. Results showed that 71% of studies using CICO made adaptations to core components. While most of these adaptations were made at the onset of treatment, we found little evidence to suggest researchers had a systematic process for selecting adaptations. As schools continue to seek the most efficient and effective means to provide behavior supports to students, adaptive intervention planning is an important process to consider.

Research indicates that intensification in multitiered systems of support (MTSS) can prevent problem behavior and academic deficits from worsening and ensure that students with the most intensive needs receive the most appropriate supports. In MTSS, it is important to consider evidence-based practices that teachers can intensify to improve student responsiveness to intervention. One such practice is opportunities to respond (OTRs). Research finds that increased OTRs are associated with improved academic outcomes and reduced disruptive behaviors. Previous reviews assessed the impact of increased teacher-directed OTRs on student outcomes and focused on specific populations of students. This meta-analysis extends findings of previous reviews by specifically assessing the utility of increasing OTRs as a method to intensify interventions. Results extend the current literature base by identifying specific interventions associated with increased OTRs that may be feasible to implement as methods to intensify interventions across the school day for students with academic and behavioral deficits.

Multi-tiered systems of support are frequently used in school settings because of their efficiency and effectiveness (McIntosh & Goodman, 2016). The rationale for multi-tiered systems of support is to provide early and systematic intervention to all students with increasing intensity of intervention provided as needed (Sugai & Horner, 2009). These systems generally offer three levels of support in which Tier 1 is applied with all students in a school, Tier 2 is used with about 15% of students who are at risk for more serious concerns, and Tier 3 is reserved for 3-5% of the school population with the most serious academic or behavior problems. Tier 2 supports have been defined as interventions that are standardized across students and are quickly and continuously available (Hawken, Adolphson, MacLeod, & Schumann, 2009). One common Tier 2 intervention is Check In/Check Out (CICO). As with all Tier 2 interventions, CICO is designed to reduce mild problem behavior and increase academic engagement (Crone, Hawken, & Horner, 2010). Although Tier 2 support was initially conceptualized as employing standardized intervention protocols for ease of implementation, researchers and practitioners have begun to make adaptations to standard protocols to increase their effectiveness (e.g., Boyd & Anderson, 2013; Kilgus, Fallon, & Feinberg, 2016; Melius, Swoszowski, & Siders, 2015). Adaptations are changes to core components of the standard protocol (Majeika et al., in press). These changes require a problem solving process to tailor a program so that it is better aligned with student needs. After providing an overview of CICO, this article addresses how adaptations can be beneficial, how they have been made, and when they should be made. Additionally, it discusses ways to adapt CICO, modifying the intensity or dosage of CICO, and individualizing components to meet students' needs. Classroom case studies and steps to fading CICO are also described.

Response to intervention (RTI) is a method for providing academic support to students and for identifying specific learning disabilities (SLDs). Using interrupted time series and hazard models, we examined if statewide RTI adoption in the U.S. state of Tennessee was associated with changes in rates of SLD and first-time SLD identification in elementary schools, and if these associations varied across student groups. Response to intervention was associated with an initial decline in the percentage of students with SLD in the state that continued over time, with larger decreases for students who were Black or economically disadvantaged. Response to intervention was associated with a 61% average decrease in the odds of first-time SLD identification by third grade (about a 0.006 change in the predicted probability), with greater declines for students who were Black or economically disadvantaged. We discuss these results in the context of disproportionality in special education and the need for research examining whether declines in SLD were due to improved academic outcomes, as opposed to delays in identification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

School-Wide Positive Behavior Interventions and Supports (SWPBIS) relies on effective implementation of Tier 1 practices to ensure accurate identification of students in need of more intensive supports at Tier 2 or Tier 3. While measures of school-level fidelity are widely used, measures of classroom-level implementation of Tier 1 supports are less common. If classroom levels of Tier 1 supports are variable, schools may identify students for Tier 2 supports when, instead, teachers need support implementing Tier 1 in classrooms. The purpose of this case study was to assess the impact of a self-monitoring intervention, Monitoring Behavior on the Go (MoBeGo), on the academic engagement and disruptive behavior of a middle school student. Initially, the impact of the self-monitoring intervention was inadequate. In the context of evaluating the impact of a Tier 2 intervention, we identified an opportunity to assess whether a class-wide Tier 1 program, Class-Wide Function-Related Intervention Teams (CW-FIT), enhanced the effectiveness of the Tier 2 intervention. Using an A-B-BC-B-BC design, we compared the effectiveness of a Tier 2 intervention alone with a combined Tier 1 + Tier 2 intervention. When the class-wide Tier 1 program was layered on top of the Tier 2 intervention, the student's academic engagement showed an increase in level and stability. [ABSTRACT FROM AUTHOR]

Check-in check-out (CICO) is a widely implemented program aimed at increasing prosocial behavior. Aligned with the logic of Tier 2 supports, CICO has a standard protocol meant to be implemented in a standardized fashion across all students. However, as CICO is not effective for all at-risk students, it is quite common to find researchers implementing adapted versions of CICO. The purpose of this review was to explore the types of, rationale for, and timing of adaptations made to CICO. Results showed that 71% of studies using CICO made adaptations to core components. While most of these adaptations were made at the onset of treatment, we found little evidence to suggest researchers had a systematic process for selecting adaptations. As schools continue to seek the most efficient and effective means to provide behavior supports to students, adaptive intervention planning is an important process to consider. [ABSTRACT FROM AUTHOR]