Allison L. Bruhn

A Preliminary Investigation of Emotional and Behavioral Screening Practices in K–12 Schools

Recently, the Council for Children with Behavioral Disorders (CCBD) highlighted the need to “actively screen for those in need of mental health services.” Questions remain, however, about the prevalence of screening and associated processes. The purpose of this study was to gather initial evidence through an electronic survey about current screening practices such as the types of screeners being used in K-12 schools and barriers to implementation. Participants in the study were 454 school or district-level administrators representing a range of school levels, locales, and socioeconomic levels. Data were analyzed using descriptive statistics and the chi-squared test of goodness-of-fit statistic. Only 12.6% of respondents indicated their school or district conducted schoolwide emotional or behavioral screening (SEBS), with access-and awareness-related issues cited as reasons for not conducting SEBS. Results of this preliminary investigation are discussed in terms of CCBD’s goals, and with caution, recommendations for research and practice are offered.

A Review of Tier 2 Interventions Conducted within Multitiered Models of Behavioral Prevention.

To support students’ academic, behavioral, and social needs, many schools have adopted multitiered models of prevention. Because Tier 3 interventions are costly in terms of time and resources, schools must find efficient and effective Tier 2 interventions prior to providing such intense supports. In this article, we review the literature base on Tier 2 interventions conducted within the context of multitiered prevention models evidencing a Tier 1 behavioral plan. Article selection and coding procedures are described and results are presented. Finally, we summarize our findings of four research questions, reflect on limitations, and offer suggestions for future inquiry.

Self-Monitoring Interventions for Students with Behavior Problems: A Systematic Review of Current Research:

Explicitly teaching skills associated with self-determination has been promoted to support students’ independence and control over their own lives. This is especially important for students with behavior problems. One self-determination skill or behavior that has been studied widely is self-monitoring. Although multiple reviews of various self-monitoring interventions exist, we provide an updated review of the literature focusing on the role various elements such as reinforcement, feedback, function, and technology play in self-monitoring interventions for students with behavior problems. In this review, we synthesize 41 recent (2000–2012) studies of self-monitoring interventions conducted with K–12 students exhibiting persistent behavior problems. Key findings, limitations, and implications for research and practice are discussed.

Improving Behavior by Using Multicomponent Self-Monitoring within a Targeted Reading Intervention:

Many researchers have documented the interrelatedness of reading and behavior (McIntosh, Sadler, & Brown, 2012). Thus, research examining the best way to intervene with students who exhibit problems in both skill sets is merited. Recently, taking an integrated approach to reading and behavioral intervention has been suggested (Mooney, Ryan, Uhing, Reid, & Epstein, 2005; Stewart, Benner, Martella, & Marchand-Martella, 2007). In this study, we examined the effects of integrating a multicomponent self-monitoring intervention into a targeted reading classroom. Specifically, we used an ABAB withdrawal design (Kennedy, 2005) to determine the presence of a functional relation between a multicomponent self-monitoring intervention and the academic engagement and disruptive behavior of two middle school girls with reading and behavioral problems. Limitations as well as implications for research and practice are included.

A Tier 2 Framework for Behavior Identification and Intervention

B ecause educators are called to meet all students’ academic and behavioral needs in a comprehensive, systematic way, thousands of schools across the country have implemented multitiered systems of support such as response to intervention and positive behavioral interventions and supports (PBIS). Central to these proactive, tiered systems is the use of a problem-solving protocol for assessing student response to highquality instruction and intervention. The goal is that data will be used to guide educational programming that matches students’ needs and abilities through multiple tiers, or levels, of support. In PBIS, the Tier 1 level of prevention involves explicit teaching of three to five schoolwide behavioral expectations. Once these expectations have been taught, modeled, and practiced, students earn reinforcement (e.g., praise, tokens) for meeting or exceeding behavioral expectations. When implemented with fidelity, approximately 80% of a school’s student population should have their behavioral needs met by the system’s schoolwide, Tier 1 level of support (Lewis & Sugai, 1999). A student support team (SST), often comprising a few teachers, a school psychologist, and an administrator, uses data to determine which students are making adequate progress with exposure to Tier 1 only. Students who are not making adequate progress or demonstrate problems meriting further support beyond well-implemented Tier 1 programming may be eligible for a Tier 2 intervention in addition to Tier 1 supports. Tier 2 interventions, which usually apply to 10% to 15% of the school population, include smallgroup social skills instruction, selfregulation strategies, and, most commonly, ‘‘check-in/check-out’’ (CICO). Tier 2 interventions are designed to be highly efficient, and generally they are provided to small groups of students exhibiting comparable problems. Progress is monitored more frequently in Tier 2 than in Tier 1 to efficiently determine student responsiveness to core plus intervention programming. Approximately 5% of the school’s student population may require individualized Tier 3 supports due to more complex problems and extensive behavioral histories. Function-based interventions, a Tier 3 support, require data gathered from a variety of people (e.g., parents, teachers, student, school psychologist) using multiple methods (e.g., interviews, direct observations, rating scales) to guide development of a multicomponent intervention. This process is time-, resource-, and labor-intensive, as are other Tier 3 supports such as mental health counseling and wraparound services. Therefore, schools must first ensure that effective, evidence-based Tier 2 interventions are being implemented with fidelity in an effort to ensure that resources are allocated appropriately. One critical component of effective PBIS frameworks is the implementation of data-based decision making. Collecting data serves multiple purposes including (a) determining the effects of an intervention or instruction; (b) providing formative and summative evaluation; (c) making decisions about the allocation of school-based services; and (d) promoting communication among parents, teachers, students, and other school personnel (Alberto & Troutman, 2009). At Tier 1, data such as the number of office discipline referrals (ODR) and/or scores from universal screeners may be used to identify students who need more targeted interventions. Within Tier 2 and 3 supports, more frequent assessment occurs to help determine students’ response to intervention. The idea is that when data are used to guide intervention and instructional decisions, students are more likely to experience positive outcomes. In this article, a framework, or step-by-step process, for data-based decision making within Tier 2 is presented with the goal of improving the effectiveness and efficiency of Tier 2 identification and intervention for students with challenging behavior. The suggested framework is predicated on (a) the provision of high-quality Tier 1 prevention efforts implemented with integrity and (b) the use of a psychometrically sound behavioral rating scale, the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997), for identifying specific areas of need. The logic of the framework is that to accurately place a student in a Tier 2 intervention, the SST must consider the nature and intensity of the student’s behavior and use data to match the student to an appropriate intervention. In the following section, the five-step process and additional considerations for implementing this framework are described.

Using a Changing-Criterion Design to Evaluate the Effects of Check-In/Check-Out with Goal Modification.

Check-in/check-out (CICO) is a Tier 2 behavioral intervention that has demonstrated effectiveness for students with challenging behavior in a variety of educational settings. Existing research has focused primarily on testing the intervention’s effectiveness and the role of behavioral function in moderating response to intervention. Only a handful of studies have included examinations of different procedures for fading CICO to promote maintained behavioral change. These have included decreasing the frequency of teacher feedback, using a mystery motivator to thin the schedule of reinforcement, and increasing goal contingencies over time. In this study, two middle school females with conduct problems were identified for participation in CICO with goal modification. During intervention, the first modification entailed setting the CICO card goal for each participant based on averages obtained on the CICO card during baseline rather than the commonly used 80% goal. Then, predetermined decision rules about how to modify subsequent goals were executed according to student progress. Effects on student behavior were evaluated using a changing-criterion with withdrawal design, and scores on the CICO card improved and classroom problem behavior decreased for both participants. Limitations and recommendations for future research are discussed.

Using Data to Individualize a Multicomponent, Technology-Based Self-Monitoring Intervention

Technology in schools is abundant as is the call for evidence-based interventions for students who need additional support to be successful. One promising use of technology is for self-monitoring interventions aimed at improving classroom behavior. In this study, two middle school students with disabilities used a multicomponent, self-monitoring app on an iPad during their reading classes. Using a data-based individualization approach, teachers worked with the primary investigator to monitor students’ response to the intervention and adapt the intervention accordingly. A single-subject design was used to test the effects of the intervention, and a functional relation was established for both participants who improved their academic engagement and decreased their disruptive behavior. Additionally, participants indicated the intervention was socially valid. Limitations, implications, and future directions are discussed.

“I Don’t Like Being Good!” Changing Behavior With Technology-Based Self-Monitoring

Self-monitoring has a well-established literature base for improving the behavior of students with a range of ages and abilities. Whereas self-monitoring often involves technology for prompting self-monitoring procedures, to date, only a few studies have examined the use of technology for recording self-monitored behavior. To extend the literature in this area, the effects of technology-based self-monitoring were examined using an iPad application called SCORE IT in which students and teachers rate students’ behavior and view automated graphs of progress toward goals. Using a baseline and intervention (ABAB) design to measure outcomes, improvements in behavior were established for both middle school participants—one with attention deficit hyperactivity disorder and another receiving noncategorical special education services for reading, math, and behavioral deficits. Findings, limitations, and recommendations for future research are discussed.

Goal-Setting Interventions for Students with Behavior Problems: A Systematic Review:

Students with persistent behavior problems, including those with or at risk for emotional or behavioral disorders, often struggle to be self-regulated learners. To improve self-regulation skills, numerous strategies have been suggested, including goal setting. Whereas goal setting has focused mostly on academic and life skills, behavioral goal setting has received less attention, particularly in terms of determining best practices for effective goal setting in school-based interventions. Thus, the purpose of this review was to examine the existing literature on interventions using behavioral goal setting alone and behavioral goal setting as part of a multicomponent intervention for students with persistent behavior problems. Findings from 40 studies are discussed in terms of participants, setting, design, measures, intervention components, and outcomes with a specific focus on directions for future research and implications for practice.

A Step-By-Step Guide to Tier 2 Behavioral Progress Monitoring:

Students who are at risk for or show low-intensity behavioral problems may need targeted, Tier 2 interventions. Often, Tier 2 problem-solving teams are charged with monitoring student responsiveness to intervention. This process may be difficult for those who are not trained in data collection and analysis procedures. To aid practitioners in these worthwhile tasks, we offer a step-by-step guide to collecting and evaluating Tier 2 behavioral progress monitoring data. This systematic approach includes (a) selecting an appropriate method of measurement, (b) planning for data collection and evaluation, (c) collecting and analyzing data, (d) considering treatment fidelity, and (e) adjusting intervention based on student responsiveness. Each step is described in detail with specific examples and additional resources are provided.