Sara C. McDaniel

The Effects of Tier II Check-in/Check-out Including Adaptation for Non-Responders on the Off-Task Behavior of Elementary Students in a Residential Setting.

This study evaluated the effects of a Tier II positive behavior interventions and supports (PBIS) intervention, Check-in/Check-out (CICO), on the off-task behavior of 4 students with behavioral challenges and special needs in a residential facility. In addition, the study examined the effects of additional mentor contact (i.e., mid-day check-up; Check-in/Check-up/Check-out; CICUCO) on the off-task behavior of a student who was nonresponsive to CICO. CICO produced decreases in the occurrences of off-task behavior in both CICO and CICUCO conditions with both noted as highly acceptable by school CICO mentors. Limitations and future directions are discussed.

Embedding the Positive Behavioral Interventions and Supports Framework Into the Complex Array of Practices Within Alternative Education Settings A Decision-Making Process

Alternative education (AE) programs and schools usually serve distinct populations of students with educational disabilities and mental health or other needs. AE program staff often employ a range of curricula, interventions, and strategies that form an eclectic approach to addressing student needs. This may result in practices that are misaligned, contraindicated, or improperly implemented and lead to poor outcomes. In addition, this eclectic approach may not be implemented in an organized, tiered manner that ensures all students’ access to a continuum of supports and services. In this article, a decision-making process for staff in AE settings to adopt and embed positive behavioral interventions and supports (PBIS) practices is presented. This process is rooted in the PBIS framework of systems, data, and practices, and in a public health model of team-based decision making. The authors submit that this approach could be used across a variety of AE program models.

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.

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.

Student Self-Graphing as a Strategy to Increase Teacher Effectiveness and Student Motivation.

M s. Wilson and Ms. Turner coteach third grade together. Their school is in its first year of implementing a comprehensive, integrated, three-tiered (CI3T) model of prevention to meet students’ academic, behavioral, and social needs. As part of their secondary intervention, Ms. Wilson and Ms. Turner have embedded timed, repeated reading to promote oral reading fluency in small guided reading groups for students who are identified as at risk. In an effort to track student progress, the schoolwide assessment team (SWAT) has asked teachers to collect curriculum-based measures (CBM) on reading fluency using Dynamic Indicators of Basic Early Literacy Skills (DIBELS). The teachers have been tracking their students’ progress via an online database. This coteaching pair also has three students who were identified as moderate risk on a systematic behavior screening. These students participate in Check-in, Checkout (CICO). Each student begins and ends each day with a positive contact with Ms. Turner, the CICO mentor. She provides each student with positive feedback and a new CICO daily point card and makes sure he or she is ready for his or her day (i.e., brought all materials to school). At the end of the day, the students check out with Ms. Turner. At this time, Ms. Turner graphs the students’ daily progress by recording the number of points earned in a Microsoft Excel database. In addition, the teaching pair has one student in their classroom who does not initiate social interactions. They decided to implement a social skills self-monitoring intervention to promote conversation skills. With DIBELS data, the CICO daily point card, self-monitoring sheet, and ongoing academic assessments, there was a plethora of data that needed to be collected, entered, and analyzed. In addition, the teachers felt as though the students would benefit from seeing and understanding their performance data. Ms. Wilson and Ms. Turner believed that their students were ready to take ownership of their progress. They set out to find a strategy to help manage their data and improve their students’ academic, behavioral, and social skills performance. They came across student self-graphing in their research. They wondered if student self-graphing would alleviate some of their tasks and if it is an appropriate strategy for their students. If so, what types of graphing systems should they employ? Many teachers face challenges similar to those of Ms. Wilson and Ms. Turner in their classrooms. Managing and analyzing multiple forms of data can be overwhelming. Perplexed by additional responsibilities (e.g., preparing for and participating in high-stakes testing, developing longterm standards-based lessons, attending staff meetings, educational conferences, and school programs), teachers are serving an increasingly diverse group of learners within the general education setting, including students with emotional and behavioral disorders (EBD). Students with EBD are at risk for school failure because of behavior, motivation, and learning deficits, but research has indicated that the negative effects of EBD can be mitigated through proactive academic, behavioral, and social skills interventions (Kauffman & Landrum, 2009). Many schools across the country are adopting multitiered models to identify students for appropriate interventions and monitor their progress. In general, multi-tiered models provide a continuum of support based on student need, with the goal being early detection and intervention for struggling students. Ms. Wilson and Ms. Turner’s school has chosen to implement a CI3T model of prevention (Lane, Kalberg, & Menzies, 2009). The CI3T model combines the academic framework of response to intervention, the behavioral framework of schoolwide positive behavior supports, and an additional social skills element. A multi-tiered model such as CI3T is a practical way for schools to support students and allocate school resources. The model is composed of three tiers of intervention: (a) primary or universal support (Tier 1), (b) secondary or targeted group interventions (Tier 2), and (c) tertiary or specialized individual interventions (Tier 3). The SWAT team monitors the progress and outcomes of the primary plan using academic and behavioral data. They also help teachers identify students for more targeted supports such as a supplemental reading program (e.g., Stepping Stones to Literacy; Nelson, Cooper, & Gonzalez, 2004), a targeted behavioral intervention (e.g., CICO; Crone, Horner, & Hawken, 2010), or a social skills intervention (e.g., Social Skills Improvement System; Elliott & Gresham, 2007). Specific entry (e.g., two office discipline referrals within 3 weeks or scoring at moderate risk on the Student Risk Screening Scale (SRSS) (Drummond, 1994) and exit criteria (e.g., zero office referrals for 2 months or screening at low risk on the SRSS) are used to determine the students’ response to the intervention STUDENT SELF-GRAPHING

School-Wide Positive Behavioral Interventions and Supports in a Residential School for Students With Emotional and Behavioral Disorders: First Years of Implementation and Maintenance Follow-Up Focus Groups

Students with emotional and behavioral disorders (E/BD) often receive educational services delivered in more restrictive settings. Positive behavioral interventions and supports (PBIS) is a framework that may address the complex needs of these students in these restrictive settings. This article describes the training and technical support provided to a residential school serving students with E/BD as they implemented school-wide PBIS (SWPBIS) over several years and when the external support was removed, follow-up focus groups of school staff were conducted. Results across three 6-month periods indicate a reduction in the number of discipline referrals and high levels of fidelity of implementation of SWPBIS when external support was provided. When the external supports were removed, the number of discipline referrals increased and the level of fidelity decreased. Several themes related to SWPBIS from the focus groups were identified. A brief discussion follows with how external support was reintroduced.

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.

Creating Smarter Classrooms: Data-Based Decision Making for Effective Classroom Management.

M rs. Maxwell is a third grade general education teacher who is consistently having problems with one of her students with challenging behaviors, Chris. Chris is very disruptive and his disruptions are affecting Mrs. Maxwell’s instruction and the learning opportunities of her other students. Mrs. Maxwell has decided that she needs to develop a framework for monitoring data on Chris’s classroom behavior so that she can effectively address his problem behaviors. The term ‘‘data-based decision making’’ (DBDM) has become pervasive in education and typically refers to the use of data to make decisions in schools, from assessment of an individual student’s academic progress (Fuchs, Fuchs, Hamlett, & Stecker, 1991) to whole-school reform efforts (Horner, Sugai, & Andersen, 2010). School improvement through DBDM has been a long-standing goal of teachers, administrators, and policymakers, and ensuring the quality and utility of available data to inform education-related decisions is a key part of the No Child Left Behind Act of 2001 and subsequently, the Individuals with Disabilities Education Act of 2004. The elegance of DBDM is that it allows us to partition our bias. We all have inherent values and beliefs that influence our judgment and decisions. We ‘‘know’’ good behavior and who is or is not meeting our classroom behavioral expectations (e.g., raising a hand before responding, reading quietly during silent reading). The idea that ‘‘I get all the information I need from my own observation’’ is a false assumption many teachers hold. Research, including action research, is about experience, but it’s also about the quantification of experience through measurement, the outcome of which is data. Our jobs as action researchers or evaluators of our own behavior and our students’ behavior then are to ‘‘prove’’ that our intuition and observations are accurate and that our judgments and decisions are justified using data. As W. Edwards Deming famously quipped, ‘‘In God we trust, all others must bring data.’’ Put simply, we need data to make decisions we can trust. This is the foundation of DBDM. Although DBDM is widely advocated, research suggests that a lack of skill in collecting, interpreting, and using data is a barrier to frequent and systematic use (Sandall, Schwartz, & LaCroix, 2004). Further, research indicates that educators are ‘‘afraid’’ of data; afraid of what it might reveal or the work entailed to build the capacity to collect and use data in everyday practice (Crum, 2009). Our goal is to address these concerns and advocate the use of DBDM for effective classroom management.

Barriers and Facilitators to Integrating SWPBIS in Alternative Education Settings With Existing Behavior Management Systems

Alternative education (AE) settings such as residential and juvenile justice facilities and self-contained schools are complex settings for students with unique academic and behavioral needs. The schoolwide positive behavioral interventions and support (SWPBIS) model has proven utility in traditional schools, but little research exists to inform SWPBIS implementation in AE settings. To address this research gap, the researchers conducted two separate focus groups with similar AE settings regarding their integration of SWPBIS with an existing behavior management system. Resulting themes between and within groups are presented in terms of systems, data, and practice. The two settings discussed similar challenges, although their responses to these challenges were in stark contrast, as were the resulting success of SWPBIS integration. Limitations and future directions for researchers are described in addition to specific policy implications.

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.