Longitudinal mentoring with school-based positive behavioral support teams: Influences on staff and learner behavior

Johnson, LeAnne D; Reichle, Joe; Monn, Emily

doi:10.1080/17489530903182152

Cited by 3 publications

(4 citation statements)

References 54 publications

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“…Extending recommendations made by Marsh and Farrell (2015), efforts to enhance the capacity of education systems to meaningfully use data for decision making may benefit from (a) coaches with content area expertise who are able to guide problem solving and enactment of solutions at a local classroom or program level, (b) data coaches or analysts who have a more singular focus on interpreting and using data, and (c) teams that are comprised of content and data coaches as well as others who seek to make systemic improvements based on knowledge gained through data. A substantial set of literature exists for how to prepare teams with the skills needed to use data within a data-based problem-solving process (Alonzo, Ketterlin-Geller, & Tindal, 2007; Deno, 2005; Johnson & Reichle Monn, 2009; Newton, Horner, Algozzine, Todd, & Algozzine, 2012). To enhance the efficacy of that preparation, a systematic Team-Initiated Problem Solving (TIPS) model with accompanying professional development was created to provide teams with a concrete structure for engaging in each step of a data-based problem-solving process (Newton et al, 2012; Todd et al, 2011).…”

Section: Introduction To the Problemmentioning

confidence: 99%

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

Johnson

2017

Topics in Early Childhood Special Education

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Multi-tiered systems of support (MTSS) provide frameworks to guide the instructional decisions and problem solving of educators (i.e., Sugai & Horner, 2009). Within MTSS, the availability of ongoing child assessment and progress monitoring data is described as a critical feature (Buzhardt et al., 2010; Sugai & Horner, 2009). For some time, there was an assumption that if tools were created to gather information about children's proficiency with and progress in developing essential learning targets, educators would be equipped to effectively engage in two essential components of instruction, data-driven decision making and ongoing problem solving (Hoogland et al., 2016; Marsh & Farrell, 2015; Todd et al., 2011). Unfortunately, multiple researchers have demonstrated that despite the availability of tools for gathering data, those data do not always lead to decision making about instructional practices that are in service of improving child outcomes (Heritage, Kim, Vendlinski, & Herman, 2009; Olah, Lawrence, & Riggan, 2010). The purpose of this article is to explore one approach to trying to address the problems associated with data-driven decision making in early childhood through use of cloudbased tools for gathering and summarizing data. To begin that exploration, a brief description will be provided that grounds the work in critical efforts to address the social, emotional, and behavioral development of young children. The complexities of the work in early childhood will then be described as a means for considering why existing tools may not be sufficient when promoting data-based problem solving. Then, a model for team-based problem solving will be described as a necessary approach to building knowledge about the use of data that meaningfully supports instructional decisions. Finally, a cloud-based knowledge management system will be described, along with the experience of a group of early childhood programs using the system, to demonstrate the potential benefits that that type of system may offer when the goal is to promote actual use of data, not just the gathering of data. Actualizing the use of data for effective decision making is a multifaceted issue. In addition to the barriers that are often expected during the installation of new practices, such as time and expertise (Anderson,

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Section: Introduction To the Problemmentioning

confidence: 99%

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

Johnson

2017

Topics in Early Childhood Special Education

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“…Some work has conserved the approach by continuing to calculate some form of descriptive degrees of overlap , including percentage of data points exceeding the median (PEM; Ma, 2006), percentage of zero data points (PZD; Johnson, Reichle, & Monn, 2009), and the percentage of all nonover-lapping data (PAND; Parker et al, 2007). Olive and Smith (2005) compared a set of descriptive effect size statistics (including a regression-based effect size, PND, standard mean difference, and mean baseline reduction) to visual analysis of several data sets and found that each consistently estimated relative effect size.…”

Section: Evaluating Results In Ssed Researchmentioning

confidence: 99%

Single-Subject Experimental Design for Evidence-Based Practice

Byiers

Reichle

Symons

2012

Am J Speech Lang Pathol

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259

198

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Purpose Single-subject experimental designs (SSEDs) represent an important tool in the development and implementation of evidence-based practice in communication sciences and disorders. The purpose of this article is to review the strategies and tactics of SSEDs and their application in speech-language pathology research. Method The authors discuss the requirements of each design, followed by advantages and disadvantages. The logic and methods for evaluating effects in SSED are reviewed as well as contemporary issues regarding data analysis with SSED data sets. Examples of challenges in executing SSEDs are included. Specific exemplars of how SSEDs have been used in speech-language pathology research are provided throughout. Conclusion SSED studies provide a flexible alternative to traditional group designs in the development and identification of evidence-based practice in the field of communication sciences and disorders.

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“…In the absence of expert support, function-based interventions that are considered a core feature of the top tier of PBIS and the Pyramid Model have had limited success within early childhood programs (Johnson, Reichle, & Monn, 2009). Integration of systems, practitioner knowledge and capacity, as well as feasible models for using data to guide decision making are barriers (Dunlap & Fox, 2011).…”

mentioning

confidence: 99%

“…The persistence of challenging behaviors for some children highlights a need and an opportunity to explore several key principles of behavioral intervention that are necessary for effective decision making when more personalized interventions must be layered on top of high-quality universal supports. In the absence of expert support, function-based interventions that are considered a core feature of the top tier of PBIS and the Pyramid Model have had limited success within early childhood programs (Johnson, Reichle, & Monn, 2009). Integration of systems, practitioner knowledge and capacity, as well as feasible models for using data to guide decision making are barriers (Dunlap & Fox, 2011).…”

mentioning

confidence: 99%