Association for Behavior Analysis International

Everyday activities as well as great creative achievements such as those that occur in science, mathematics, and art arise from complex repertoires. Generativity and language-pervading topics in areas such as psychology and education-often lie at the heart of both everyday activities and more complex, novel performances. Within behavior analysis, a multitude of studies has derived tool and component skills that should be established in order to arrive at terminal desired performances, such as holding a conversation, reading and comprehending complex written materials, thinking mathematically, and conducting scientific experiments. Analysis of the components can also suggest promising instructional interventions for developing these terminal repertoires. This symposium will present an analysis of components involved in verbal behaviors described in the Assessment of Basic Language and Learning Skills (ABLLS) and in mathematical thinking. These components will be classified in terms of the learning type model developed by Tiemann and Markle (1990) and further refined by Layng (2005, 2007). Additionally, based on these analyses and on the research literature, suggestions for teaching these different verbal and generative repertoires will be offered.

Service providers for children with autism and other developmental disabilities rely on behavioral assessments to determine not only clients initial repertoires, but also to develop effective treatment. The ABLLS is among the behavioral assessments that are used frequently by professionals to evaluate existing repertoires and support treatment decisions in terms of target behavior choices. This conceptual paper will present an analysis and classification of different verbal repertoires that are defined in the ABLLS according to the different Types of Learning described by Sota, Leon, and Layng (2011) and Tiemann and Markle (1990). The ABLLS areas that will be analyzed and classified include receptive language, vocal imitation, requests, labeling, intraverbals, and syntax and grammar. Within each one of these areas, tasks will be classified according to the following general Types of Learning: psychomotor, simple cognitive, and complex cognitive. Each one of these Types of Learning will be subdivided into more specific categories, and these categories will be operationally defined as a means to develop systematic criteria for classifying the different target verbal behaviors. Additionally, we hope to provide a tool that can be used for the analysis and classification of other verbal behaviors not directly addressed in this analysis.

The search for evidence-based practices to teach language for children on the autism spectrum is a growing endeavor given the number of children who are diagnosed each year. Suggestions regarding best practices for teaching language to children with autism and other developmental disabilities can be found in a myriad of scientific articles, newsletters, blogs, among other means of communication; however, only a few practices are evidence-based. The choice between different language-teaching procedures depend on the clients existing repertoire, the amount of time and resources available, and the terminal goals described in individualized educational plans. Once terminal repertoires are described, it is necessary to match teaching procedure choices to student needs. The different types of learning described in the instructional design literature can provide guidelines on how to make choices among existing language teaching procedures, since they point to stimulus-stimulus and stimulus-response relations that need to be taught. This conceptual paper will propose practices for teaching receptive language, vocal imitation, requests, labeling, intraverbals, and syntax and grammar based on (a) the literature on language teaching evidence-based practices and (b) an analysis and classification of ABLLS verbal repertoires according to the types of learning.

Proficiency in mathematics entails much more than being fluent in math facts and procedures for solving equations. Proficiency involves thinking mathematically, and includes a number of component skills that span a variety of learning types. Designing instruction to help learners develop the full range of components that make up mathematical proficiency can be challenging. This presentation will discuss an analysis of mathematical proficiency based on the K-8 Common Core State Standards for Mathematics and the National Research Councils report Adding It Up. This analysis will be presented in terms of the learning type model developed by Tiemann and Markle (1990) and further refined by Layng (2005, 2007) and discussed with a focus on mathematical thinking as verbal behavior. Suggestions and considerations for the design of math instruction based on this analysis and incorporating what we know from research on learning and performancefor example, problem solving, stimulus equivalence, and the development and transfer of stimulus controlwill be presented.