The goal of Direct Instruction (DI) is to teach content as effectively and efficiently as possible. To do this, instructional designers must identify generative relations or strategies that allow the learner to respond correctly to untaught situations. The purpose of content analysis is to identify generative relations in the domain to be taught and arrange the content in such a way that it supports maximally generative instruction. This article explains the role of content analysis in developing DI programs and provides examples and nonexamples of content analysis in five content domains: spelling, basic arithmetic facts, earth science, basic language, and narrative language. It includes a brief sketch of a general methods of conducting a content analysis. It concludes that content analysis is the foundation upon which generative instruction is built and that instructional designers could produce more effective, efficient, and powerful programs by attending explicitly and carefully to content analysis.

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Direct Instruction (DI) teaches challenging academic content to a range of diverse learners. In order to do so, DI includes a complex system for organizing and directing teacher-student interactions to maximize learning. This system includes: instructional formats that specify the interactions between teacher and student, flexible skills-based groupings, active student responding, responsive interactions between students and teachers, ongoing data-based decision making, and mastery teaching. In this article, we describe each of these main features of the system, define their functions, reveal how they are interwoven throughout all DI lessons, and provide specific examples of their application during instruction. Our goal is to describe and clarify critical features of DI lesson presentation and teacher-student interaction so that instructional designers, teachers, and other practitioners can use existing DI programs effectively and include these features in newly developed programs.Keywords Direct Instruction . instructional design . teacher . student interactions . teaching Direct Instruction (DI) sets for itself the ambitious goal of effectively and efficiently teaching challenging academic content to a wide range of learners including typically developing students and those with significant disabilities. This requires attention to numerous aspects of the design and process of instruction. The design of instruction includes content analysis to find powerful generative relations that can be taught (Slocum & Rolf, this issue), analysis of concepts to develop sets of examples and nonexamples that demonstrate their range and limits (Johnson, this issue), development of faultless communication through juxtaposition of examples and nonexamples to ensure that communication is logically consistent with one, and only one, interpretation (Twyman, this issue), and other aspects of instructional program development such as sequencing and interconnection among topics (Watkins & Slocum, 2004). But even the most carefully and elegantly designed program cannot change student behavior without powerful systems for teacherstudent interactions. An instructional program, at best, represents potential. For its potential to be realized, the instructional

The process of adopting curricula and programs in U.S. schools is an understudied topic. Given the importance of selecting evidence-based and contextually relevant programs that meet the needs of the school, additional research to examine this process is critical. In this exploratory investigation, we conducted semi-structured interviews with ten building-level and ten district-level school administrators to learn (a) how they identify needs for a new program and (b) the perceived factors that influence decisions in selecting a program to adopt. Qualitative data from interview transcripts were analyzed through thematic analysis as outlined by Braun and Clarke (3(2):77, 2006), and saturation was reached at interview 18. Results yield three primary themes in the data and several related subthemes. We discuss these results as they apply to ways to support schools through the adoption process and the ample opportunities for future research.

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