FOR A NUMBER of years, we have been helping high schools and districts that are attempting to create or improve career academies- hence our title: "Learning by Doing Career Academies."4 Our assistance includes developing the schools' capacity to keep track of results for students by relying mainly on information that is ordinarily available from student transcripts. We believe it is essential for schools themselves to continuously gauge results of career academies and other educational programs, because even if such programs have been found to be effective somewhere, they are unlikely to be effective everywhere. Therefore, one of our aims here is to describe how schools can self-monitor, and what they can learn in the process of operating career academies. Second, we wish to add to general knowledge about career academies by testing some ideas about why they may be more effective in some locations and situations than in others. Specifically, we conduct a cross-site analysis to explore whether sites that implement certain features of the academy model to a greater degree also obtain bigger gains in performance of academy students. Both parts of the analysis in this chapter focus on student performance during high school, not post-high school outcomes such as employment or college attendance. Longevity and solid evidence of effectiveness distinguish career academies from other programs, practices, and policies that were bundled under the "school-to-work" or "school-to- career" rubric in the United States during the 1990s. The first known career academies were started in 1969, well in advance of more recent initiatives such as Tech Prep or job shadowing, though much later than co-op or vocational education (Stern et al. 1995). Relatively persuasive evidence of career academies' effectiveness comes from several quasiexperimental studies, and especially from a random-assignment evaluation conducted by MDRC (Manpower Demonstration Research Corporation). The second part of this paper spells out what we mean by a career academy, and summarizes career academies' history and existing research. (Chapter 6 in this volume, by Margaret Terry Orr and her colleagues, provides additional information about the purposes and pedagogical practices of career academies, especially those associated with the National Academy Foundation.) Despite evidence that career academies can produce positive results for students, success is never automatic. Local circumstances may enhance or undermine the effectiveness of a particular career academy, within a particular school at a particular time. The careeracademy model has several elements, all of which require planning and effort to put in place and keep in place. In 1997 we established the Career Academy Support Network (CASN) to help high schools and districts that want to develop and improve career academies. CASN provides professional development, on-site coaching, and other kinds of implementation assistance. As part of its service, CASN uses available data from student transcripts to monitor implementation and to determine whether academy students are improving more or less rapidly than nonacademy students in the same high schools. Nonrandom selection of students into academies and nonrandom attrition make it impossible for this analysis to determine whether academy participation causes greater improvement in students' performance.2 But the available data can nevertheless inform academy stakeholders about the kinds of students who are participating and how their performance is changing over time. The third and fourth sections of the chapter illustrate this kind of analysis. The second question we seek to answer in this paper is whether academy students' growth, relative to nonacademy students in the same school, is associated with implementation of certain key features of the model. We focus on measures having to do with scheduling and course taking because these are considered fundamental to the academy design and they can be computed from student transcripts. This analysis, using a hierarchical model, is reported in the fourth and fifth sections of the chapter. Although the differences between academy and nonacademy students may be attributable in part to nonrandom selection or attrition, this exercise demonstrates a new procedure for estimating how much implementation matters. Copyright © 2007 by Russell Sage Foundation.
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