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The Comprehensive School Improvement Program (CSIP), which was mandated by New York State to address issues of school reform, was a disappointment both in the way the State conceived the program and in the way it was implemented. CSIP was designed to encourage cooperative planning among teachers, principals, parents, and other school staff to foster school-based decision-making. Schools were mandated to participate in this project if they were designated on the State's Comprehensive Assessment Report list of schools needing substantial improvement in third and sixth grade reading and math scores, as well as attendance. This report reviews the following: (1) the State's responsibility; (2) shortcomings of the Board's implementation of CSIP; (3) description and history of CSIP; (4) description of the CSIP facilitators; (5) results of visits to seven CSIP sites; (6) how CSIP fell short of its theoretical goals; (7) how CSIP's implementation was inadequate; (8) CSIP decentralization; (9) next steps; and (10) recommendations for changes on the state level, the structure of CSIP, and the implementation of CSIP. Appendices include the following: (1) excerpt from section 100.2 of the Commissioner's regulations; (2) CSIP data; and (3) descriptions of the seven CSIP sites visited. (BJV)

Most vision systems for robotic applications rely entirely on the extraction of information from gray-level images. Humans, however, regularly depend on color to discriminate between objects. Therefore, the inclusion of color in a robot vision system seems a natural extension of the existing gray-level capabilities. A method for robot object recognition using a color-coding classification scheme is discussed. The scheme is based on an algebraic system in which a two-dimensional color image is represented as a polynomial of two variables. The system is then used to find the color contour of objects. In a controlled environment, such as that of the in-orbit space station, a particular class of objects can thus be quickly recognized by its color.