A 1-(S,T)-edge-connectivity augmentation algorithm by Enni S.

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Such re­ ports are confusing and too unwieldy for busy professionals to make pro­ ductive use of. Examples of this include item analyses by school , clas s , and student, which are often rather lengthy. There certainly is useful in­ formation here , but it is hard to sort out and make sense of. 4; however, as we will see in chapter 3 , these tend to be limited in what useful information they can provide because of the data aggregations used to create them. Other testing reports that take the de­ tailed or individual student data by class and summarize it for teachers by learning objective are more useful .

Figure 3 . 5 . The ful l scope o f t h e data variables 42 Chapter Three displays the top layer, but this can become very complex . 5 . This is where decision-support and data-warehousing systems come in handy, as the system allows you to easily retrieve and analyze data from within this vast data set. DECON STRUCTING THE QUESTIONS Let us now look more deeply at the second strategy or question I raised earlier: What groups of students may be contributing to Milford's suc­ cess? The real issue here is how we can deconstruct this question into doable analyses ; that is , what analyses should we run?

Which is all very easy to do once A Framework for Analyzin g Your Questions 49 you learn to use the ad hoc query tool . The assistant superintendent in Mil­ ford regularly runs queries such as this one and discusses the results with his administrative and teaching staff to determine what is working and what else they can do to improve achievement. Due to the level of specificity of this analysis and discussion, Milford is making important progress and is one reason , albeit not the only reason , they are doing well compared to similar districts .

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