By Bernard Moret, Henry D. Shapiro
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Additional resources for Algorithms from P to NP, Vol. I: Design and Efficiency
An assessor listened to a business owner and wrote down a current valuation for the property and possessions. In addition, the assessor made notes, often in the margins of a small ledger, about the potential for this business and at what point to visit again. Let us translate this image of an assessor to the educational environ ment. What if we listened to our students and observed what they now possess in the way of skills? Could we set a goal or a valuable growth tar get for this student? Could that help us determine a starting point for learning and help us differentiate the learning to reach the necessary growth for this student?
19 20 Data Driven D ifferentiation in the Sta ndards- Based Classroom describes student needs and behaviors, and the right-hand column suggests ways to use feedback to deal with each situation. This may be a handy chart to keep in your reflection book or journal as you consider specific students and their needs. Feedback and praise that are planned and tuned to specific student behaviors and needs are far more effective than any generalized practice of generic praise and shotgun solutions. Specific praise allows a child to feel deeply connected, and that is a most powerful motivator (Kessler, 2000).
Dr. Costa proposes twelve intelligent behaviors that help us with our thinking: • Persistence. To persist in spite of obstacles or frustrations to find solu tions to dilemmas or problems • Decreasing Impulsivity. To control physical and emotional responses in order to be successful in a situation • Empathic listening. To feel for another by putting oneself "in their shoes," evoking the thoughts and feelings that they might be experiencing • Metacognition . To be aware of one's own thinking and to revisit or • Flexibility in thinking.
Algorithms from P to NP, Vol. I: Design and Efficiency by Bernard Moret, Henry D. Shapiro