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Troy Patterson

Gifted & Talented…and Afraid | EduGuide - 1 views

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msm289

shared by Troy Patterson on 30 Oct 14 - No Cached
  • In fourth grade, I was one of three students selected to participate in a “Gifted & Talented” program. My parents were so proud; I was one of the “smart kids,” a brilliant writer and a natural actress, and life was going to be so easy for me.
  • getting the right answers, best grades, and lead roles in school plays wasn’t about learning; it was about proving, to myself and the world, that I deserved that “Gifted & Talented” distinction.
  • I was afraid to take risks that might show me off as anything less than innately brilliant.
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  • I avoided subjects I found difficult, padding my schedule with all English and Social Studies while skipping Math and Science. I stopped trying out for school plays, afraid of not being cast as a lead.
  • as for writing, supposedly my greatest “natural talent,” I hoarded everything I wrote and never managed to finish or submit a single manuscript for publication. Because even one rejection letter wouldn’t have just meant that my story wasn’t good enough; it would have meant that I, as a human being, was not good enough.
  • for me, the “Gifted & Talented” label unintentionally contributed to what psychologist Carol Dweck would call a “fixed mindset”:
  • The categories are not “smart” and “dumb”; they are “learned that already” and “haven’t learned that yet.”
  • As a fourth grader, I wasn’t more gifted or talented than anyone else; I had just learned a little more about theater, literature and writing, thanks to my parents, than many of my peers had.
  • That didn’t make me smart or talented any more than it made my peers stupid.
Troy Patterson

Updating Data-Driven Instruction and the Practice of Teaching | Larry Cuban on School R... - 0 views

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msm292 reform data

shared by Troy Patterson on 27 Dec 14 - No Cached
  • I am talking about data-driven instruction–a way of making teaching less subjective, more objective, less experience-based, more scientific.
  • Data-driven instruction, advocates say, is scientific and consistent with how successful businesses have used data for decades to increase their productivity.
  • Of course, teachers had always assessed learning informally before state- and district-designed tests. Teachers accumulated information (oops! data) from pop quizzes, class discussions, observing students in pairs and small groups, and individual conferences.
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  • Based on these data, teachers revised lessons. Teachers leaned heavily on their experience with students and the incremental learning they had accumulated from teaching 180 days, year after year.
  • Teachers’ informal assessments of students gathered information directly and  would lead to altered lessons.
  • In the 1990s and, especially after No Child Left Behind became law in 2002, the electronic gathering of data, disaggregating information by groups and individuals, and then applying lessons learned from analysis of tests and classroom practices became a top priority.
  • Now, principals and teachers are awash in data.
  • How do teachers use the massive data available to them on student performance?
  • studied four elementary school grade-level teams in how they used data to improve lessons. She found that supportive principals and superintendents and habits of collaboration increased use of data to alter lessons in two of the cases but not in the other two.
  • Julie Marsh and her colleagues found 15 where teachers used annual tests, for example, in basic ways to target weaknesses in professional development or to schedule double periods of language arts for English language learners.
  • These researchers admitted, however, that they could not connect student achievement to the 36 instances of basic to complex data-driven decisions  in these two districts.
  • Of these studies, the expert panel found 64 that used experimental or quasi-experimental designs and only six–yes, six–met the Institute of Education Sciences standard for making causal claims about data-driven decisions improving student achievement. When reviewing these six studies, however, the panel found “low evidence” (rather than “moderate” or “strong” evidence) to support data-driven instruction. In short, the assumption that data-driven instructional decisions improve student test scores is, well, still an assumption not a fact.
  • Numbers may be facts. Numbers may be objective. Numbers may smell scientific. But we give meaning to these numbers. Data-driven instruction may be a worthwhile reform but as an evidence-based educational practice linked to student achievement, rhetoric notwithstanding, it is not there yet.
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