Читать книгу The Data Coach's Guide to Improving Learning for All Students - Katherine E. Stiles - Страница 31

Increasing the effective use of data to improve learning is the problem the authors set out to solve. Imagine two shores with an ocean in between (see Figure 1.4). On one shore are data—the myriad data now inundating schools: state test data sliced and diced every which way, local assessments, demographic data, dropout rates, graduation rates, course-taking patterns, attendance data, survey data, and on and on. On the distant shore are the desire, intention, moral commitment, and mandate to improve student learning and close persistent achievement gaps. But often there is no bridge between the shores and a wide ocean in between. Sadly, it is children who are drowning in the data gap, particularly children of color, English Language Learners, children living in poverty, and those with exceptional needs.

Why is there such a wide data gap? Although there are many contributing factors, the authors of this book agree with Richard Elmore that the data gap is primarily a problem of capacity:

With increased accountability, American schools and those who work in them are being asked to do something new—to engage in systematic, continuous improvement in the quality of the educational experience of students and to subject themselves to the discipline of measuring their success by the metric of students’ academic performance. Most people who currently work in public schools weren’t hired to do this work, nor have they been adequately prepared to do it either by their professional education or by their prior experience in schools. (Elmore, 2002, p. 5)

When you combine lack of adequate preparation with intense accountability pressures, poor use and even abuses of data abound. For example, if educators do not thoroughly understand their content and how to teach it, they can incorrectly diagnose student-learning problems and resort to drilling students on test items or tutoring “bubble” students—those who just missed a proficiency-level cut point—just to pass the test (Abrams & Madaus, 2003; Love, 2003). On the other hand, as Ann Lewis asserts, “There is plenty of evidence around that, when teachers know their content and know how to teach it at high levels to all students, ‘teaching to the test’ fades into the background of everyday instruction and learning” (as quoted in Sparks, 2005, p. 90).

Figure 1.4 The data gap.

If educators do not believe in all children’s capacity to reach challenging standards, they can react with complacency or resignation when they see achievement gaps among racial/ethnic and economic groups; or even worse, they can choose to institute practices such as tracking, which further limit opportunities to learn (Jerald, 2005; Love, 2003; Zuman, 2006). If they do not know how to interpret data and student work accurately, they can jump to flawed and even damaging conclusions (Confrey & Makar, 2005; Love, 2004). And without a systematic improvement process, schools, particularly those serving underserved students, languish in chronic underperformance—no matter what the pressures of accountability. As Elmore (2003) warns, “when we bear down on testing without the reciprocal supply of capacity . . . we exacerbate the problem we are trying to fix” (p. 6).

Figure 1.5 Connecting data to results.