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We're excited about middle school teachers who are fired up about their lessons—who want to spend their time discussing, preparing, and immersing themselves in the rich content they teach. We value teachers who truly listen to what students are saying—who create a space for students to think their way through to answers, and who praise their insights and curiosities along the way.

We value educators with a hunger for feedback and professional growth, and a dedication to excellence. Our teachers embrace a rigorous liberal arts program and a positive approach to discipline, believe in the power of family and community partnerships, and commit to helping students succeed in college and beyond. Ascend is a network of K public charter schools serving 5, students across 12 schools in Central Brooklyn. Our mission is to provide our students an extraordinary education, placing them firmly on the path to success in college and beyond.

We guide our students to think critically and independently, and to enjoy education as an end in itself. We teach a rich and rigorous liberal arts curriculum that nurtures students' natural curiosity about the world. We foster a positive, non-punitive school culture where students feel connected, empowered, and safe to take academic risks. And we design our school buildings to reflect the incredible accomplishments that happen within their walls and our high aspirations for teaching and learning.

To create this kind of vibrant learning community, we invest heavily in our faculty by providing ongoing professional training and support, and encourage our educators to collaborate closely and push each other to achieve great outcomes. Visit our website to learn more about the Ascend approach. Staff and reporting relationships The middle school ELA teacher reports to the dean of instruction.

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He or she also accepts direction from and works closely with the dean of students. In practice, American public schools generally do a poor job of systematically developing and evaluating teachers. School districts often fall short in efforts to improve the performance of less effective teachers, and failing that, of removing them.

Principals typically have too broad a span of control frequently supervising as many as 30 teachers , and too little time and training to do an adequate job of assessing and supporting teachers. Many principals are themselves unprepared to evaluate the teachers they supervise. Due process requirements in state law and union contracts are sometimes so cumbersome that terminating ineffective teachers can be quite difficult, except in the most extreme cases. In addition, some critics believe that typical teacher compensation systems provide teachers with insufficient incentives to improve their performance.

Some advocates of this approach expect the provision of performance-based financial rewards to induce teachers to work harder and thereby increase their effectiveness in raising student achievement. Others expect that the apparent objectivity of test-based measures of teacher performance will permit the expeditious removal of ineffective teachers from the profession and will encourage less effective teachers to resign if their pay stagnates. Some believe that the prospect of higher pay for better performance will attract more effective teachers to the profession and that a flexible pay scale, based in part on test-based measures of effectiveness, will reduce the attrition of more qualified teachers whose commitment to teaching will be strengthened by the prospect of greater financial rewards for success.

Encouragement from the administration and pressure from advocates have already led some states to adopt laws that require greater reliance on student test scores in the evaluation, discipline, and compensation of teachers. Other states are considering doing so. But there is no current evidence to indicate either that the departing teachers would actually be the weakest teachers, or that the departing teachers would be replaced by more effective ones. Nor is there empirical verification for the claim that teachers will improve student learning if teachers are evaluated based on test score gains or are monetarily rewarded for raising scores.

NCLB has used student test scores to evaluate schools, with clear negative sanctions for schools and, sometimes, their teachers whose students fail to meet expected performance standards. We can judge the success or failure of this policy by examining results on the National Assessment of Educational Progress NAEP , a federally administered test with low stakes, given to a small but statistically representative sample of students in each state. The NCLB approach of test-based accountability promised to close achievement gaps, particularly for minority students.

Scores rose at a much more rapid rate before NCLB in fourth grade math and in eighth grade reading, and rose faster after NCLB in fourth grade reading and slightly faster in eighth grade math. These data do not support the view that that test-based accountability increases learning gains. Table 1 shows only simple annual rates of growth, without statistical controls.

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A recent careful econometric study of the causal effects of NCLB concluded that during the NCLB years, there were noticeable gains for students overall in fourth grade math achievement, smaller gains in eighth grade math achievement, but no gains at all in fourth or eighth grade reading achievement. The study did not compare pre- and post-NCLB gains. Such findings provide little support for the view that test-based incentives for schools or individual teachers are likely to improve achievement, or for the expectation that such incentives for individual teachers will suffice to produce gains in student learning.

As we show in what follows, research and experience indicate that approaches to teacher evaluation that rely heavily on test scores can lead to narrowing and over-simplifying the curriculum, and to misidentifying both successful and unsuccessful teachers. These and other problems can undermine teacher morale, as well as provide disincentives for teachers to take on the neediest students.

When attached to individual merit pay plans, such approaches may also create disincentives for teacher collaboration. In truth, although payment for professional employees in the private sector is sometimes related to various aspects of their performance, the measurement of this performance almost never depends on narrow quantitative measures analogous to test scores in education.

Rather, private-sector managers almost always evaluate their professional and lower-management employees based on qualitative reviews by supervisors; quantitative indicators are used sparingly and in tandem with other evidence. Management experts warn against significant use of quantitative measures for making salary or bonus decisions.

Other human service sectors, public and private, have also experimented with rewarding professional employees by simple measures of performance, with comparably unfortunate results. When the U. The counselors also began to concentrate on those unemployed workers who were most able to find jobs on their own, diminishing their attention to those whom the employment programs were primarily designed to help. A third reason for skepticism is that in practice, and especially in the current tight fiscal environment, performance rewards are likely to come mostly from the redistribution of already-appropriated teacher compensation funds, and thus are not likely to be accompanied by a significant increase in average teacher salaries unless public funds are supplemented by substantial new money from foundations, as is currently the situation in Washington, D.

If performance rewards do not raise average teacher salaries, the potential for them to improve the average effectiveness of recruited teachers is limited and will result only if the more talented of prospective teachers are more likely than the less talented to accept the risks that come with an uncertain salary. Once again, there is no evidence on this point. And finally, it is important for the public to recognize that the standardized tests now in use are not perfect, and do not provide unerring measurements of student achievement.

These tests are unlike the more challenging open-ended examinations used in high-achieving nations in the world. This seemingly paradoxical situation can occur because drilling students on narrow tests does not necessarily translate into broader skills that students will use outside of test-taking situations. Furthermore, educators can be incentivized by high-stakes testing to inflate test results. At the extreme, numerous cheating scandals have now raised questions about the validity of high-stakes student test scores.

Without going that far, the now widespread practice of giving students intense preparation for state tests—often to the neglect of knowledge and skills that are important aspects of the curriculum but beyond what tests cover—has in many cases invalidated the tests as accurate measures of the broader domain of knowledge that the tests are supposed to measure. We see this phenomenon reflected in the continuing need for remedial courses in universities for high school graduates who scored well on standardized tests, yet still cannot read, write or calculate well enough for first-year college courses.

Statisticians, psychometricians, and economists who have studied the use of test scores for high-stakes teacher evaluation, including its most sophisticated form, value-added modeling VAM , mostly concur that such use should be pursued only with great caution. Donald Rubin, a leading statistician in the area of causal inference, reviewed a range of leading VAM techniques and concluded:. We do not think that their analyses are estimating causal quantities, except under extreme and unrealistic assumptions. The estimates from VAM modeling of achievement will often be too imprecise to support some of the desired inferences.

There are many pitfalls to making causal attributions of teacher effectiveness on the basis of the kinds of data available from typical school districts. We still lack sufficient understanding of how seriously the different technical problems threaten the validity of such interpretations. And a recent report of a workshop conducted jointly by the National Research Council and the National Academy of Education concluded:. Value-added methods involve complex statistical models applied to test data of varying quality.

Accordingly, there are many technical challenges to ascertaining the degree to which the output of these models provides the desired estimates. Among the concerns raised by researchers are the prospects that value-added methods can misidentify both successful and unsuccessful teachers and, because of their instability and failure to disentangle other influences on learning, can create confusion about the relative sources of influence on student achievement. If used for high-stakes purposes, such as individual personnel decisions or merit pay, extensive use of test-based metrics could create disincentives for teachers to take on the neediest students, to collaborate with one another, or even to stay in the profession.

Efforts to address one statistical problem often introduce new ones. As a result, reliance on student test scores for evaluating teachers is likely to misidentify many teachers as either poor or successful. Thus, teachers working in affluent suburban districts would almost always look more effective than teachers in urban districts if the achievement scores of their students were interpreted directly as a measure of effectiveness. Even when student demographic characteristics are taken into account, the value-added measures are too unstable i.

For example, with VAM, the essay-writing a student learns from his history teacher may be credited to his English teacher, even if the English teacher assigns no writing; the mathematics a student learns in her physics class may be credited to her math teacher. Some students receive tutoring, as well as homework help from well-educated parents. Even among parents who are similarly well- or poorly educated, some will press their children to study and complete homework more than others.

Class sizes vary both between and within schools, a factor influencing achievement growth, particularly for disadvantaged children in the early grades. A teacher who works in a well-resourced school with specialist supports may appear to be more effective than one whose students do not receive these supports. Although value-added methods can support stronger inferences about the influences of schools and programs on student growth than less sophisticated approaches, the research reports cited above have consistently cautioned that the contributions of VAM are not sufficient to support high-stakes inferences about individual teachers.

And less sophisticated models do even less well. The difficulty arises largely because of the nonrandom sorting of teachers to students across schools, as well as the nonrandom sorting of students to teachers within schools. Several studies show that VAM results are correlated with the socioeconomic characteristics of the students. Of course, it could also be that affluent schools or districts are able to recruit the best teachers.

This possibility cannot be ruled out entirely, but some studies control for cross-school variability and at least one study has examined the same teachers with different populations of students, showing that these teachers consistently appeared to be more effective when they taught more academically advanced students, fewer English language learners, and fewer low-income students. Teachers who have chosen to teach in schools serving more affluent students may appear to be more effective simply because they have students with more home and school supports for their prior and current learning, and not because they are better teachers.

Some policy makers assert that it should be easier for students at the bottom of the achievement distribution to make gains because they have more of a gap to overcome. This assumption is not confirmed by research. Furthermore, students who have fewer out-of-school supports for their learning have been found to experience significant summer learning loss between the time they leave school in June and the time they return in the fall.

We discuss this problem in detail below. For now, suffice it to say that teachers who teach large numbers of low-income students will be noticeably disadvantaged in spring-to-spring test gain analyses, because their students will start the fall further behind than more affluent students who were scoring at the same level in the previous spring.

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The most acceptable statistical method to address the problems arising from the non-random sorting of students across schools is to include indicator variables so-called school fixed effects for every school in the data set. This approach, however, limits the usefulness of the results because teachers can then be compared only to other teachers in the same school and not to other teachers throughout the district.

For example, a teacher in a school with exceptionally talented teachers may not appear to add as much value to her students as others in the school, but if compared to all the teachers in the district, she might fall well above average. In any event, teacher effectiveness measures continue to be highly unstable, whether or not they are estimated using school fixed effects.

Statistical models cannot fully adjust for the fact that some teachers will have a disproportionate number of students who may be exceptionally difficult to teach students with poorer attendance, who have become homeless, who have severe problems at home, who come into or leave the classroom during the year due to family moves, etc.

In any school, a grade cohort is too small to expect each of these many characteristics to be represented in the same proportion in each classroom. Another recent study documents the consequences of students in this case, apparently purposefully not being randomly assigned to teachers within a school. It uses a VAM to assign effects to teachers after controlling for other factors, but applies the model backwards to see if credible results obtain.

For example, students who do well in fourth grade may tend to be assigned to one fifth grade teacher while those who do poorly are assigned to another. The usefulness of value-added modeling requires the assumption that teachers whose performance is being compared have classrooms with students of similar ability or that the analyst has been able to control statistically for all the relevant characteristics of students that differ across classrooms.

Purposeful, nonrandom assignment of students to teachers can be a function of either good or bad educational policy. Some grouping schemes deliberately place more special education students in selected inclusion classrooms or organize separate classes for English language learners. Skilled principals often try to assign students with the greatest difficulties to teachers they consider more effective. Some teachers are more effective with students with particular characteristics, and principals with experience come to identify these variations and consider them in making classroom assignments.

In contrast, some less conscientious principals may purposefully assign students with the greatest difficulties to teachers who are inexperienced, perhaps to avoid conflict with senior staff who resist such assignments. Furthermore, traditional tracking often sorts students by prior achievement. Regardless of whether the distribution of students among classrooms is motivated by good or bad educational policy, it has the same effect on the integrity of VAM analyses: the nonrandom pattern makes it extremely difficult to make valid comparisons of the value-added of the various teachers within a school.

Unlike school, district, and state test score results based on larger aggregations of students, individual classroom results are based on small numbers of students leading to much more dramatic year-to-year fluctuations. Even the most sophisticated analyses of student test score gains generate estimates of teacher quality that vary considerably from one year to the next. In addition to changes in the characteristics of students assigned to teachers, this is also partly due to the small number of students whose scores are relevant for particular teachers. Small sample sizes can provide misleading results for many reasons.

No student produces an identical score on tests given at different times. A student who is not certain of the correct answers may make more lucky guesses on multiple-choice questions on one test, and more unlucky guesses on another. Researchers studying year-to-year fluctuations in teacher and school averages have also noted sources of variation that affect the entire group of students, especially the effects of particularly cooperative or particularly disruptive class members.

Analysts must average test scores over large numbers of students to get reasonably stable estimates of average learning. The larger the number of students in a tested group, the smaller will be the average error because positive errors will tend to cancel out negative errors.

But the sampling error associated with small classes of, say, students could well be too large to generate reliable results. Most teachers, particularly those teaching elementary or middle school students, do not teach enough students in any year for average test scores to be highly reliable. In schools with high mobility, the number of these students with scores at more than one point in time, so that gains can be measured, is smaller still.

In this respect VAM results are even less reliable indicators of teacher contributions to learning than a single test score. VAM approaches incorporating multiple prior years of data suffer similar problems. In addition to the size of the sample, a number of other factors also affect the magnitude of the errors that are likely to emerge from value-added models of teacher effectiveness. In a careful modeling exercise designed to account for the various factors, a recent study by researchers at Mathematica Policy Research, commissioned and published by the Institute of Education Sciences of the U.

Department of Education, concludes that the errors are sufficiently large to lead to the misclassification of many teachers. The Mathematica models, which apply to teachers in the upper elementary grades, are based on two standard approaches to value-added modeling, with the key elements of each calibrated with data on typical test score gains, class sizes, and the number of teachers in a typical school or district.

This means that in a typical performance measurement system, more than one in four teachers who are in fact teachers of average quality would be misclassified as either outstanding or poor teachers, and more than one in four teachers who should be singled out for special treatment would be misclassified as teachers of average quality.

Despite the large magnitude of these error rates, the Mathematica researchers are careful to point out that the resulting misclassification of teachers that would emerge from value-added models is still most likely understated because their analysis focuses on imprecision error alone. The failure of policy makers to address some of the validity issues, such as those associated with the nonrandom sorting of students across schools, discussed above, would lead to even greater misclassification of teachers. Measurement error also renders the estimates of teacher quality that emerge from value-added models highly unstable.

Because of the range of influences on student learning, many studies have confirmed that estimates of teacher effectiveness are highly unstable. There was similar movement for teachers who were highly ranked in the first year. Another study confirmed that big changes from one year to the next are quite likely, with year-to-year correlations of estimated teacher quality ranging from only 0.

Such instability from year to year renders single year estimates unsuitable for high-stakes decisions about teachers, and is likely to erode confidence both among teachers and among the public in the validity of the approach. The problems of measurement error and other sources of year-to-year variability are especially serious because many policy makers are particularly concerned with removing ineffective teachers in schools serving the lowest-performing, disadvantaged students.

Yet students in these schools tend to be more mobile than students in more affluent communities. In highly mobile communities, if two years of data are unavailable for many students, or if teachers are not to be held accountable for students who have been present for less than the full year, the sample is even smaller than the already small samples for a single typical teacher, and the problem of misestimation is exacerbated. Yet the failure or inability to include data on mobile students also distorts estimates because, on average, more mobile students are likely to differ from less mobile students in other ways not accounted for by the model, so that the students with complete data are not representative of the class as a whole.

Even if state data systems permit tracking of students who change schools, measured growth for these students will be distorted, and attributing their progress or lack of progress to different schools and teachers will be problematic. If policy makers persist in attempting to use VAM to evaluate teachers serving highly mobile student populations, perverse consequences can result.

Once teachers in schools or classrooms with more transient student populations understand that their VAM estimates will be based only on the subset of students for whom complete data are available and usable, they will have incentives to spend disproportionately more time with students who have prior-year data or who pass a longevity threshold, and less time with students who arrive mid-year and who may be more in need of individualized instruction. The most frequently proposed solution to this problem is to limit VAM to teachers who have been teaching for many years, so their performance can be estimated using multiple years of data, and so that instability in VAM measures over time can be averaged out.

This statistical solution means that states or districts only beginning to implement appropriate data systems must wait several years for sufficient data to accumulate. More critically, the solution does not solve the problem of nonrandom assignment, and it necessarily excludes beginning teachers with insufficient historical data and teachers serving the most disadvantaged and most mobile populations, thus undermining the ability of the system to address the goals policy makers seek.

The statistical problems we have identified here are not of interest only to technical experts. To the extent that this policy results in the incorrect categorization of particular teachers, it can harm teacher morale and fail in its goal of changing behavior in desired directions.

For example, if teachers perceive the system to be generating incorrect or arbitrary evaluations, perhaps because the evaluation of a specific teacher varies widely from year to year for no explicable reason, teachers could well be demoralized, with adverse effects on their teaching and increased desire to leave the profession. In addition, if teachers see little or no relationship between what they are doing in the classroom and how they are evaluated, their incentives to improve their teaching will be weakened.

The statistical concerns we have described are accompanied by a number of practical problems of evaluating teachers based on student test scores on state tests. Most secondary school teachers, all teachers in kindergarten, first, and second grades and some teachers in grades three through eight do not teach courses in which students are subject to external tests of the type needed to evaluate test score gains.

And even in the grades where such gains could, in principle, be measured, tests are not designed to do so. Value-added measurement of growth from one grade to the next should ideally utilize vertically scaled tests, which most states including large states like New York and California do not use. In order to be vertically scaled, tests must evaluate content that is measured along a continuum from year to year.

Following an NCLB mandate, most states now use tests that measure grade-level standards only and, at the high school level, end-of-course examinations, neither of which are designed to measure such a continuum. These test design constraints make accurate vertical scaling extremely difficult. Without vertically scaled tests, VAM can estimate changes in the relative distribution, or ranking, of students from last year to this, but cannot do so across the full breadth of curriculum content in a particular course or grade level, because many topics are not covered in consecutive years.

Furthermore, the tests will not be able to evaluate student achievement and progress that occurs well below or above the grade level standards. Teachers, however, vary in their skills. Some teachers might be relatively stronger in teaching probability, and others in teaching algebra. Overall, such teachers might be equally effective, but VAM would arbitrarily identify the former teacher as more effective, and the latter as less so. And finally, if high school students take end-of-course exams in biology, chemistry, and physics in different years, for example, there is no way to calculate gains on tests that measure entirely different content from year to year.

It is often quite difficult to match particular students to individual teachers, even if data systems eventually permit such matching, and to unerringly attribute student achievement to a specific teacher. In some cases, students may be pulled out of classes for special programs or instruction, thereby altering the influence of classroom teachers. Some schools expect, and train, teachers of all subjects to integrate reading and writing instruction into their curricula.

Many classes, especially those at the middle-school level, are team-taught in a language arts and history block or a science and math block, or in various other ways. In schools with certain kinds of block schedules, courses are taught for only a semester, or even in nine or 10 week rotations, giving students two to four teachers over the course of a year in a given class period, even without considering unplanned teacher turnover.

Similarly, NCLB requires low-scoring schools to offer extra tutoring to students, provided by the school district or contracted from an outside tutoring service. High quality tutoring can have a substantial effect on student achievement gains. Teachers should not be held responsible for learning gains or losses during the summer, as they would be if they were evaluated by spring-to-spring test scores. These summer gains and losses are quite substantial. Another recent study showed that two-thirds of the difference between the ninth grade test scores of high and low socioeconomic status students can be traced to summer learning differences over the elementary years.

To rectify obstacles to value-added measurement presented both by the absence of vertical scaling and by differences in summer learning, schools would have to measure student growth within a single school year, not from one year to the next. To do so, schools would have to administer high stakes tests twice a year, once in the fall and once in the spring.

The need, mentioned above, to have test results ready early enough in the year to influence not only instruction but also teacher personnel decisions is inconsistent with fall to spring testing, because the two tests must be spaced far enough apart in the year to produce plausibly meaningful information about teacher effects. A test given late in the spring, with results not available until the summer, is too late for this purpose.

Most teachers will already have had their contracts renewed and received their classroom assignments by this time. Although the various reasons to be skeptical about the use of student test scores to evaluate teachers, along with the many conceptual and practical limitations of empirical value added measures, might suffice by themselves to make one wary of the move to test-based evaluation of teachers, they take on even greater significance in light of the potential for large negative effects of such an approach. Using test scores to evaluate teachers unfairly disadvantages teachers of the neediest students.

Because of the inability of value-added methods to fully account for the differences in student characteristics and in school supports, as well as the effects of summer learning loss, teachers who teach students with the greatest educational needs will appear to be less effective than they are.

This could lead to the inappropriate dismissal of teachers of low-income and minority students, as well as of students with special educational needs. The success of such teachers is not accurately captured by relative value-added metrics, and the use of VAM to evaluate such teachers could exacerbate disincentives to teach students with high levels of need. Within a school, teachers will have incentives to avoid working with such students likely to pull down their teacher effectiveness scores.

Narrowing of the curriculum to increase time on what is tested is another negative consequence of high-stakes uses of value-added measures for evaluating teachers. The tests most likely to be used in any test-based teacher evaluation program are those that are currently required under NCLB, or that will be required under its reauthorized version. The current law requires that all students take standardized tests in math and reading each year in grades three through eight, and once while in high school.

Although NCLB also requires tests in general science, this subject is tested only once in the elementary and middle grades, and the law does not count the results of these tests in its identification of inadequate schools. Thus, for elementary and some middle-school teachers who are responsible for all or most curricular areas, evaluation by student test scores creates incentives to diminish instruction in history, the sciences, the arts, music, foreign language, health and physical education, civics, ethics and character, all of which we expect children to learn.

Survey data confirm that even with the relatively mild school-wide sanctions for low test scores provided by NCLB, schools have diminished time devoted to curricular areas other than math and reading. This shift was most pronounced in districts where schools were most likely to face sanctions—districts with schools serving low-income and minority children.

There are two reasons for this outcome. First, it is less expensive to grade exams that include only, or primarily, multiple-choice questions, because such questions can be graded by machine inexpensively, without employing trained professional scorers. Machine grading is also faster, an increasingly necessary requirement if results are to be delivered in time to categorize schools for sanctions and interventions, make instructional changes, and notify families entitled to transfer out under the rules created by No Child Left Behind.

And scores are also needed quickly if test results are to be used for timely teacher evaluation. If teachers are found wanting, administrators should know this before designing staff development programs or renewing teacher contracts for the following school year. As a result, standardized annual exams, if usable for high-stakes teacher or school evaluation purposes, typically include no or very few extended-writing or problem-solving items, and therefore do not measure conceptual understanding, communication, scientific investigation, technology and real-world applications, or a host of other critically important skills.

Not surprisingly, several states have eliminated or reduced the number of writing and problem-solving items from their standardized exams since the implementation of NCLB. Further, most principals are not adequately prepared to conduct accurate teacher evaluations. Many now find themselves spending an inordinate amount of time conducting formal classroom observations with extensive item checklists in hand.

They are visiting each classroom several times a year rather than spending the time needed for schoolwide efforts that will improve curriculum and instruction. It is a case of evaluation run amok. Lavigne and Good provide a chilling example of this pathology. In a school of 20 teachers, that means spending between and hours per year on observation, not assistance. Some research even suggests that classroom observations for purposes of evaluation actually reduce performance. A pilot report from Chicago found small effects when principals used an evaluation strategy that included two observations of reading teachers per year.

The results of the evaluations were used for teacher and school improvement, not harsh consequences. A key finding was that extensive training of principals in observation techniques and how to use the evaluations in program improvement made a large difference. Finally, many walkthroughs by principals miss the essence of good teaching and instead concentrate on trivia, according to Peter DeWitt. Crucially, such a narrow policy focus on dismissing a few teachers often leads to a failure to address other vital in-school measures, which significantly influence the performance of all teachers and the achievement of students.

For example, large numbers of teachers leave inner-city schools each year. Teacher churn and the resulting heavy use of substitutes are a major reason for low student performance. Excellent teachers are leaving the profession due to the stress of teaching in low-income urban schools and dreadful working conditions. This problem overshadows the damage done by a few underperforming teachers. Several researchers have recommended policies aimed at encouraging the retention of our best teachers.

Top teachers want collegiality, being part of effective teams, better working conditions, somebody paying attention to them, and career paths that allow them to keep teaching but take on additional responsibility helping other teachers or solving school performance problems and earn more money. Districts that solely concentrate on firing incompetent teachers miss this much larger and more productive target. It is also important to recognize that the quality of the curriculum and instructional materials is just about as important as teacher quality.

For more about the importance of curriculum and educational resources, see the companion article Provide High-Quality Instruction. In addition, the level of school funding matters. Yes, money does make a difference. The reports find that increased funding results in improved student performance, and conversely, cutting school budgets depresses outcomes.

Similar results were found in Indiana after the state drastically cut educational support. For a review of the literature that has revealed funding matters, see Does Money Matter in Education? Most states are spending below their expenditures, and some are cutting even more. Equally important is site and district leadership, particularly as they relate to building systems that connect teaching, curriculum, and instruction; to continuously improving these elements; and to improving the school climate by increasing the degree of engagement of teachers, students, and parents and community.

A recent report by Thomas Kane from Harvard found teacher perception of the school being a good place to work improved performance. In math, the amount of professional development and teacher feedback also helped. Principal leadership accounts for about one-quarter of in-school measures of student performance, teacher quality about one-third. For a comprehensive report on principal training, see The School Principal as Leader: Guiding Schools to Better Teaching and Learning and the standards for school leadership approved by the National Policy Board for Educational Administration in The evidence is clear—conflict between key stakeholders tends to sabotage the cooperative efforts needed to achieve effective reform.

When it comes to evaluating schools, high-stakes accountability based on tests has been just as ineffective and just as problematic in terms of unintended consequences. Concentrating on five percent of low-testing schools and responding to their performance with drastic measures—closures, mass firings, or conversion to charters—has produced negligible results. Such reform measures do, however, severely impact those schools, their students, and the surrounding communities. This is even more concerning given that many of the affected schools were unfairly misidentified.

They were actually progressing equal to or better than the remaining schools in the district. The failure of school turnaround policies has been documented by a number of respected sources. States that used tests to grade schools have found major problems with accuracy, and many have reversed the policy.

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Many have questioned whether the state reform formula and direction were actually the driving force behind the early gains. Instead, they point to the efforts made by excellent local superintendents who stressed the Build-and-Support approach. There are reports showing that segregation and in-school deficiencies considerably outweigh school-to-school comparisons in predicting achievement gaps.

This research demonstrates that, as of yet, the knowledge base for identifying failing schools is not sufficiently developed to allow for fair assessments. In addition, there is no clear research-based consensus regarding the best ways to intervene in low-performing schools. For example, recent evaluations of the federal School Improvement Grants program aimed at the lowest-underperforming schools found a slight overall improvement, but one-third of the grantees actually had falling scores.

The feds are currently providing a bit more flexibility to applicants under the program, admitting that their previous prescriptions were off base. Moreover, even if reform efforts were fair and successful, focusing on the few schools at the bottom ignores the vast majority of children. As Michael Fullan, one of the most respected leaders of the Build-and-Support approach, has pointed out, policies aimed at improving all schools have far better results. Edward Fiske and Helen Ladd made a similar point in an op-ed about successful low-income districts in London.

The districts that flourished pursued a districtwide strategic improvement plan as opposed to targeting the lowest performers, used broad accountability systems that went beyond test scores, and provided support for low-income students. Provide significant resources to support planning and restructuring and leverage competitive grants. Create transparent tiers of intervention and support combined with ongoing capacity building and sharing best practices.

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Glass, Gene. Take All the Credit? Strauss, V. The Washington Post. A Broader, Bolder Approach to Education. Sawhill, I. Kerwin McCrimmon, K. Kaiser Health News. American Statistical Association. OpEd News. American Education Research Association. Educational Researcher. Phi Delta Kappan. Lavigne, A. New York: Routledge. Stigins, R. Ballou, D. Amrein-Beardsley, A.

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Consequential Learning: A Public Approach to Better Schools (Paperback)

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