Embedded Formative Assessment. Dylan Wiliam. Читать онлайн. Newlib. NEWLIB.NET

Автор: Dylan Wiliam
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isbn: 9781945349232
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researchers have made some progress in determining what kinds of teacher knowledge do contribute to student progress. For example, elementary school teachers’ scores on a test of mathematical knowledge for teaching (MKT) correlated significantly with their students’ progress in mathematics (Hill, Rowan, & Ball, 2005). Although the effect was greater than the impact of socioeconomic status or race, it was, in real terms, small; a one standard deviation increase in a teacher’s mathematical knowledge for teaching resulted in a 4 percent increase in a student’s rate of learning. In other words, students whose teacher scored highly on the MKT (that is, one standard deviation above the mean) would learn in fifty weeks what a student whose teacher scored the average would learn in fifty-two weeks—a difference, but not a big one. Or, to put it another way, we saw earlier that one standard deviation of teacher quality increases the rate of student learning by around 40 percent, and we have just seen that one standard deviation of mathematical knowledge for teaching increases the rate of student learning by 4 percent. This suggests that subject knowledge accounts for only around 10 percent of the variability in teacher quality.

      While the impact of teacher knowledge on student progress in Hill et al.’s (2005) research may be disappointingly small, this is, in fact, one of the strongest results in the research literature. A study of over thirteen thousand teachers, involving almost one million items of data on over three hundred thousand students in the Los Angeles Unified School District (LAUSD), shows that student progress is unrelated to teachers’ scores on licensure examinations, nor are teachers with advanced degrees more effective (Buddin & Zamarro, 2009). Most surprising, in the study, there is no relationship between LAUSD teachers’ scores on the Reading Instruction Competence Assessment (which all elementary school teachers must pass) and their students’ scores in reading. As the researchers themselves note, since this test is a requirement for all elementary school teachers, those who fail the test are not permitted to teach, and so we cannot conclude that the test is not effective in screening out weaker teachers, but the results do suggest that the relationship between teachers’ knowledge of reading pedagogy and student progress in reading is, at best, weak and perhaps nonexistent (Buddin & Zamarro, 2009).

      In an article in the New Yorker, Malcolm Gladwell (2008a) likens this situation to the difficulties of finding a good quarterback for the National Football League (NFL). Apparently, for most positions, how well a player plays in college predicts how well he will fare in the NFL, but at quarterback, how well a player plays in college seems to be useless at predicting how well he will play in the pros.

      One theory about why good—and often even outstanding—college quarterbacks fail in the NFL is that the professional game is so complex (Gladwell, 2008a). To try to mitigate this discrepancy, all players drafted in the NFL now take the Wonderlic Personnel Test—a fifty-item test that assesses arithmetic, geometric, logical, and verbal reasoning. Unfortunately, as several studies have shown (for example, Mirabile, 2005), there does not appear to be any clear relationship between scores on the Wonderlic and how good a quarterback will be in the NFL. In 1999, for example, of the five quarterbacks taken in the first round of the draft, only one—Donovan McNabb—is likely to end up in the Hall of Fame, and yet his score was the lowest of the five. Other quarterbacks scoring in the same range as McNabb include Dan Marino and Terry Bradshaw—widely agreed to be two of the greatest quarterbacks ever (Mirabile, 2005). Although efforts continue to try to predict who will do well and who will not within the NFL, Gladwell (2008a) suggests that there is increasing acceptance that the only way to find out whether someone will do well in the NFL is to try him out in the NFL.

      The same appears to be true for teaching. It may be that the only way to find out whether someone has what it takes to be a teacher is to try him or her out in the classroom, even though Thomas Kane and Douglas Staiger have estimated that we might need to try out four prospective teachers to get one good one (as cited in Gladwell, 2008a).

      Even if we could identify in advance who would make the best teachers, doing anything useful with that information would take a long time. Suppose, for example, we could predict exactly how good each teacher was going to be. Suppose also that we had the luxury of so many people wanting to be teachers that we could raise the bar to a level whereby only two-thirds of those who are currently entering the profession would get in. Over time, this would certainly raise the quality of teachers. However, if we “raise the bar” for entry into teaching today, it would still be forty years before those who had begun teaching before the raising of the bar finally retired.

      We can’t wait that long. While deselecting the least effective teachers and trying to raise the quality of those entering the profession will have some effects, they are likely to be small, and nothing like the kinds of improvements in teacher quality our students need. If we are serious about securing our economic future, we have to help improve the quality of those teachers already working in our schools—what Marnie Thompson, my former colleague at the Educational Testing Service, calls the “love the one you’re with” strategy.

      Improving educational outcomes is a vital economic necessity, and the only way that we can achieve this is by increasing the quality of the teaching force. Identifying the least effective teachers and deselecting them has a role to play, as does trying to increase the quality of those entering the profession, but as the data and the research studies examined in this chapter have shown, the effects of these measures will be small and will take a long time to materialize. In short, if we rely on these measures to raise student achievement, the benefits will be too small and will arrive too late to maintain the United States’ status as one of the world’s leading economies. Our future economic prosperity, therefore, depends on investing in those teachers already working in our schools.

      chapter 2

      The Case for Formative Assessment

      We’ve discussed how increasing the educational achievement of students is a national economic priority, and the only way to do that is to improve teacher quality. We also saw that deselecting existing teachers and improving the quality of entrants into the profession will have, at best, marginal effects, and so securing our economic future boils down to helping current teachers become more effective.

      This chapter reviews the research on teacher professional development—specifically focusing on learning styles, educational neuroscience, and content-area knowledge—and shows that while there are many possible ways in which we could seek to develop the practice of serving teachers, attention to minute-by-minute and day-to-day formative assessment is likely to have the biggest impact on student outcomes. It continues by discussing the origins of formative assessment and by defining what, exactly, formative assessment is. The chapter concludes by presenting the strategies of formative assessment, which will be the subjects of each subsequent chapter in this book, and by discussing assessment as the bridge between teaching and learning.

      Andrew Leigh (2010) analyzed a data set that includes test scores on ninety thousand Australian elementary school students and found that, as in the American research, whether the teacher has a master’s degree or not makes no difference. He did, however, find a statistically significant relationship between how much a student learns and the experience of the teacher, as seen in figure 2.1.

      Source: Adapted from Leigh, 2010.

       Figure 2.1: Increases in teacher productivity with experience.

      The value added by a teacher increases particularly quickly in the first five years of teaching, but what is most sobering about figure 2.1 is the vertical axis. If a student’s literacy teacher is a twenty-year veteran, the student will learn more than he will if his teacher is a novice, but not much more. In a year with a twenty-year veteran, a student will make an extra half-month’s progress—in other words,