A Warm Welcome From Babe Ruth’s Home Town to the Registry of Efficacy and Effectiveness Studies (REES)

Every baseball season, many home runs are hit by various players across the major leagues. But in all of history, there is one home run that stands out for baseball fans. In the 1932 World Series, Babe Ruth (born in Baltimore!) pointed to the center field fence. He then hit the next pitch over that fence, exactly where he said he would.

Just 86 years later, the U.S. Department of Education, in collaboration with the Society for Research on Educational Effectiveness (SREE), launched a new (figurative) center field fence for educational evaluation. It’s called the Registry of Efficacy and Effectiveness Studies (REES). The purpose of REES is to ask evaluators of educational programs to register their research designs, measures, analyses, and other features in advance. This is roughly the equivalent of asking researchers to point to the center field fence, announcing their intention to hit the ball right there. The reason this matters is that all too often, evaluators carry out evaluations that do not produce desired, positive outcomes on some measures or some analyses. They then report outcomes only on the measures that did show positive outcomes, or they might use different analyses from those initially planned, or only report outcomes for a subset of their full sample. On this last point, I remember a colleague long ago who obtained and re-analyzed data from a large and important national study that studied several cities but only reported data for Detroit. In her analyses of data from the other cities, she found that the results the authors claimed were seen only in Detroit, not in any other city.

REES pre-registration will, over time, make it possible for researchers, reviewers, and funders to find out whether evaluators are reporting all of the findings and all of the analyses as they originally planned them.  I would assume that within a period of years, review facilities such as the What Works Clearinghouse will start requiring pre-registration before accepting studies for its top evidence categories. We will certainly do so for Evidence for ESSA. As pre-registration becomes common (as it surely will, if IES is suggesting or requiring it), review facilities such as WWC and Evidence for ESSA will have to learn how to use the pre-registration information. Obviously, minor changes in research designs or measures may be allowed, especially small changes made before posttests are known. For example, if some schools named in pre-registration are not in the posttest sample, the evaluators might explain that the schools closed (not a problem if this did not upset pretest equivalence), but if they withdrew for other reasons, reviewers would want to know why, and would insist that withdrawn schools be included in any intent-to-treat (ITT) analysis. Other fields, including much of medical research, have been using pre-registration for many years, and I’m sure REES and review facilities in education could learn from their experiences and policies.

What I find most heartening in REES and pre-registration is that it is an indication of how much and how rapidly educational research has matured in a short time. Ten years ago REES could not have been realistically proposed. There was too little high-quality research to justify it, and frankly, few educators or policy makers cared very much about the findings of rigorous research. There is still a long way to go in this regard, but embracing pre-registration is one way we say to our profession and ourselves that the quality of evidence in education can stand up to that in any other field, and that we are willing to hold ourselves accountable for the highest standards.

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In baseball history, Babe Ruth’s “pre-registered” home run in the 1932 series is referred to as the “called shot.” No one had ever done it before, and no one ever did it again. But in educational evaluation, we will soon be calling our shots all the time. And when we say in advance exactly what we are going to do, and then do it, just as we promised, showing real benefits for children, then educational evaluation will take a major step forward in increasing users’ confidence in the outcomes.

 

 

 

Photo credit: Babe Ruth, 1920, unattributed photo [Public domain], via Wikimedia Commons

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.

 

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Two Years of Second Grade? Really?

In a recent blog, Mike Petrilli, President of the Fordham Institute, floated an interesting idea. Given the large numbers of students in high-poverty schools who finish elementary school far behind, what if we gave them all a second year of second grade? (he calls it “2.5”). This, he says, would give disadvantaged schools another year to catch kids up, without all the shame and fuss of retaining them.

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At one level, I love this idea, but not on its merits. One more year of second grade would cost school districts or states the national average per-pupil cost of $11,400. So would I like to have $11,400 more for every child in a school district serving many disadvantaged students? You betcha. But another year of second grade is not in the top hundred things I’d do with it.

Just to give you an idea of what we’re talking about, my state, Maryland, has about 900,000 students in grades K-12. Adding a year of second grade for all of them would cost about $10,260,000,000. If half of them are, say, in Title 1 schools (one indicator of high poverty), that’s roughly $5 billion and change. Thanks, Mike! To be fair, this $5 billion would be spent over a 12-year period, as students go through year 2.5, so let’s say only a half billion a year.

What could Maryland’s schools do with a half billion dollars a year?  Actually, I wrote them a plan, arguing that if Maryland were realistically planning to ensure the success of every child on that state tests, they could do it, but it would not be cheap.

What Maryland, or any state, could do with serious money would be to spend it on proven programs, especially for struggling learners. As one example, consider tutoring. The well-known Reading Recovery program, for instance, uses a very well-trained tutor working one-to-one with a struggling first grader for about 16 weeks. The cost was estimated by Hollands et al. (2016) at roughly $4600. So Petrilli’s second grade offer could be traded for about three years of tutoring, not just for struggling first graders, but for every single student in a high-poverty school. And there are much less expensive forms of tutoring. It would be easy to figure out how every single student in, say, Baltimore, could receive tutoring every single year of elementary school using paraprofessionals and small groups for students with less serious problems and one-to-one tutoring for those with more serious problems (see Slavin, Inns, & Pellegrini, 2018).

Our Evidence for ESSA website lists many proven, highly effective approaches in reading and math. These are all ready to go; the only reason that they are not universally used is that they cost money, or so I assume. And not that much money, in the grand scheme of things.

I don’t understand why, even in this thought experiment, Mike Petrili is unwilling to consider the possibility of spending serious money on programs and practices that have actually been proven to work. But in case anyone wants to follow up on his idea, or at least pilot it in Maryland, please mail me $5 billion, and I will make certain that every student in every high-poverty school in the state does in fact reach the end of elementary school performing at or near grade level. Just don’t expect to see double when you check in on our second graders.

References

Hollands, F. M., Kieffer, M. J., Shand, R., Pan, Y., Cheng, H., & Levin, H. M. (2016). Cost-effectiveness analysis of early reading programs: A demonstration with recommendations for future research. Journal of Research on Educational Effectiveness9(1), 30-53.

Slavin, R. E., Inns, A., Pellegrini, M. & Lake (2018).  Response to proven instruction (RTPI): Enabling struggling learners. Submitted for publication.

Photo credit: By Petty Officer 1st Class Jerry Foltz (https://www.dvidshub.net/image/383907) [Public domain], via Wikimedia Commons

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.

John Hattie is Wrong

John Hattie is a professor at the University of Melbourne, Australia. He is famous for a book, Visible Learning, which claims to review every area of research that relates to teaching and learning. He uses a method called “meta-meta-analysis,” averaging effect sizes from many meta-analyses. The book ranks factors from one to 138 in terms of their effect sizes on achievement measures. Hattie is a great speaker, and many educators love the clarity and simplicity of his approach. How wonderful to have every known variable reviewed and ranked!

However, operating on the principle that anything that looks to be too good to be true probably is, I looked into Visible Learning to try to understand why it reports such large effect sizes. My colleague, Marta Pellegrini from the University of Florence (Italy), helped me track down the evidence behind Hattie’s claims. And sure enough, Hattie is profoundly wrong. He is merely shoveling meta-analyses containing massive bias into meta-meta-analyses that reflect the same biases.

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Part of Hattie’s appeal to educators is that his conclusions are so easy to understand. He even uses a system of dials with color-coded “zones,” where effect sizes of 0.00 to +0.15 are designated “developmental effects,” +0.15 to +0.40 “teacher effects” (i.e., what teachers can do without any special practices or programs), and +0.40 to +1.20 the “zone of desired effects.” Hattie makes a big deal of the magical effect size +0.40, the “hinge point,” recommending that educators essentially ignore factors or programs below that point, because they are no better than what teachers produce each year, from fall to spring, on their own. In Hattie’s view, an effect size of from +0.15 to +0.40 is just the effect that “any teacher” could produce, in comparison to students not being in school at all. He says, “When teachers claim that they are having a positive effect on achievement or when a policy improves achievement, this is almost always a trivial claim: Virtually everything works. One only needs a pulse and we can improve achievement.” (Hattie, 2009, p. 16). An effect size of 0.00 to +0.15 is, he estimates, “what students could probably achieve if there were no schooling” (Hattie, 2009, p. 20). Yet this characterization of dials and zones misses the essential meaning of effect sizes, which are rarely used to measure the amount teachers’ students gain from fall to spring, but rather the amount students receiving a given treatment gained in comparison to gains made by similar students in a control group over the same period. So an effect size of, say, +0.15 or +0.25 could be very important.

Hattie’s core claims are these:

  • Almost everything works
  • Any effect size less than +0.40 is ignorable
  • It is possible to meaningfully rank educational factors in comparison to each other by averaging the findings of meta-analyses.

These claims appear appealing, simple, and understandable. But they are also wrong.

The essential problem with Hattie’s meta-meta-analyses is that they accept the results of the underlying meta-analyses without question. Yet many, perhaps most meta-analyses accept all sorts of individual studies of widely varying standards of quality. In Visible Learning, Hattie considers and then discards the possibility that there is anything wrong with individual meta-analyses, specifically rejecting the idea that the methods used in individual studies can greatly bias the findings.

To be fair, a great deal has been learned about the degree to which particular study characteristics bias study findings, always in a positive (i.e., inflated) direction. For example, there is now overwhelming evidence that effect sizes are significantly inflated in studies with small sample sizes, brief durations, use measures made by researchers or developers, are published (vs. unpublished), or use quasi-experiments (vs. randomized experiments) (Cheung & Slavin, 2016). Many meta-analyses even include pre-post studies, or studies that do not have pretests, or have pretest differences but fail to control for them. For example, I once criticized a meta-analysis of gifted education in which some studies compared students accepted into gifted programs to students rejected for those programs, controlling for nothing!

A huge problem with meta-meta-analysis is that until recently, meta-analysts rarely screened individual studies to remove those with fatal methodological flaws. Hattie himself rejects this procedure: “There is…no reason to throw out studies automatically because of lower quality” (Hattie, 2009, p. 11).

In order to understand what is going on in the underlying meta-analyses in a meta-meta-analysis, is it crucial to look all the way down to the individual studies. As a point of illustration, I examined Hattie’s own meta-meta-analysis of feedback, his third ranked factor, with a mean effect size of +0.79. Hattie & Timperly (2007) located 12 meta-analyses. I found some of the ones with the highest mean effect sizes.

At a mean of +1.24, the meta-analysis with the largest effect size in the Hattie & Timperley (2007) review was a review of research on various reinforcement treatments for students in special education by Skiba, Casey, & Center (1985-86). The reviewers required use of single-subject designs, so the review consisted of a total of 35 students treated one at a time, across 25 studies. Yet it is known that single-subject designs produce much larger effect sizes than ordinary group designs (see What Works Clearinghouse, 2017).

The second-highest effect size, +1.13, was from a meta-analysis by Lysakowski & Walberg (1982), on instructional cues, participation, and corrective feedback. Not enough information is provided to understand the individual studies, but there is one interesting note. A study using a single-subject design, involving two students, had an effect size of 11.81. That is the equivalent of raising a child’s IQ from 100 to 277! It was “winsorized” to the next-highest value of 4.99 (which is like adding 75 IQ points). Many of the studies were correlational, with no controls for inputs, or had no control group, or were pre-post designs.

A meta-analysis by Rummel and Feinberg (1988), with a reported effect size of +0.60, is perhaps the most humorous inclusion in the Hattie & Timperley (2007) meta-meta-analysis. It consists entirely of brief lab studies of the degree to which being paid or otherwise reinforced for engaging in an activity that was already intrinsically motivating would reduce subjects’ later participation in that activity. Rummel & Feinberg (1988) reported a positive effect size if subjects later did less of the activity they were paid to do. The reviewers decided to code studies positively if their findings corresponded to the theory (i.e., that feedback and reinforcement reduce later participation in previously favored activities), but in fact their “positive” effect size of +0.60 indicates a negative effect of feedback on performance.

I could go on (and on), but I think you get the point. Hattie’s meta-meta-analyses grab big numbers from meta-analyses of all kinds with little regard to the meaning or quality of the original studies, or of the meta-analyses.

If you are familiar with the What Works Clearinghouse (2007), or our own Best-Evidence Syntheses (www.bestevidence.org) or Evidence for ESSA (www.evidenceforessa.org), you will know that individual studies, except for studies of one-to-one tutoring, almost never have effect sizes as large as +0.40, Hattie’s “hinge point.” This is because WWC, BEE, and Evidence for ESSA all very carefully screen individual studies. We require control groups, controls for pretests, minimum sample sizes and durations, and measures independent of the treatments. Hattie applies no such standards, and in fact proclaims that they are not necessary.

It is possible, in fact essential, to make genuine progress using high-quality rigorous research to inform educational decisions. But first we must agree on what standards to apply.  Modest effect sizes from studies of practical treatments in real classrooms over meaningful periods of time on measures independent of the treatments tell us how much a replicable treatment will actually improve student achievement, in comparison to what would have been achieved otherwise. I would much rather use a program with an effect size of +0.15 from such studies than to use programs or practices found in studies with major flaws to have effect sizes of +0.79. If they understand the situation, I’m sure all educators would agree with me.

To create information that is fair and meaningful, meta-analysts cannot include studies of unknown and mostly low quality. Instead, they need to apply consistent standards of quality for each study, to look carefully at each one and judge its freedom from bias and major methodological flaws, as well as its relevance to practice. A meta-analysis cannot be any better than the studies that go into it. Hattie’s claims are deeply misleading because they are based on meta-analyses that themselves accepted studies of all levels of quality.

Evidence matters in education, now more than ever. Yet Hattie and others who uncritically accept all studies, good and bad, are undermining the value of evidence. This needs to stop if we are to make solid progress in educational practice and policy.

References

Cheung, A., & Slavin, R. (2016). How methodological features affect effect sizes in education. Educational Researcher, 45 (5), 283-292.

Hattie, J. (2009). Visible learning. New York, NY: Routledge.

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77 (1), 81-112.

Lysakowski, R., & Walberg, H. (1982). Instructional effects of cues, participation, and corrective feedback: A quantitative synthesis. American Educational Research Journal, 19 (4), 559-578.

Rummel, A., & Feinberg, R. (1988). Cognitive evaluation theory: A review of the literature. Social Behavior and Personality, 16 (2), 147-164.

Skiba, R., Casey, A., & Center, B. (1985-86). Nonaversive procedures I the treatment of classroom behavior problems. The Journal of Special Education, 19 (4), 459-481.

What Works Clearinghouse (2017). Procedures handbook 4.0. Washington, DC: Author.

Photo credit: U.S. Farm Security Administration [Public domain], via Wikimedia Commons

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.

 

The Good, the Bad, and the (Un)Promising

The ESSA evidence standards are finally beginning to matter. States are starting the process that will lead them to make school improvement awards to their lowest-achieving schools. The ESSA law is clear that for schools to qualify for these awards, they must agree to implement programs that meet the strong, moderate, or promising levels of the ESSA evidence standards. This is very exciting for those who believe in the power of proven programs to transform schools and benefit children. It is good news for kids, for teachers, and for our profession.

But inevitably, there is bad news with the good. If evidence is to be a standard for government funding, there are bound to be people who disseminate programs lacking high-quality evidence who will seek to bend the definitions to declare themselves “proven.” And there are also bound to be schools and districts that want to keep using what they have always used, or to keep choosing programs based on factors other than evidence, while doing the minimum the law requires.

The battleground is the ESSA “promising” criterion. “Strong” programs are pretty well defined as having significant positive evidence from high-quality randomized studies. “Moderate” programs are pretty well defined as having significant positive evidence from high-quality matched studies. Both “strong” and “moderate” are clearly defined in Evidence for ESSA (www.evidenceforessa.org), and, with a bit of translation, by the What Works Clearinghouse, both of which list specific programs that meet or do not meet these standards.

“Promising,” on the other hand is kind  of . . . squishy. The ESSA evidence standards do define programs meeting “promising” as ones that have statistically significant effects in “well-designed and well-implemented” correlational studies, with controls for inputs (e.g., pretests).  This sounds good, but it is hard to nail down in practice. I’m seeing and hearing about a category of studies that perfectly illustrate the problem. Imagine that a developer commissions a study of a form of software. A set of schools and their 1000 students are assigned to use the software, while control schools and their 1000 students do not have access to the software but continue with business as usual.

Computers routinely produce “trace data” that automatically tells researchers all sorts of things about how much students used the software, what they did with it, how successful they were, and so on.

The problem is that typically, large numbers of students given software do not use it. They may never even hit a key, or they may use the software so little that the researchers rule the software use to be effectively zero. So in a not unusual situation, let’s assume that in the treatment group, the one that got the software, only 500 of the 1000 students actually used the software at an adequate level.

Now here’s the rub. Almost always, the 500 students will out-perform the 1000 controls, even after controlling for pretests. Yet this would be likely to happen even if the software were completely ineffective.

To understand this, think about the 500 students who did use the software and the 500 who did not. The users are probably more conscientious, hard-working, and well-organized. The 500 non-users are more likely to be absent a lot, to fool around in class, to use their technology to play computer games, or go on (non-school-related) social media, rather than to do math or science for example. Even if the pretest scores in the user and non-user groups were identical, they are not identical students, because their behavior with the software is not equal.

I once visited a secondary school in England that was a specially-funded model for universal use of technology. Along with colleagues, I went into several classes. The teachers were teaching their hearts out, making constant use of the technology that all students had on their desks. The students were well-behaved, but just a few dominated the discussion. Maybe the others were just a bit shy, we thought. From the front of each class, this looked like the classroom of the future.

But then, we filed to the back of each class, where we could see over students’ shoulders. And we immediately saw what was going on. Maybe 60 or 70 percent of the students were actually on social media unrelated to the content, paying no attention to the teacher or instructional software!

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Now imagine that a study compared the 30-40% of students who were actually using the computers to students with similar pretests in other schools who had no computers at all. Again, the users would look terrific, but this is not a fair comparison, because all the goof-offs and laggards in the computer school had selected themselves out of the study while goof-offs and laggards in the control group were still included.

Rigorous researchers use a method called intent-to-treat, which in this case would include every student, whether or not they used the software or played non-educational computer games. “Not fair!” responds the software developer, because intent-to-treat includes a lot of students who never touched a key except to use social media. No sophisticated researcher accepts such an argument, however, because including only users gives the experimental group a big advantage.

Here’s what is happening at the policy level. Software developers are using data from studies that only include the students who made adequate use of the software. They are then claiming that such studies are correlational and meet the “promising” standard of ESSA.

Those who make this argument are correct in saying that such studies are correlational. But these studies are very, very, very bad, because they are biased toward the treatment. The ESSA standards specify well-designed and well-implemented studies, and these studies may be correlational, but they are not well-designed or well-implemented. Software developers and other vendors are very concerned about the ESSA evidence standards, and some may use the “promising” category as a loophole. Evidence for ESSA does not accept such studies, even as promising, and the What Works Clearinghouse does not even have any category that corresponds to “promising.” Yet vendors are flooding state departments of education and districts with studies they claim to meet the ESSA standards, though in the lowest category.

Recently, I heard something that could be a solution to this problem. Apparently, some states are announcing that for school improvement grants, and any other purpose that has financial consequences, they will only accept programs with “strong” and “moderate” evidence. They have the right to do this; the federal law says school improvement grants must support programs that at least meet the “promising” standard, but it does not say states cannot set a higher minimum standard.

One might argue that ignoring “promising” studies is going too far. In Evidence for ESSA (www.evidenceforessa.org), we accept studies as “promising” if they have weaknesses that do not lead to bias, such as clustered studies that were significant at the student but not the cluster level. But the danger posed by studies claiming to fit “promising” using biased designs is too great. Until the feds fix the definition of “promising” to exclude bias, the states may have to solve it for themselves.

I hope there will be further development of the “promising” standard to focus it on lower-quality but unbiased evidence, but as things are now, perhaps it is best for states themselves to declare that “promising” is no longer promising.

Eventually, evidence will prevail in education, as it has in many other fields, but on the way to that glorious future, we are going to have to make some adjustments. Requiring that “promising” be truly promising would be a good place to begin.

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.

 

What if a Sears Catalogue Married Consumer Reports?

blog_3-15-18_familyreading_500x454When I was in high school, I had a summer job delivering Sears catalogues. I borrowed my mother’s old Chevy station wagon and headed out fully laden into the wilds of the Maryland suburbs of Washington.

I immediately learned something surprising. I thought of a Sears catalogue as a big book of advertisements. But the people to whom I was delivering them often saw it as a book of dreams. They were excited to get their catalogues. When a neighborhood saw me coming, I became a minor celebrity.

Thinking back on those days, I was thinking about our Evidence for ESSA website (www.evidenceforessa.org). I realized that what I wanted it to be was a way to communicate to educators the wonderful array of programs they could use to improve outcomes for their children. Sort of like a Sears catalogue for education. However, it provides something that a Sears catalogue does not: Evidence about the effectiveness of each catalogue entry. Imagine a Sears catalogue that was married to Consumer Reports. Where a traditional Sears catalogue describes a kitchen gadget, “It slices and dices, with no muss, no fuss!”, the marriage with Consumer Reports would instead say, “Effective at slicing and dicing, but lots of muss. Also fuss.”

If this marriage took place, it might take some of the fun out of the Sears catalogue (making it a book of realities rather than a book of dreams), but it would give confidence to buyers, and help them make wise choices. And with proper wordsmithing, it could still communicate both enthusiasm, when warranted, and truth. But even more, it could have a huge impact on the producers of consumer goods, because they would know that their products would need to be rigorously tested and found to be able to back up their claims.

In enhancing the impact of research on the practice of education, we have two problems that have to be solved. Just like the “Book of Dreams,” we have to help educators know the wonderful array of programs available to them, programs they may never had heard of. And beyond the particular programs, we need to build excitement about the opportunity to select among proven programs.

In education, we make choices not for ourselves, but on behalf of our children. Responsible educators want to choose programs and practices that improve the achievement of their students. Something like a marriage of the Sears catalogue and Consumer Reports is necessary to address educators’ dreams and their need for information on program outcomes. Users should be both excited and informed. Information usually does not excite. Excitement usually does not inform. We need a way to do both.

In Evidence for ESSA, we have tried to give educators a sense that there are many solutions to enduring instructional problems (excitement), and descriptions of programs, outcomes, costs, staffing requirements, professional development, and effects for particular subgroups, for example (information).

In contrast to Sears catalogues, Evidence for ESSA is light (Sears catalogues were huge, and ultimately broke the springs on my mother’s station wagon). In contrast to Consumer Reports, Evidence for ESSA is free.  Every marriage has its problems, but our hope is that we can capture the excitement and the information from the marriage of these two approaches.

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.

Picture source: Nationaal Archief, the Netherlands

 

Evidence for ESSA Celebrates its First Anniversary

Penguin 02 22 18On February 28, 2017 we launched Evidence for ESSA (www.evidenceforessa.org), our website providing the evidence to support educational programs according to the standards laid out in the Every Child Succeeds Act in December, 2015.

Evidence for ESSA began earlier, of course. It really began one day in September, 2016, when I heard leaders of the Institute for Education Sciences (IES) and the What Works Clearinghouse (WWC) announce that the WWC would not be changed to align with the ESSA evidence standards. I realized that no one else was going to create scientifically valid, rapid, and easy-to-use websites providing educators with actionable information on programs meeting ESSA standards. We could do it because our group at Johns Hopkins University, and partners all over the world, had been working for many years creating and updating another website, the Best Evidence Encyclopedia (BEE; www.bestevidence.org).BEE reviews were not primarily designed for practitioners and they did not align with ESSA standards, but at least we were not starting from scratch.

We assembled a group of large membership organizations to advise us and to help us reach thoughtful superintendents, principals, Title I directors, and others who would be users of the final product. They gave us invaluable advice along the way. We also assembled a technical working group (TWG) of distinguished researchers to advise us on key decisions in establishing our website.

It is interesting to note that we have not been able to obtain adequate funding to support Evidence for ESSA. Instead, it is mostly being written by volunteers and graduate students, all of whom are motivated only by a passion for evidence to improve the education of students.

A year after launch, Evidence for ESSA has been used by more than 36,000 unique users, and I hear that it is very useful in helping states and districts meet the ESSA evidence standards.

We get a lot of positive feedback, as well as complaints and concerns, to which we try to respond rapidly. Feedback has been important in changing some of our policies and correcting some errors and we are glad to get it.

At this moment we are thoroughly up-to-date on reading and math programs for grades pre-kindergarten to 12, and we are working on science, writing, social-emotional outcomes, and summer school. We are also continuing to update our more academic BEE reviews, which draw from our work on Evidence for ESSA.

In my view, the evidence revolution in education is truly a revolution. If the ESSA evidence standards ultimately prevail, education will at long last join fields such as medicine and agriculture in a dynamic of practice to development to evaluation to dissemination to better practice, in an ascending spiral that leads to constantly improving practices and outcomes.

In a previous revolution, Thomas Jefferson said, “If I had to choose between government without newspapers and newspapers without government, I’d take the newspapers.” In our evidence revolution in education, Evidence for ESSA, the WWC, and other evidence sources are our “newspapers,” providing the information that people of good will can use to make wise and informed decisions.

Evidence for ESSA is the work of many dedicated and joyful hands trying to provide our profession with the information it needs to improve student outcomes. The joy in it is the joy in seeing teachers, principals, and superintendents see new, attainable ways to serve their children.

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.

Getting the Best Mileage from Proven Programs

Race carWouldn’t you love to have a car that gets 200 miles to the gallon? Or one that can go hundreds of miles on a battery charge? Or one that can accelerate from zero to sixty twice as fast as any on the road?

Such cars exist, but you can’t have them. They are experimental vehicles or race cars that can only be used on a track or in a lab. They may be made of exotic materials, or may not carry passengers or groceries, or may be dangerous on real roads.

In working on our Evidence for ESSA website (www.evidenceforessa.org), we see a lot of studies that are like these experimental cars. For example, there are studies of programs in which the researcher or her graduate students actually did the teaching, or in which students used innovative technology with one adult helper for every machine or every few machines. Such studies are fine for theory building or as pilots, but we do not accept them for Evidence for ESSA, because they could never be replicated in real schools.

However, there is a much more common situation to which we pay very close attention. These are studies in which, for example, teachers receive a great deal of training and coaching, but an amount that seems replicable, in principle. For example, we would reject a study in which the experimenters taught the program, but not one in which they taught ordinary teachers how to use the program.

In such studies, the problem comes in dissemination. If studies validating a program provided a lot of professional development, we would accept it only if the disseminator provides a similar level of professional development, and their estimates of cost and personnel take this level of professional development into account. We put on our website clear expectations that these services be provided at a level similar to what was provided in the research, if the positive outcomes seen in the research are to be obtained.

The problem is that disseminators often offer schools a form of the program that was never evaluated, to keep costs low. They know that schools don’t like to spend a lot on professional development, and they are concerned that if they require the needed levels of PD or other services or materials, schools won’t buy their program. At the extreme end of this, there are programs that were successfully evaluated using extensive professional development, and then put their teacher’s manual on the web for schools to use for free.

A recent study of a program called Mathalicious illustrated the situation. Mathalicious is an on-line math course for middle school. An evaluation found that teachers randomly assigned to just get a license, with minimal training, did not obtain significant positive impacts, compared to a control group. Those who received extensive on-line training, however, did see a significant improvement in math scores, compared to controls.

When we write our program descriptions, we compare program implementation details in the research to what is said or required on the program’s website. If these do not match, within reason, we try to make it clear what were the key elements necessary for success.

Going back to the car analogy, our procedures eliminate those amazing cars that can only operate on special tracks, but we accept cars that can run on streets, carry children and groceries, and generally do what cars are expected to do. But if outstanding cars require frequent recharging, or premium gasoline, or have other important requirements, we’ll say so, in consultation with the disseminator.

In our view, evidence in education is not for academics, it’s for kids. If there is no evidence that a program as disseminated benefits kids, we don’t want to mislead educators who are trying to use evidence to benefit their children.

This blog was developed with support from the Laura and John Arnold Foundation. The views expressed here do not necessarily reflect those of the Foundation.