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 (, 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!


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 (, 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.



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