blog posts and news stories

The Rebel Alliance is Growing

The rebellion against the old NCLB way of doing efficacy research is gaining force. A growing community among edtech developers, funders, researchers, and school users has been meeting in an attempt to reach a consensus on an alternative built on ESSA.

This is being assisted by openness in the directions currently being pursued by IES. In fact, we are moving into a new phase marked by two-way communication with the regime. While the rebellion hasn’t yet handed over its lightsabers, it is encouraged by the level of interest from prominent researchers.

From these ongoing discussions, there have been some radical suggestions inching toward consensus. A basic idea now being questioned is this:

The difference between the average of the treatment group and the average of the control group is a valid measure of effectiveness.

There are two problems with this:

  1. In schools, there’s no “placebo” or something that looks like a useful program but is known to have zero effectiveness. Whatever is going on in the schools, or classes, or with teachers and students in the control condition has some usefulness or effectiveness. The usefulness of the activities in the control classes or schools may be greater than the activities being evaluated in the study, or may be not as useful. The study may find that the “effectiveness” of the activities being studied is positive, negative, or too small to be discerned statistically by the study. In any case, the size (negative or positive) of the effect is determined as much by what’s being done in the control group as the treatment group.
  2. Few educational activities have the same level of usefulness for all teachers and students. Looking at only the average will obscure the differences. For example, we ran a very large study for the U.S. Department of Education of a STEM program where we found, on average, the program was effective. What the department didn’t report was that it only worked for the white kids, not the black kids. The program increased instead of reducing the existing achievement gap. If you are considering adopting this STEM program, the impact on the different subgroups is relevant–a high minority school district may want to avoid it. Also, to make the program better, the developers need to know where it works and where it doesn’t. Again, the average impact is not just meaningless but also can be misleading.

A solution to the overuse of the average difference from studies is to conduct a lot more studies. The price the ED paid for our large study could have paid for 30 studies of the kind we are now conducting in the same state of the same program; in 10% of the time of the original study. If we had 10 different studies for each program, where studies are conducted in different school districts with different populations and levels of resources, the “average” across these studies start to make sense. Importantly, the average across these 10 studies for each of the subgroups will give a valid picture of where, how, and with which students and teachers the program tends to work best. This kind of averaging used in research is called meta-analysis and allows many small differences found across studies to build on the power of each study to generate reliable findings.

If developers or publishers of the products being used in schools took advantage of their hundreds of implementations to gather data, and if schools would be prepared to share student data for this research, we could have researcher findings that both help schools decide what will likely work for them and help developers improve their products.

2018-09-21

New Project with ALSDE to Study AMSTI

Empirical Education is excited to announce a new study of the Alabama Math, Science, and Technology Initiative (AMSTI) commissioned by the Alabama legislature. AMSTI is the Alabama State Department of Education’s initiative to improve math and science teaching statewide; the program, which started over 20 years ago and now operates in over 900 schools across the state, has been validated multiple times by external evaluators, including Empirical’s own 2012 study directed by Chief Scientist Andrew Jaciw. That cluster-randomized trial, which involved 82 schools and ~700 teachers, assessed the efficacy of AMSTI over a three year period and showed an overall positive effect (Newman et al., 2012).

The new study will use a quasi-experimental matched comparison group design and take advantage of existing data available from the Alabama State Department of Education and the AMSTI program. Schools using AMSTI will be compared to matched schools not using AMSTI to determine the impact of the program on math and science achievement for students in grades 3 through 8, as well as differential impacts of the program on important student subgroups. We will also make use of Improvement Science principles to examine school climates where the program may have greater or reduced impact.

At the conclusion of the study, Empirical Education’s report will be made available to select committees of the Alabama state legislature, the Governor and the Alabama State Board of Education, and the Alabama State Department of Education. Researchers from Empirical Education will also travel to Montgomery, Alabama to present the study findings and recommendations for improvement to the Alabama legislature.

2018-07-13

A Rebellion Against the Current Research Regime

Finally! There is a movement to make education research more relevant to educators and edtech providers alike.

At various conferences, we’ve been hearing about a rebellion against the “business as usual” of research, which fails to answer the question of, “Will this product work in this particular school or community?” For educators, the motive is to find edtech products that best serve their students’ unique needs. For edtech vendors, it’s an issue of whether research can be cost-effective, while still identifying a product’s impact, as well as helping to maximize product/market fit.

The “business as usual” approach against which folks are rebelling is that of the U.S. Education Department (ED). We’ll call it the regime. As established by the Education Sciences Reform Act of 2002 and the Institute of Education Sciences (IES), the regime anointed the randomized control trial (or RCT) as the gold standard for demonstrating that a product, program, or policy caused an outcome.

Let us illustrate two ways in which the regime fails edtech stakeholders.

First, the regime is concerned with the purity of the research design, but not whether a product is a good fit for a school given its population, resources, etc. For example, in an 80-school RCT that the Empirical team conducted under an IES contract on a statewide STEM program, we were required to report the average effect, which showed a small but significant improvement in math scores (Newman et al., 2012). The table on page 104 of the report shows that while the program improved math scores on average across all students, it didn’t improve math scores for minority students. The graph that we provide here illustrates the numbers from the table and was presented later at a research conference.

bar graph representing math, science, and reading scores for minority vs non-minority students

IES had reasons couched in experimental design for downplaying anything but the primary, average finding, however this ignores the needs of educators with large minority student populations, as well as of edtech vendors that wish to better serve minority communities.

Our RCT was also expensive and took many years, which illustrates the second failing of the regime: conventional research is too slow for the fast-moving innovative edtech development cycles, as well as too expensive to conduct enough research to address the thousands of products out there.

These issues of irrelevance and impracticality were highlighted last year in an “academic symposium” of 275 researchers, edtech innovators, funders, and others convened by the organization now called Jefferson Education Exchange (JEX). A popular rallying cry coming out of the symposium is to eschew the regime’s brand of research and begin collecting product reviews from front-line educators. This would become a Consumer Reports for edtech. Factors associated with differences in implementation are cited as a major target for data collection. Bart Epstein, JEX’s CEO, points out: “Variability among and between school cultures, priorities, preferences, professional development, and technical factors tend to affect the outcomes associated with education technology. A district leader once put it to me this way: ‘a bad intervention implemented well can produce far better outcomes than a good intervention implemented poorly’.”

Here’s why the Consumer Reports idea won’t work. Good implementation of a program can translate into gains on outcomes of interest, such as improved achievement, reduction in discipline referrals, and retention of staff, but only if the program is effective. Evidence that the product caused a gain on the outcome of interest is needed or else all you measure is the ease of implementation and student engagement. You wouldn’t know if the teachers and students were wasting their time with a product that doesn’t work.

We at Empirical Education are joining the rebellion. The guidelines for research on edtech products we recently prepared for the industry and made available here is a step toward showing an alternative to the regime while adopting important advances in the Every Student Succeeds Act (ESSA).

We share the basic concern that established ways of conducting research do not answer the basic question that educators and edtech providers have: “Is this product likely to work in this school?” But we have a different way of understanding the problem. From years of working on federal contracts (often as a small business subcontractor), we understand that ED cannot afford to oversee a large number of small contracts. When there is a policy or program to evaluate, they find it necessary to put out multi-million-dollar, multi-year contracts. These large contracts suit university researchers, who are not in a rush, and large research companies that have adjusted their overhead rates and staffing to perform on these contracts. As a consequence, the regime becomes focused on the perfection in the design, conduct, and reporting of the single study that is intended to give the product, program, or policy a thumbs-up or thumbs-down.

photo of students in a classroom on computers

There’s still a need for a causal research design that can link conditions such as resources, demographics, or teacher effectiveness with educational outcomes of interest. In research terminology, these conditions are called “moderators,” and in most causal study designs, their impact can be measured.

The rebellion should be driving an increase the number of studies by lowering their cost and turn-around time. Given our recent experience with studies of edtech products, this reduction can reach a factor of 100. Instead of one study that costs $3 million and takes 5 years, think in terms of a hundred studies that cost $30,000 each and are completed in less than a month. If for each product, there are 5 to 10 studies that are combined, they would provide enough variation and numbers of students and schools to detect differences in kinds of schools, kinds of students, and patterns of implementation so as to find where it works best. As each new study is added, our understanding of how it works and with whom improves.

It won’t be enough to have reviews of product implementation. We need an independent measure of whether—when implemented well—the intervention is capable of a positive outcome. We need to know that it can make (i.e., cause) a difference AND under what conditions. We don’t want to throw out research designs that can detect and measure effect sizes, but we should stop paying for studies that are slow and expensive.

Our guidelines for edtech research detail multiple ways that edtech providers can adapt research to better work for them, especially in the era of ESSA. Many of the key recommendations are consistent with the goals of the rebellion:

  • The usage data collected by edtech products from students and teachers gives researchers very precise information on how well the program was implemented in each school and class. It identifies the schools and classes where implementation met the threshold for which the product was designed. This is a key to lowering cost and turn-around time.
  • ESSA offers four levels of evidence which form a developmental sequence, where the base level is based on existing learning science and provides a rationale for why a school should try it. The next level looks for a correlation between an important element in the rationale (measured through usage of that part of the product) and a relevant outcome. This is accepted by ESSA as evidence of promise, informs the developers how the product works, and helps product marketing teams get the right fit to the market. a pyramid representing the 4 levels of ESSA
  • The ESSA level that provides moderate evidence that the product caused the observed impact requires a comparison group matched to the students or schools that were identified as the users. The regime requires researchers to report only the difference between the user and comparison groups on average. Our guidelines insist that researchers must also estimate the extent to which an intervention is differentially effective for different demographic categories or implementation conditions.

From the point of view of the regime, nothing in these guidelines actually breaks the rules and regulations of ESSA’s evidence standards. Educators, developers, and researchers should feel empowered to collect data on implementation, calculate subgroup impacts, and use their own data to generate evidence sufficient for their own decisions.

A version of this article was published in the Edmarket Essentials magazine.

2018-05-09

Recognizing Success

When the Obama-Duncan administration approaches teacher evaluation, the emphasis is on recognizing success. We heard that clearly in Arne Duncan’s comments on the release of teacher value-added modeling (VAM) data for LA Unified by the LA Times. He’s quoted as saying, “What’s there to hide? In education, we’ve been scared to talk about success.” Since VAM is often thought of as a method for weeding out low performing teachers, Duncan’s statement referencing success casts the use of VAM in a more positive light. Therefore we want to raise the issue here: how do you know when you’ve found success? The general belief is that you’ll recognize it when you see it. But sorting through a multitude of variables is not a straightforward process, and that’s where research methods and statistical techniques can be useful. Below we illustrate how this plays out in teacher and in program evaluation.

As we report in our news story, Empirical is participating in the Gates Foundation project called Measures of Effective Teaching (MET). This project is known for its focus on value-added modeling (VAM) of teacher effectiveness. It is also known for having collected over 10,000 videos from over 2,500 teachers’ classrooms—an astounding accomplishment. Research partners from many top institutions hope to be able to identify the observable correlates for teachers whose students perform at high levels as well as for teachers whose students do not. (The MET project tested all the students with an “alternative assessment” in addition to using the conventional state achievement tests.) With this massive sample that includes both data about the students and videos of teachers, researchers can identify classroom practices that are consistently associated with student success. Empirical’s role in MET is to build a web-based tool that enables school system decision-makers to make use of the data to improve their own teacher evaluation processes. Thus they will be able to build on what’s been learned when conducting their own mini-studies aimed at improving their local observational evaluation methods.

When the MET project recently had its “leads” meeting in Washington DC, the assembled group of researchers, developers, school administrators, and union leaders were treated to an after-dinner speech and Q&A by Joanne Weiss. Joanne is now Arne Duncan’s chief of staff, after having directed the Race to the Top program (and before that was involved in many Silicon Valley educational innovations). The approach of the current administration to teacher evaluation—emphasizing that it is about recognizing success—carries over into program evaluation. This attitude was clear in Joanne’s presentation, in which she declared an intention to “shine a light on what is working.” The approach is part of their thinking about the reauthorization of ESEA, where more flexibility is given to local decision- makers to develop solutions, while the federal legislation is more about establishing achievement goals such as being the leader in college graduation.

Hand in hand with providing flexibility to find solutions, Joanne also spoke of the need to build “local capacity to identify and scale up effective programs.” We welcome the idea that school districts will be free to try out good ideas and identify those that work. This kind of cycle of continuous improvement is very different from the idea, incorporated in NCLB, that researchers will determine what works and disseminate these facts to the practitioners. Joanne spoke about continuous improvement, in the context of teachers and principals, where on a small scale it may be possible to recognize successful teachers and programs without research methodologies. While a teacher’s perception of student progress in the classroom may be aided by regular assessments, the determination of success seldom calls for research design. We advocate for a broader scope, and maintain that a cycle of continuous improvement is just as much needed at the district and state levels. At those levels, we are talking about identifying successful schools or successful programs where research and statistical techniques are needed to direct the light onto what is working. Building research capacity at the district and state level will be a necessary accompaniment to any plan to highlight successes. And, of course, research can’t be motivated purely by the desire to document the success of a program. We have to be equally willing to recognize failure. The administration will have to take seriously the local capacity building to achieve the hoped-for identification and scaling up of successful programs.

2010-11-18
Archive