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Updated Research Guidelines Will Improve Education Technology Products and Provide More Value to Schools

Recommendations include 16 best practices for the design, implementation, and reporting of Usable Evidence for Educators

Palo Alto, CA (April 25, 2018) – Empirical Education Inc. and the Education Technology Industry Network (ETIN) of SIIA released an important update to the “Guidelines for Conducting and Reporting Edtech Impact Research in U.S. K-12 Schools” today.

Authored by Empirical Education researchers, Drs. Denis Newman, Andrew Jaciw, and Valeriy Lazarev, the Guidelines detail 16 best practices for the design, implementation, and reporting of efficacy research of education technology. Recommendations range from completing the product’s logic model before fielding it to disseminating a study’s results in accessible and non-technical language.

The Guidelines were first introduced in July 2017 at ETIN’s Edtech Impact Symposium to address the changing demand for research. They served to address new challenges driven by the accelerated pace of edtech development and product releases, the movement of new software to the cloud, and the passage of the Every Student Succeeds Act (ESSA). The authors committed to making regular updates to keep pace with technical advances in edtech and research methods.

“Our collaboration with ETIN brought the right mix of practical expertise to this important document,” said Denis Newman, CEO of Empirical Education and lead author of the Guidelines. “ETIN provided valuable expertise in edtech marketing, policy, and development. With over a decade of experience evaluating policies, programs, and products for the U.S. Department of Education, major research organizations, and publishers, Empirical Education brought a deep understanding of how studies are traditionally performed and how they can be improved in the future. Our experience with our Evidence as a Service™ offering to investors and developers of edtech products also informed the guidelines.”

The current edition advocates for analysis of usage patterns in the data collected routinely by edtech applications. These patterns help to identify classrooms and schools with adequate implementation and lead to lower-cost faster turn-around research. So rather than investing hundreds of thousands of dollars in a single large-scale study, developers should consider multiple small-scale studies. The authors point to the advantages of looking at subgroup analysis to better understand how and for whom the product works best, thus more directly answering common educator questions. Issues with quality of implementation are addressed in greater depth, and the visual design of the Guidelines has been refined for improved readability.

“These guidelines may spark a rebellion against the research business as usual, which doesn’t help educators know whether an edtech product will work for their specific populations. They also provide a basis for schools and developers to partner to make products better,” said Mitch Weisburgh, Managing Partner of Academic Business Advisors, LLC and President of ETIN, who has moderated panels and webinars on edtech research.

Empirical Education, in partnership with a variety of organizations, is conducting webinars to help explain the updates to the Guidelines, as well as to discuss the importance of these best practices in the age of ESSA. The updated Guidelines are available here: https://www.empiricaleducation.com/research-guidelines/.

How Efficacy Studies Can Help Decision-makers Decide if a Product is Likely to Work in Their Schools

We and our colleagues have been working on translating the results of rigorous studies of the impact of educational products, programs, and policies for people in school districts who are making the decisions whether to purchase or even just try out—pilot—the product. We are influenced by Stanford University Methodologist Lee Cronbach, especially his seminal book (1982) and article (1975) where he concludes “When we give proper weight to local conditions, any generalization is a working hypothesis, not a conclusion…positive results obtained with a new procedure for early education in one community warrant another community trying it. But instead of trusting that those results generalize, the next community needs its own local evaluation” (p. 125). In other words, we consider even the best designed experiment to be like a case study, as much about the local and moderating role of context, as about the treatment when interpreting the causal effect of the program.

Following the focus on context, we can consider characteristics of the people and of the institution where the experiment was conducted to be co-causes of the result that deserve full attention—even though, technically, only the treatment, which was randomly assigned was controlled. Here we argue that any generalization from a rigorous study, where the question is whether the product is likely to be worth trying in a new district, must consider the full context of the study.

Technically, in the language of evaluation research, these differences in who or where the product or “treatment” works are called “interaction effects” between the treatment and the characteristic of interest (e.g., subgroups of students by demographic category or achievement level, teachers with different skills, or bandwidth available in the building). The characteristic of interest can be called a “moderator”, since it changes, or moderates, the impact of the treatment. An interaction reveals if there is differential impact and whether a group with a particular characteristic is advantaged, disadvantaged, or unaffected by the product.

The rules set out by The Department of Education’s What Works Clearinghouse (WWC) focus on the validity of the experimental conclusion: Did the program work on average compared to a control group? Whether it works better for poor kids than for middle class kids, works better for uncertified teachers versus veteran teachers, increases or closes a gap between English learners and those who are proficient, are not part of the information provided in their reviews. But these differences are exactly what buyers need in order to understand whether the product is a good candidate for a population like theirs. If a program works substantially better for English proficient students than for English learners, and the purchasing school has largely the latter type of student, it is important that the school administrator know the context for the research and the result.

The accuracy of an experimental finding depends on it not being moderated by conditions. This is recognized with recent methods of generalization (Tipton, 2013) that essentially apply non-experimental adjustments to experimental results to make them more accurate and more relevant to specific local contexts.

Work by Jaciw (2016a, 2016b) takes this one step further.

First, he confirms the result that if the impact of the program is moderated, and if moderators are distributed differently between sites, then an experimental result from one site will yield a biased inference for another site. This would be the case, for example, if the impact of a program depends on individual socioeconomic status, and there is a difference between the study and inference sites in the proportion of individuals with low socioeconomic status. Conditions for this “external validity bias” are well understood, but the consequences are addressed much less often than the usual selection bias. Experiments can yield accurate results about the efficacy of a program for the sample studied, but that average may not apply either to a subgroup within the sample or to a population outside the study.

Second, he uses results from a multisite trial to show empirically that there is potential for significant bias when inferring experimental results from one subset of sites to other inference sites within the study; however, moderators can account for much of the variation in impact across sites. Average impact findings from experiments provide a summary of whether a program works, but leaves the consumer guessing about the boundary conditions for that effect—the limits beyond which the average effect ceases to apply. Cronbach was highly aware of this, titling a chapter in his 1982 book “The Limited Reach of Internal Validity”. Using terms like “unbiased” to describe impact findings from experiments is correct in a technical sense (i.e., the point estimate, on hypothetical repeated sampling, is centered on the true average effect for the sample studied), but it can impart an incorrect sense of the external validity of the result: that it applies beyond the instance of the study.

Implications of the work cited, are, first, that it is possible to unpack marginal impact estimates through subgroup and moderator analyses to arrive at more-accurate inferences for individuals. Second, that we should do so—why obscure differences by paying attention to only the grand mean impact estimate for the sample? And third, that we should be planful in deciding which subgroups to assess impacts for in the context of individual experiments.

Local decision-makers’ primary concern should be with whether a program will work with their specific population, and to ask for causal evidence that considers local conditions through the moderating role of student, teacher, and school attributes. Looking at finer differences in impact may elicit criticism that it introduces another type of uncertainty—specifically from random sampling error—which may be minimal with gross impacts and large samples, but influential when looking at differences in impact with more and smaller samples. This is a fair criticism, but differential effects may be less susceptible to random perturbations (low power) than assumed, especially if subgroups are identified at individual levels in the context of cluster randomized trials (e.g., individual student-level SES, as opposed to school average SES) (Bloom, 2005; Jaciw, Lin, & Ma, 2016).

References:
Bloom, H. S. (2005). Randomizing groups to evaluate place-based programs. In H. S. Bloom (Ed.), Learning more from social experiments. New York: Russell Sage Foundation.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 116-127.

Cronbach, L. J. (1982). Designing evaluations of educational and social programs. San Francisco, CA: Jossey-Bass.

Jaciw, A. P. (2016). Applications of a within-study comparison approach for evaluating bias in generalized causal inferences from comparison group studies. Evaluation Review, (40)3, 241-276. Retrieved from https://journals.sagepub.com/doi/abs/10.1177/0193841X16664457

Jaciw, A. P. (2016). Assessing the accuracy of generalized inferences from comparison group studies using a within-study comparison approach: The methodology. Evaluation Review, (40)3, 199-240. Retrieved from https://journals.sagepub.com/doi/abs/10.1177/0193841x16664456

Jaciw, A., Lin, L., & Ma, B. (2016). An empirical study of design parameters for assessing differential impacts for students in group randomized trials. Evaluation Review. Retrieved from https://journals.sagepub.com/doi/10.1177/0193841X16659600

Tipton, E. (2013). Improving generalizations from experiments using propensity score subclassification: Assumptions, properties, and contexts. Journal of Educational and Behavioral Statistics, 38, 239-266.

Empirical Presents about Aspire Public School’s t3 System at AEA 2013

Empirical Education presented at the annual conference of the American Evaluation Association (AEA) in Washington, DC. Our newest research manager, Kristen Koue, along with our chief scientist, Andrew Jaciw reflected on striking the right balance between conducting a rigorous randomized control trial that meets i3 grant parameters, while also conducting an implementation evaluation that provides useful formative feedback to the Aspire population.

Study Shows a “Singapore Math” Curriculum Can Improve Student Problem Solving Skills

A study of HMH Math in Focus (MIF) released today by research firm Empirical Education Inc. demonstrates a positive impact of the curriculum on Clark County School District elementary students’ math problem solving skills. The 2011-2012 study was contracted by the publisher, which left the design, conduct, and reporting to Empirical. MIF provides elementary math instruction based on the pedagogical approach used in Singapore. The MIF approach to instruction is designed to support conceptual understanding, and is said to be closely aligned with the Common Core State Standards (CCSS), which focuses more on in-depth learning than previous math standards.

Empirical found an increase in math problem solving among students taught with HMH Math in Focus compared to their peers. The Clark County School District teachers also reported an increase in their students’ conceptual understanding, as well as an increase in student confidence and engagement while explaining and solving math problems. The study addressed the difference between the CCSS-oriented MIF and the existing Nevada math standards and content. While MIF students performed comparatively better on complex problem solving skills, researchers found that students in the MIF group performed no better than the students in the control group on the measure of math procedures and computation skills. There was also no significant difference between the groups on the state CRT assessment, which has not fully shifted over to the CCSS.

The research used a group randomized control trial to examine the performance of students in grades 3-5 during the 2011-2012 school year. Each grade-level team was randomly assigned to either the treatment group that used MIF or the control group that used the conventional math curriculum. Researchers used three different assessments to capture math achievement contrasting procedural and problem solving skills. Additionally, the research design employed teacher survey data to conduct mediator analyses (correlations between percentage of math standards covered and student math achievement) and assess fidelity of classroom implementation.

You can download the report and research summary from the study using the links below.
Math in Focus research report
Math in Focus research summary

Empirical Releases Final Report on HMH Fuse™ iPad App

Today Empirical and Houghton Mifflin Harcourt made the following announcement. You can download the report and research summary from the study using the links below.
Fuse research report
Fuse research summary

Study Shows HMH Fuse™ iPad® App Can Dramatically Improve Student Achievement

Strong implementation in Riverside Unified School District associated with nine-point increase in percentile standing

BOSTON – April 10, 2012 – A study of HMH Fuse: Algebra 1 app released today by research firm Empirical Education Inc. identifies implementation as a key factor in the success of mobile technology. The 2010–2011 study was a pilot of a new educational app from global education leader Houghton Mifflin Harcourt (HMH) that re-imagines the conventional textbook to fully deploy interactive features of the mobile device. The HMH Fuse platform encourages the use of personalized lesson plans by combining direct instruction, ongoing support, assessment and intervention in one easy-to-use suite of tools.

Empirical found that the iPad-using students in the four participating districts: Long Beach, Fresno, San Francisco and Riverside Unified School District (Riverside Unified), performed on average as well as their peers using the traditional textbook. However, after examining its own results, Riverside Unified found an increase in test scores among students taught with HMH Fuse compared to their peers. Empirical corroborated these results, finding a statistically significant impact equivalent to a nine-point percentile increase. The Riverside Unified teachers also reported substantially greater usage of the HMH Fuse app both in teaching and by the students in class.

“Education technology does not operate in a vacuum, and the research findings reinforce that with a supportive school culture and strategic implementation, technology can have a significant impact on student achievement,” said Linda Zecher, President and CEO of HMH. “We’re encouraged by the results of the study and the potential of mobile learning to accelerate student achievement and deepen understanding in difficult to teach subjects like algebra.”

Across all districts, the study found a positive effect on student attitudes toward math, and those students with positive attitudes toward math achieved higher scores on the California Standards Test.

The research design was a “gold standard” randomized control trial that examined the performance of eighth-grade students during the 2010-2011 school year. Each teacher’s classes were randomly assigned to either the treatment group that used the HMH Fuse app or the control group that used the conventional print format of the same content.

“The rapid pace of mobile technology’s introduction into K-12 education leaves many educators with important questions about its efficacy especially given their own resources and experience,” said Denis Newman, CEO of Empirical Education. “The results from Riverside highlight the importance of future research on mobile technologies that account for differences in teacher experience and implementation.”

To access the full research report, go to www.empiricaleducation.com. A white paper detailing the implementation and impact of HMH Fuse in Riverside is available on the HMH website.

Comment on the NY Times: In Classroom of Future, Stagnant Scores

The New York Times is running a series of front-page articles on “Grading the Digital School.” The first one ran Labor Day weekend and raised the question as to whether there’s any evidence that would persuade a school board or community to allocate extra funds for technology. With the demise of the Enhancing Education Through Technology (EETT) program, federal funds dedicated to technology will no longer be flowing into states and districts. Technology will have to be measured against any other discretionary purchase. The resulting internal debates within schools and their communities about the expense vs. value of technology promise to have interesting implications and are worth following closely.

The first article by Matt Richtel revisits a debate that has been going on for decades between those who see technology as the key to “21st Century learning” and those who point to the dearth of evidence that technology makes any measurable difference to learning. It’s time to try to reframe this discussion in terms of what can be measured. And in considering what to measure, and in honor of Labor Day, we raise a question that is often ignored: what role do teachers play in generating the measurable value of technology?

Let’s start with the most common argument in favor of technology, even in the absence of test score gains. The idea is that technology teaches skills “needed in a modern economy,” and these are not measured by the test scores used by state and federal accountability systems. Karen Cator, director of the U.S. Department of Education office of educational technology, is quoted as saying (in reference to the lack of improvement in test scores), “…look at all the other things students are doing: learning to use the Internet to research, learning to organize their work, learning to use professional writing tools, learning to collaborate with others.” Presumably, none of these things directly impact test scores. The problem with this perennial argument is that many other things that schools keep track of should provide indicators of improvement. If as a result of technology, students are more excited about learning or more engaged in collaborating, we could look for an improvement in attendance, a decrease in drop-outs, or students signing up for more challenging courses.

Information on student behavioral indicators is becoming easier to obtain since the standardization of state data systems. There are some basic study designs that use comparisons among students within the district or between those in the district and those elsewhere in the state. This approach uses statistical modeling to identify trends and control for demographic differences, but is not beyond the capabilities of many school district research departments¹ or the resources available to the technology vendors. (Empirical has conducted research for many of the major technology providers, often focusing on results for a single district interested in obtaining evidence to support local decisions.) Using behavioral or other indicators, a district such as that in the Times article can answer its own questions. Data from the technology systems themselves can be used to identify users and non-users and to confirm the extent of usage and implementation. It is also valuable to examine whether some students (those in most need or those already doing okay) or some teachers (veterans or novices) receive greater benefit from the technology. This information may help the district focus resources where they do the most good.

A final thought about where to look for impacts of technologies comes from a graph of the school district’s budget. While spending on technology and salaries have both declined over the last three years, spending on salaries is still about 25 times as great as on technologies. Any discussion of where to find an impact of technology must consider labor costs, which are the district’s primary investment. We might ask whether a small investment in technology would allow the district to reduce the numbers of teachers by, for example, allowing a small increase in the number of students each teacher can productively handle. Alternatively, we might ask whether technology can make a teacher more effective, by whatever measures of effective teaching the district chooses to use, with their current students. We might ask whether technologies result in keeping young teachers on the job longer or encouraging initiative to take on more challenging assignments.

It may be a mistake to look for a direct impact of technology on test scores (aside from technologies aimed specifically at that goal), but it is also a mistake to assume the impact is, in principle, not measurable. We need a clear picture of how various technologies are expected to work and where we can look for the direct and indirect effects. An important role of technology in the modern economy is providing people with actionable evidence. It would be ironic if education technology was inherently opaque to educational decision makers.

¹ Or we would hope, the New York Times. Sadly, the article provides a graph of trends in math and reading for the district highlighted in the story compared to trends for the state. The graphic is meant to show that the district is doing worse than the state average. But the article never suggests that we should consider the population of the particular district and whether it is doing better or worse than one would expect, controlling for demographics, available resources, and other characteristics.

A Conversation About Building State and Local Research Capacity

John Q Easton, director of the Institute of Education Sciences (IES), came to New Orleans recently to participate in the annual meeting of the American Educational Research Association. At one of his stops, he was the featured speaker at a meeting of the Directors of Research and Evaluation (DRE), an organization composed of school district research directors. (DRE is affiliated with AERA and was recently incorporated as a 501©(3)). John started his remarks by pointing out that for much of his career he was a school district research director and felt great affinity to the group. He introduced the directions that IES was taking, especially how it was approaching working with school systems. He spent most of the hour fielding questions and engaging in discussion with the participants. Several interesting points came out of the conversation about roles for the researchers who work for education agencies.

Historically, most IES research grant programs have been aimed at university or other academic researchers. It is noteworthy that even in a program for “Evaluation of State and Local Education Programs and Policies,” grants have been awarded only to universities and large research firms. There is no expectation that researchers working for the state or local agency would be involved in the research beyond the implementation of the program. The RFP for the next generation of Regional Education Labs (REL) contracts may help to change that. The new RFP expects the RELs to work closely with education agencies to define their research questions and to assist alliances of state and local agencies in developing their own research capacity.

Members of the audience noted that, as district directors of research, they often spend more time reviewing research proposals from students and professors at local colleges who want to conduct research in their schools, rather than actually answering questions initiated by the district. Funded researchers treat the districts as the “human subjects,” paying incentives to participants and sometimes paying for data services. But the districts seldom participate in defining the research topic, conducting the studies, or benefiting directly from the reported findings. The new mission of the RELs to build local capacity will be a major shift.

Some in the audience pointed out reasons to be skeptical that this REL agenda would be possible. How can we build capacity if research and evaluation departments across the country are being cut? In fact, very little is known about the number of state or district practitioners whose capacity for research and evaluation could be built by applying the REL resources. (Perhaps, a good first research task for the RELs would be to conduct a national survey to measure the existing capacity.)

John made a good point in reply: IES and the RELs have to work with the district leadership—not just the R&E departments—to make this work. The leadership has to have a more analytic view. They need to see the value of having an R&E department that goes beyond test administration, and is able to obtain evidence to support local decisions. By cultivating a research culture in the district, evaluation could be routinely built in to new program implementations from the beginning. The value of the research would be demonstrated in the improvements resulting from informed decisions. Without a district leadership team that values research to find out what works for the district, internal R&E departments will not be seen as an important capacity.

Some in the audience pointed out that in parallel to building a research culture in districts, it will be necessary to build a practitioner culture among researchers. It would be straightforward for IES to require that research grantees and contractors engage the district R&E staff in the actual work, not just review the research plan and sign the FERPA agreement. Practitioners ultimately hold the expertise in how the programs and research can be implemented successfully in the district, thus improving the overall quality and relevance of the research.

Looking Back 35 Years to Learn about Local Experiments

With the growing interest among federal agencies in building local capacity for research, we took another look at an article by Lee Cronbach published in 1975. We found it has a lot to say about conducting local experiments and implications for generalizability. Cronbach worked for much of his career at Empirical’s neighbor, Stanford University, and his work has had a direct and indirect influence on our thinking. Some may interpret Cronbach’s work as stating that randomized trials of educational interventions have no value because of the complexity of interactions between subjects, contexts, and the experimental treatment. In any particular context, these interactions are infinitely complex, forming a “hall of mirrors” (as he famously put it, p. 119), making experimental results—which at most can address a small number of lower-order interactions—irrelevant. We don’t read it that way. Rather, we see powerful insights as well as cautions for conducting the kinds of field experiments that are beginning to show promise for providing educators with useful evidence.

We presented these ideas at the Society for Research in Educational Effectiveness conference in March, building the presentation around a set of memorable quotes from the 1975 article. Here we highlight some of the main ideas.

Quote #1: “When we give proper weight to local conditions, any generalization is a working hypothesis, not a conclusion…positive results obtained with a new procedure for early education in one community warrant another community trying it. But instead of trusting that those results generalize, the next community needs its own local evaluation” (p. 125).

Practitioners are making decisions for their local jurisdiction. An experiment conducted elsewhere (including over many locales, where the results are averaged) provides a useful starting point, but not “proof” that it will or will not work in the same way locally. Experiments give us a working hypothesis concerning an effect, but it has to be tested against local conditions at the appropriate scale of implementation. This brings to mind California’s experience with class size reduction following the famous experiment in Tennessee, and how the working hypothesis corroborated through the experiment did not transfer to a different context. We also see applicability of Cronbach’s ideas in the Investing in Innovation (i3) program, where initial evidence is being taken as a warrant to scale-up intervention, but where the grants included funding for research under new conditions where implementation may head in unanticipated directions, leading to new effects.

Quote #2: “Instead of making generalization the ruling consideration in our research, I suggest that we reverse our priorities. An observer collecting data in one particular situation…will give attention to whatever variables were controlled, but he will give equally careful attention to uncontrolled conditions…. As results accumulate, a person who seeks understanding will do his best to trace how the uncontrolled factors could have caused local departures from the modal effect. That is, generalization comes late, and the exception is taken as seriously as the rule” (pp. 124-125).

Finding or even seeking out conditions that lead to variation in the treatment effect facilitates external validity, as we build an account of the variation. This should not be seen as a threat to generalizability because an estimate of average impact is not robust across conditions. We should spend some time looking at the ways that the intervention interacts differently with local characteristics, in order to determine which factors account for heterogeneity in the impact and which ones do not. Though this activity is exploratory and not necessarily anticipated in the design, it provides the basis for understanding how the treatment plays out, and why its effect may not be constant across settings. Over time, generalizations can emerge, as we compile an account of the different ways in which the treatment is realized and the conditions that suppress or accentuate its effects.

Quote #3: “Generalizations decay” (p. 122).

In the social policy arena, and especially with the rapid development of technologies, we can’t expect interventions to stay constant. And we certainly can’t expect the contexts of implementation to be the same over many years. The call for quicker turn-around in our studies is therefore necessary, not just because decision-makers need to act, but because any finding may have a short shelf life.

Cronbach, L. J. (1975). Beyond the two disciplines of scientifi­c psychology. American Psychologist, 116-127.

Making Vendor Research More Credible

The latest evidence that research can be both rigorous and relevant was the subject of an announcement that the Software and Information Industry Association (SIIA) made last month about their new guidelines for conducting effectiveness research. The document is aimed at SIIA members, most of whom are executives of education software and technology companies and not necessarily schooled in research methodology. The main goal in publishing the guidelines is to improve the quality—and therefore the credibility—of research sponsored by the industry. The document provides SIIA members with things to keep in mind when contracting for research or using research in marketing materials. The document also has value for educators, especially those responsible for purchasing decisions. That’s an important point that I’ll get back to.

One thing to make clear in this blog entry is that while your humble blogger (DN) is given credit as the author, the Guidelines actually came from a working group of SIIA members who put in many months of brainstorming, discussion, and review. DN’s primary contribution was just to organize the ideas, ensure they were technically accurate, and put them into easy to understand language.

Here’s a taste of some of the ideas contained in the 22 guidelines:

• With a few exceptions, all research should be reported regardless of the result. Cherry picking just the studies with strong positive results distorts the facts and in the long run hurts credibility. One lesson that might be taken from this is that conducting several small studies may be preferable to trying to prove a product effective (or not) in a single study.

• Always provide a link to the full report. Too often in marketing materials (including those of advocacy groups, not just publishers) a fact such as “8th grade math achievement increased from 31% in 2004 to 63% in 2005,” is offered with no citation. In this specific case, the fact was widely cited but after considerable digging could be traced back to a report described by the project director as “anecdotal”.

• Be sure to take implementation into account. In education, all instructional programs require setting up complex systems of teacher-student interaction, which can vary in numerous ways. Issues of how research can support the process and what to do with inadequate or outright failed implementation must be understood by researchers and consumers of research.

• Watch out for the control condition. In education there are no placebos. In almost all cases we are comparing a new program to whatever is in place. Depending on how well the existing program works, the program being evaluated may appear to have an impact or not. This calls for careful consideration of where to test a product and understandable concern by educators as to how well a particular product tested in another district will perform against what is already in place in their district.

The Guidelines are not just aimed at industry. SIIA believes that as decision-makers at schools begin to see a commitment to providing stronger research, their trust in the results will increase. It is also in the educators’ interest to review the guidelines because they provide a reference point for what actionable research should look like. Ultimately, the Guidelines provide educators with help in conducting their own research, whether it is on their own or in partnership with the education technology providers.

i3 Request for Proposals Calls for New Approaches to Rigorous Evaluation

In the strongest indication yet that the new administration is serious about learning from its multi-billion-dollar experience, the draft notice for the Invest in Innovation (i3) grants sets out new requirements for research and evaluation. While it is not surprising that the U.S. Department of Education requires scientific evidence for programs asking for funds for expansion and scaling up, it is important to note that strong evidence is now being defined not just in terms of rigorous methods but also in terms of “studies that in total include enough of the range of participants and settings to support scaling up to the State, regional, or national level.” This requirement for generalizability is a major step toward sponsoring research that has value for practical decisions. Along the same lines, high quality evaluations are those that include implementation data and performance feedback.

The draft notice also includes recognition of an important research design: “interrupted time series.” While not acceptable under the current What Works Clearinghouse criteria, this method—essentially looking for a change in a series of measures taken before and after implementing a new program—has enormous practical application for schools systems with solid longitudinal data systems.

Finally, we notice that ED is requiring that all evaluators cooperate with broader national efforts to combine evidence from multiple sources and will provide technical assistance to evaluators to assure consistency among researchers. They want to be sure at the end of the process they have useful evidence about what worked, what didn’t, and why.