blog posts and news stories

Instructional Coaching: Positive Impacts on Edtech Use and Student Learning

In 2019, Digital Promise contracted with Empirical Education to evaluate the impact of the Dynamic Learning Project (DLP) on teacher and student edtech usage and on student achievement. DLP provided school-based instructional technology coaches with mentoring and professional developing, with the goal to increase educational equity and impactful use of technology. You may have seen the blog post we published in summer 2020 announcing the release of our design memo for the study. The importance of this project was magnified during the pandemic-induced shift to an increased use of online tools. 

The results of the study are summarized in this research brief published last month. We found evidence of positive impacts on edtech use and student learning across three districts involved in DLP.  

These findings make a contribution to the evidence base for how to drive meaningful technology use in schools. This should continue to be an area of investigation for future studies; districts focused on equity and inclusion must ensure that edtech is adopted broadly across teacher and student populations.

Updating Evidence According to ESSA

The U.S. Department of Education (ED) sets validity standards for evidence of what works in schools through The Every Student Succeeds Act (ESSA), which provides usefully defined tiers of evidence.

When we helped develop the research guidelines for the Software & Information Industry Association, we took a close look at ESSA and how it is often interpreted. Now, as research is evolving with cloud-based online tools that automatically report usage data, it is important to review the standards and to clarify both ESSA’s useful advances and how the four tiers fail to address some critical scientific concepts. These concepts are needed for states, districts, and schools to make the best use of research findings.

We will cover the following in subsequent postings on this page.

1. Evidence According to ESSA: Since the founding of the Institute of Education Sciences and NCLB in 2002, the philosophy of evaluation has been focused on the perfection of one good study. We’ll discuss the cost and technical issues this kind of study raises and how it sometimes reinforces educational inequity.
2. Who is Served by Measuring Average Impact: The perfect design focused on the average impact of a program across all populations of students, teachers, and schools has value. Yet, school decision makers need to also know about the performance differences between specific groups such as students who are poor or middle class, teachers with one kind of preparation or another, or schools with AP courses vs. those without. Mark Schneider, IES’s director defines the IES mission as “IES is in the business of identifying what works for whom under what conditions.” This framing is a move toward a broader focus with more relevant results.
3. Differential Impact is Unbiased. According to the ESSA standards, studies must statistically control for selection bias and other sources of bias. But biases that impact the average for the population in the study don’t impact the size of the differential effect between subgroups. The interaction between the program and the population characteristic is unaffected. And that’s what the educators need to know about.
4. Putting many small studies together. Instead of the One Good Study approach we see the need for multiple studies each collecting data on differential impacts for subgroups. As Andrew Coulson of Mind Research Institute put it, we have to move from the One Good Study approach and on to valuing multiple studies with enough variety to be able to account for commonalities among districts. We add that meta-analysis of interaction effects are entirely feasible.

Our team works closely with the ESSA definitions and has addressed many of these issues. Our design for an RCE for the Dynamic Learning Project shows how Tiers 2 and 3 can be combined to answer questions involving intermediate results (or mediators). If you are interested in more information on the ESSA levels of evidence, the video on this page is a recorded webinar that provides clarification.

Classrooms and Districts: Breaking Down Silos in Education Research and Evidence

I just got back from Edsurge’s Fusion conference. The theme, aimed at classroom and school leaders, was personalizing classroom instruction. This is guided by learning science, which includes brain development and the impact of trauma, as well as empathetic caregiving, as Pamela Cantor beautifully explained in her keynote. It also leads to detailed characterizations of learner variability being explored at Digital Promise by Vic Vuchic’s team, which is providing teachers with mappings between classroom goals and tools and strategies that can address learners who vary in background, cognitive skills, and socio-emotional character.

One of the conference tracks that particularly interested me was the workshops and discussions under “Research & Evidence”. Here is where I experienced a disconnect between Empirical ’s research policy-oriented work interpreting ESSA and Fusion’s focus on improving the classroom.

• The Fusion conference is focused at the classroom level, where teachers along with their coaches and school leaders are making decisions about personalizing the instruction to students. They advocate basing decisions on research and evidence from the learning sciences.
  
• Our work, also using research and evidence, has been focused on the school district level where decisions are about procurement and implementation of educational materials including the technical infrastructure needed, for example, for edtech products.
  

While the classroom and district levels have different needs and resources and look to different areas of scientific expertise, they need not form conceptual silos. But the differences need to be understood.

Consider the different ways we look at piloting a new product.

• The Digital Promise edtech pilot framework attempts to move schools toward a more planful approach by getting them to identify and quantify the problem for which the product being piloted could be a solution. The success in the pilot classrooms is evaluated by the teachers, where detailed understandings by the teacher don’t call for statistical comparisons. Their framework points to tools such as the RCE Coach that can help with the statistics to support local decisions.
• Our work looks at pilots differently. Pilots are excellent for understanding implementability and classroom acceptance (and working with developers to improve the product), but even with rapid cycle tools, the quantitative outcomes are usually not available in time for local decisions. We are more interested in how data can be accumulated nationally from thousands of pilots so that teachers and administrators can get information on which products are likely to work in their classrooms given their local demographics and resources. This is where review sites like Edsurge product reviews or Noodle’s ProcureK12) could be enhanced with evidence about for whom, and under what conditions, the products work best. With over 5,000 edtech products, an initial filter to help choose what a school should pilot will be necessary.

A framework that puts these two approaches together is promulgated in the Every Student Succeeds Act (ESSA). ESSA defines four levels of evidence, based on the strength of the causal inference about whether the product works. More than just a system for rating the scientific rigor of a study, it is a guide to developing a research program with a basis in learning science. The base level says that the program must have a rationale. This brings us back to the Digital Promise edtech pilot framework needing teachers to define their problem. The ESSA level 1 rationale is what the pilot framework calls for. Schools must start thinking through what the problem is that needs to be solved and why a particular product is likely to be a solution. This base level sets up the communication between educators and developers about not just whether the product works in the classroom, but how to improve it.

The next level in ESSA, called “correlational,” is considered weak evidence, because it shows only that the product has “promise” and is worth studying with a stronger method. However, this level is far more useful as a way for developers to gather information about which parts of the program are driving student results, and which patterns of usage may be detrimental. Schools can see if there is an amount of usage that maximizes the value of the product (rather than depending solely on the developer’s rationale). This level 2 calls for piloting the program and examining quantitative results. To get correlational results, the pilot must have enough students and may require going beyond a single school. This is a reason that we usually look for a district’s involvement in a pilot.

The top two levels in the ESSA scheme involve comparisons of students and teachers who use the product to those who do not. These are the levels where it begins to make sense to combine a number of studies of the same product from different districts in a statistical process called meta-analysis so we can start to make generalizations. At these levels, it is very important to look beyond just the comparison of the program group and the control group and gather information on the characteristics of schools, teachers, and students who benefit most (and least) from the product. This is the evidence of most value to product review sites.

When it comes to characterizing schools, teachers, and students, the “classroom” and the “district” approach have different, but equally important, needs.

• The learner variability project has very fine-grained categories that teachers are able to establish for the students in their class.
  
• For generalizable evidence, we need characteristics that are routinely collected by the schools. To make data analysis for efficacy studies a common occurrence, we have to avoid expensive surveys and testing of students that are used only for the research. Furthermore, the research community must reach consensus on a limited number of variables that will be used in research. Fortunately, another aspect of ESSA is the broadening of routine data collection for accountability purposes, so that information on improvements in socio-emotional learning or school climate will be usable in studies.

Edsurge and Digital Promise are part of a west coast contingent of researchers, funders, policymakers, and edtech developers that has been discussing these issues. We look forward to continuing this conversation within the framework provided by ESSA. When we look at the ESSA levels as not just vertical but building out from concrete classroom experience to more abstract and general results from thousands of school districts, then learning science and efficacy research are combined. This strengthens our ability to serve all students, teachers, and school leaders.

Jefferson Education Accelerator Contracts with Empirical for Evidence as a Service™

Jefferson Education Accelerator (JEA) has contracted with Empirical Education Inc. for research services that will provide evidence of the impact of education technology products developed by their portfolio companies. JEA’s mission is to support and evaluate promising edtech solutions in order to help educators make more informed decisions about the products they invest in. The study is designed to meet level 2 or “moderate” evidence as defined by the Every Student Succeeds Act. Empirical will provide a Student Impact Report under its Evidence as a Service offering, which combines student-level product usage data and a school district’s administrative data to conduct a comparison group study. Denis Newman, Empirical’s CEO stated, “This is a perfect application of our Evidence as a Service product, which provides fast answers to questions about which kids will benefit the most from any particular learning program.” Todd Bloom, JEA’s Chief Academic Officer and Research Associate Professor at UVA’s Curry School of Education, commented: “Empirical Education is a highly respected research firm and offers the type of aggressive timeline that is sorely needed in the fast-paced edtech market.” A report on impact in the school year 2017-2018 is expected to be completed in July.

IES Published Our REL Southwest Study on Trends in Teacher Mobility

The U.S. Department of Education’s Institute of Education Sciences published a report of a study we conducted for REL Southwest! We are thankful for the support and engagement we received from the Educator Effectiveness Research Alliance throughout the study.

The study was published in December 2017 and provides updated information regarding teacher mobility for Texas public schools during the 2011-12 through 2015-16 school years. Teacher mobility is defined as teachers changing schools or leaving the public school system.

In the report, descriptive information on mobility rates is presented at the regional and state levels for each school year. Mobility rates are disaggregated further into destination proportions to describe the proportion of teacher mobility due to within-district movement, between-district movement, and leaving Texas public schools. This study leverages data collected by the Texas Education Agency during the pilot of the Texas Teacher Evaluation and Support System (T-TESS) in 57 school districts in 2014-15. Analyses examine how components of the T-TESS observation rubric are related to school-level teacher mobility rates.

During the 2011-12 school year, 18.7% of Texas teachers moved schools within a district, moved between districts, or left the Texas Public School system. By 2015-16, this mobility rate had increased to 22%. Moving between districts was the primary driver of the increase in mobility rates. Results indicate significant links between mobility and teacher, student, and school demographic characteristics. Teachers with special education certifications left Texas public schools at nearly twice the rate of teachers with other teaching certifications. School-level mobility rates showed significant positive correlations with the proportion of special education, economically disadvantaged, low-performing, and minority students. School-level mobility rates were negatively correlated with the proportion of English learner students. Schools with higher overall observation ratings on the T-TESS rubric tended to have lower mobility rates.

Findings from this study will provide state and district policymakers in Texas with updated information about trends and correlates of mobility in the teaching workforce, and offer a systematic baseline for monitoring and planning for future changes. Informed by these findings, policymakers can formulate a more strategic and targeted approach for recruiting and retaining teachers. For instance, instead of using generic approaches to enhance the overall supply of teachers or improve recruitment, more targeted efforts to attract and retain teachers in specific subject areas (for example, special education), in certain stages of their career (for example, novice teachers), and in certain geographic areas are likely to be more productive. Moreover, this analysis may enrich the existing knowledge base about schools’ teacher retention and mobility in relation to the quality of their teaching force, or may inform policy discussions about the importance of a stable teaching force for teaching effectiveness.

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.

IES Publishes our Recent REL Southwest Teacher Studies

The U.S. Department of Education’s Institute of Education Sciences published two reports of studies we conducted for REL Southwest! We are thankful for the support and engagement we received from the Educator Effectiveness Research Alliance and the Oklahoma Rural Schools Research Alliance throughout the studies. The collaboration with the research alliances and educators aligns well with what we set out to do in our core mission: to support K-12 systems and empower educators in making evidence-based decisions.

The first study was published earlier this month and identified factors associated with successful recruitment and retention of teachers in Oklahoma rural school districts, in order to highlight potential strategies to address Oklahoma’s teaching shortage. This correlational study covered a 10-year period (the 2005-06 to 2014-15 school years) and used data from the Oklahoma State Department of Education, the Oklahoma Office of Educational Quality and Accountability, federal non-education sources, and publicly available geographic information systems from Google Maps. The study found that teachers who are male, those who have higher postsecondary degrees, and those who have more teaching experience are harder than others to recruit and retain in Oklahoma schools. In addition, for teachers in rural districts, higher total compensation and increased responsibilities in job assignment are positively associated with successful recruitment and retention. In order to provide context, the study also examined patterns of teacher job mobility between rural and non-rural school districts. The rate of teachers in Oklahoma rural schools reaching tenure is slightly lower than the rates for teachers in non-rural areas. Also, rural school districts in Oklahoma had consistently lower rates of success in recruiting teachers than non-rural school districts from 2006-07 to 2011-12.

This most recent study, published last week, examined data from the 2014-15 pilot implementation of the Texas Teacher Evaluation and Support System (T-TESS). In 2014-15 the Texas Education Agency piloted the T-TESS in 57 school districts. During the pilot year teacher overall ratings were based solely on rubric ratings on 16 dimensions across four domains.

The study examined the statistical properties of the T-TESS rubric to explore the extent to which it differentiates teachers on teaching quality and to investigate its internal consistency and efficiency. It also explored whether certain types of schools have teachers with higher or lower ratings. Using data from the pilot for more than 8,000 teachers, the study found that the rubric differentiates teacher effectiveness at the overall, domain, and dimension levels; domain and dimension ratings on the observation rubric are internally consistent; and the observation rubric is efficient, with each dimension making a unique contribution to a teacher’s overall rating. In addition, findings indicated that T-TESS rubric ratings varied slightly in relation to some school characteristics that were examined, such as socioeconomic status and percentage of English Language Learners. However, there is little indication that these characteristics introduced bias in the evaluators’ ratings.

Sure, the edtech product is proven to work, but will it work in my district?

It’s a scenario not uncommon in your district administrators’ office. They’ve received sales pitches and demos of a slew of new education technology (edtech) products, each one accompanied with “evidence” of its general benefits for teachers and students. But underlying the administrator’s decision is a question often left unanswered: Will this work in our district?

In the conventional approach to research advocated, for example, by the U.S. Department of Education and the Every Student Succeeds Act (ESSA), the finding that is reported and used in the review of products is the overall average impact for any and all subgroups of students, teachers, or schools in the study sample. In our own research, we have repeatedly seen that who it works for and under what conditions can be more important than the average impact. There are products that are effective on average but don’t work for an important subgroup of students, or vice versa, work for some students but not all. Some examples:

• A math product, while found to be effective overall, was effective for white students but ineffective for minority students. This effect would be relevant to any district wanting to close (rather than further widen) an achievement gap.
• A product that did well on average performed very well in elementary grades but poorly in middle school. This has obvious relevance for a district, as well as for the provider who may modify its marketing target.
• A teacher PD product greatly benefitted uncertified teachers but didn’t help the veteran teachers do any better than their peers using the conventional textbook. This product may be useful for new teachers but a poor choice for others.

As a research organization, we have been looking at ways to efficiently answer these kinds of questions for products. Especially now, with the evidence requirements built into ESSA, school leaders can ask the edtech salesperson: “Does your product have evidence that ESSA calls for?” They may well hear an affirmative answer supported by an executive summary of a recent study. But, there’s a fundamental problem with what ESSA is asking for. ESSA doesn’t ask for evidence that the product is likely to work in your specific district. This is not the fault of ESSA’s drafters. The problem is built into the conventional design of research on “what works”. The U.S. Department of Education’s What Works Clearinghouse (WWC) bases its evidence rating only on an average; if there are different results for different subgroups of students, that difference is not part of the rating. Since ESSA adopts the WWC approach, that’s the law of the land. Hence, your district’s most pressing question is left unanswered: will this work for a district like mine?

Recently, the Software & Information Industry Association, the primary trade association of the software industry, released a set of guidelines for research explaining to its member companies the importance of working with districts to conduct research that will meet the ESSA standards. As the lead author of this report, I can say it was our goal to foster an improved dialog between the schools and the providers about the evidence that should be available to support buying these products. As an addendum to the guidelines aimed at arming educators with ways to look at the evidence and questions to ask the edtech salesperson, here are three suggestions:

1. It is better to have some information than no information. The fact that there’s research that found the product worked somewhere gives you a working hypothesis that it could be a better than average bet to try out in your district. In this respect, you can consider the WWC and newer sites such as Evidence for ESSA rating of the study as a screening tool—they will point you to valid studies about the product you’re interested in. But you should treat previous research as a working hypothesis rather than proof.
2. Look at where the research evidence was collected. You’ll want to know whether the research sites and populations in the study resemble your local conditions. WWC has gone to considerable effort to code the research by the population in the study and provides a search tool so you can find studies conducted in districts like yours. And if you download and read the original report, it may tell you whether it will help reduce or increase an achievement gap of concern.
3. Make a deal with the salesperson. In exchange for your help in organizing a pilot and allowing them to analyze your data, you get the product for a year at a steep discount and a good ongoing price if you decide to implement the product on a full scale. While you’re unlikely to get results from a pilot (e.g., based on spring testing) in time to support a decision, you can at least lower your cost for the materials, and you’ll help provide a neighboring district (with similar populations and conditions) with useful evidence to support a strong working hypothesis as to whether it is likely to work for them as well.
   

Report of the Evaluation of iRAISE Released

Empirical Education Inc. has completed its evaluation (read the report here) of an online professional development program for Reading Apprenticeship. WestEd’s Strategic Literacy Initiative (SLI) was awarded a development grant under the Investing in Innovation (i3) program in 2012. iRAISE (internet-based Reading Apprenticeship Improving Science Education) is an online professional development program for high school science teachers. iRAISE trained more than 100 teachers in Michigan and Pennsylvania over the three years of the grant. Empirical’s randomized control trial measured the impact of the program on students with special attention to differences in their incoming reading achievement levels.

The goal of iRAISE was to improve student achievement by training teachers in the use of Reading Apprenticeship, an instructional framework that describes the classroom in four interacting dimensions of learning: social, personal, cognitive, and knowledge-building. The inquiry-based professional development (PD) model included a week-long Foundations training in the summer; monthly synchronous group sessions and smaller personal learning communities; and asynchronous discussion groups designed to change teachers’ understanding of their role in adolescent literacy development and to build capacity for literacy instruction in the academic disciplines. iRAISE adapted an earlier face-to-face version of Reading Apprenticeship professional development, which was studied under an earlier i3 grant, Reading Apprenticeship Improving Secondary Education (RAISE), into a completely online course, creating a flexible, accessible platform.

To evaluate iRAISE, Empirical Education conducted an experiment in which 82 teachers across 27 schools were randomly assigned to either receive the iRAISE Professional Development during the 2014-15 school year or continue with business as usual and receive the program one year later. Data collection included monthly teacher surveys that measured their use of several classroom instructional practices and a spring administration of an online literacy assessment, developed by Educational Testing Service, to measure student achievement in literacy. We found significant positive impacts of iRAISE on several of the classroom practice outcomes, including teachers providing explicit instruction on comprehension strategies, their use of metacognitive inquiry strategies, and their levels of confidence in literacy instruction. These results were consistent with the prior RAISE research study and are an important replication of the previous findings, as they substantiate the success of SLI’s development of a more accessible online version of their teacher PD. After a one-year implementation with iRAISE, we do not find an overall effect of the program on student literacy achievement. However, we did find that levels of incoming reading achievement moderate the impact of iRAISE on general reading literacy such that lower scoring students benefit more. The success of iRAISE in adapting immersive, high-quality professional development to an online platform is promising for the field.

You can access the report and research summary from the study using the links below.
iRAISE research report
iRAISE research summary

Five-year evaluation of Reading Apprenticeship i3 implementation reported at SREE

Empirical Education has released two research reports on the scale-up and impact of Reading Apprenticeship, as implemented under one of the first cohorts of Investing in Innovation (i3) grants. The Reading Apprenticeship Improving Secondary Education (RAISE) project reached approximately 2,800 teachers in five states with a program providing teacher professional development in content literacy in three disciplines: science, history, and English language arts. RAISE supported Empirical Education and our partner, IMPAQ International, in evaluating the innovation through both a randomized control trial encompassing 42 schools and a systematic study of the scale-up of 239 schools. The RCT found significant impact on student achievement in science classes consistent with prior studies. Mean impact across subjects, while positive, did not reach the .05 level of significance. The scale-up study found evidence that the strategy of building cross-disciplinary teacher teams within the school is associated with growth and sustainability of the program. Both sides of the evaluation were presented at the annual conference of the Society for Research on Educational Effectiveness, March 6-8, 2016 in Washington DC. Cheri Fancsali (formerly of IMPAQ, now at Research Alliance for NYC Schools) presented results of the RCT. Denis Newman (Empirical) presented a comparison of RAISE as instantiated in the RCT and scale-up contexts.

You can access the reports and research summaries from the studies using the links below.
RAISE RCT research report
RAISE RCT research summary
RAISE Scale-up research report
RAISE Scale-up research summary