blog posts and news stories

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.


Empirical Describes Innovative Approach to Research Design for Experiment on the Value of Instructional Technology Coaching

Empirical Education (Empirical) is collaborating with Digital Promise to evaluate the impact of the Dynamic Learning Project (DLP) on student achievement. The DLP provides school-based instructional technology coaches to participating districts to increase educational equity and impactful use of technology. Empirical is working with data from prior school years, allowing us to continue this work during this extraordinary time of school closures. We are conducting quasi-experiments in three school districts across the U.S. designed to provide evidence that will be useful to DLP stakeholders, including schools and districts considering using the DLP coaching model. Today, Empirical has released its design memo outlining its innovative approach to combining teacher-level and student-level outcomes through experimental and correlational methods.

Digital Promise— through funding and partnership with Google—launched the DLP in 2017 with more than 1,000 teachers in 50 schools across 18 districts in five states. The DLP expanded in the second year of implementation (2018-2019) with more than 100 schools reached across 23 districts in seven states. Digital Promise’s surveys of participating teachers have documented teachers’ belief in the DLP’s ability to improve instruction and increase impactful technology use (see Digital Promise’s extensive postings on the DLP). Our rapid cycle evaluations will work with data from the same cohorts, while adding district administrative data and data on technology use.

Empirical’s studies will establish valuable links between instructional coaching, technology use, and student achievement, all while helping to improve future iterations of the DLP coaching model. As described in our design memo, the study is guided by Digital Promise’s logic model. In this model, coaching is expected to affect an intermediate outcome, measured in Empirical’s research in terms of patterns of usage of edtech applications, as they implicate instructional practices. These patterns and practices are then expected to impact measurable student outcomes. The Empirical team will evaluate the impact of coaching on both the mediator (patterns and practices) and the student test outcomes. We will examine student-level outcomes by subgroup. The data are currently in the collection process, and we expect report publication by summer 2020.


Updated Research on the Impact of Alabama’s Math, Science, and Technology Initiative (AMSTI) on Student Achievement

We are excited to release the findings of a new round of work conducted to continue our investigation of AMSTI. Alabama’s specialized training program for math and science teachers began over 20 years ago and now reaches over 900 schools across the state. As the program is constantly evolving to meet the demands of new standards and new assessment systems, the AMSTI team and the Alabama State Department of Education continue to support research to evaluate the program’s impact. Our new report builds on the work undertaken last year to answer three new research questions.

  1. What is the impact of AMSTI on reading achievement? We found a positive impact of AMSTI for students on the ACT Aspire reading assessment equivalent to 2 percentile points. This replicates a finding from our earlier 2012 study. This analysis used students of AMSTI-trained science teachers, as the training purposely integrates reading and writing practices into the science modules.
  2. What is the impact of AMSTI on early-career teachers? We found positive impacts of AMSTI for partially-trained math teachers and fully-trained science teachers. The sample of teachers for this analysis was those in their first three years of teaching, with varying levels of AMSTI training.
  3. How can AMSTI continue program development to better serve ELL students? Our earlier work found a negative impact of AMSTI training for ELL students in science. Building upon these results, we were able to identify a small subset of “model ELL AMSTI schools” where there was both a positive impact of AMSTI on ELL students, and where that impact was larger than any school-level effect on ELL students versus the entire sample. By looking at the site-specific best practices of these schools for supporting ELL students in science and across the board, the AMSTI team can start to incorporate these strategies into the program at large.

All research Empirical Education has conducted on AMSTI can be found on our AMSTI webpage.