blog posts and news stories

Going Beyond the NCLB-Era to Reduce Achievement Gaps

We just published on Medium an important article that traces the recent history of education research to show how an unfortunate legacy of NCLB has weakened research methods, as applied to school use of edtech, and made invisible resulting achievement gaps. This article was originally a set of four blog posts by CEO Denis Newman and Chief Scientist Andrew Jaciw. The article shows how the legacy belief that differential subgroup effects (e.g., based on poverty, prior achievement, minority status, English proficiency) found in experiments are, at best, a secondary exploration that has left serious achievement gaps unexamined. And the false belief that only studies based on data collected before program implementation are free of misleading biases has given research the warranted reputation as very slow and costly. Instead, we present a rationale for low-cost and fast-turnaround studies using cloud-based edtech usage data combined with already collected school district administrative data. Working in districts that have already implemented the program lowers the cost to the point that a dozen small studies each examining subgroup effects, which Jaciw has shown to be relatively unbiased, can be combined to produce generalizable results. These results are what school decision-makers need in order to purchase edtech that works for all their students.

Read the article on medium here.

Or read the 4-part blog series we posted this past summer.

1. Ending a Two-Decade Research Legacy

2. ESSA Evidence Tiers and Potential for Bias

3. Validating Research that Helps Reduce Achievement Gaps

4. Putting Many Small Studies Together

SREE Spring 2017 Conference Recap

Several Empirical Education team members attended the annual SREE conference in Washington, DC from March 4th - 5th. This year’s conference theme, “Expanding the Toolkit: Maximizing Relevance, Effectiveness and Rigor in Education Research,” included a variety of sessions focused on partnerships between researchers and practitioners, classroom instruction, education policy, social and emotional learning, education and life cycle transitions, and research methods. Andrew Jaciw, Chief Scientist at Empirical Education, chaired a session about Advances in Quasi-Experimental Design. Jaciw also presented a poster on developing a “systems check” for efficacy studies under development. For more information on this diagnostic approach to evaluation, watch this Facebook Live video of Andrew’s discussion of the topic.

Other highlights of the conference included Sean Reardon’s keynote address highlighting uses of “big data” in creating context and generating hypotheses in education research. Based on data from the Stanford Education Data Archive (SEDA), Sean shared several striking patterns of variation in achievement and achievement gaps among districts across the country, as well as correlations between achievement gaps and socioeconomic status. Sean challenged the audience to consider how to expand this work and use this kind of “big data” to address critical questions about inequality in academic performance and education attainment. The day prior to the lecture, our CEO, Denis Newman, attended a workshop lead by Sean and colleagues (Workshop C) that provided a detailed overview of the SEDA data and how it can be used in education research. The psychometric work to generate equivalent scores for every district in the country, the basis for his findings, was impressive and we look forward to their solving the daunting problem of extending the database to encompass individual schools.