Seeing Clearly

Five Lenses to Bring English Learner Data into Focus
Policy Paper
Aug. 16, 2017

Nearly one-third of children in the United States live in a household where a language other than English is spoken. At school, one in ten receives services as an English learner (EL), the fastest-growing student group in K–12 public education. Across diverse geographic and political contexts, schools play a critical role in integrating these students into American society, equipping them with English mastery for strong college and career outcomes. 

Success with this population matters. So, how can state and local leaders tell if a school or district is doing a good job with ELs? This seemingly simple question yields fuzzy answers. Data policies on EL outcomes are often complexly designed and generate information that is frequently misinterpreted. As a result, many states’ and districts’ vision of what constitutes excellence for ELs is blurry at best. When exemplars are hard to see, it is hard to learn from and replicate their successes. 

Worse yet, data about ELs are often downright misleading in ways that can present these students as an unsolvable problem or a liability. For example, it is common to refer to a large, stagnant “achievement gap” between ELs and native English speakers. This rhetoric is useful to attract public attention to the needs of a historically marginalized population. However, the framing quickly becomes a catchall phrase that misrepresents the issue. If stakeholders are serious about closing an EL “gap,” they will need to diagnose the root causes of it in the data, understand its contours, and have a means to detect whether they are making progress. 

Understanding and drawing inferences from EL data is complicated for a variety of reasons. This report, Seeing Clearly: Five Lenses to Bring English Learner Data into Focus, offers a framework of five corrective lenses that are critical for viewing this population accurately: 

  1. The EL subgroup is not static. 
  2. Learning a language takes time—but not forever. 
  3. ELs at different stages progress at different rates. 
  4. English skills impact academic performance. 
  5. Poverty affects most ELs and, as a result, their educational outcomes. 

A one-page overview of the framework accompanies the longer report.

These EL data quandaries are not new. For years, academics have noted issues with EL data collection, reporting, and accountability policies. However, the research has struggled to gain comprehensibility with practitioners, advocates, community stakeholders, and the media, to say nothing of policymakers. There is a clear “translation” problem of communicating what researchers have identified in academic circles into plain, actionable language on the ground. 

Moreover, there is a disconnect between EL specialists and general educator professional communities. While EL educators are often well aware of data challenges, their insights rarely reach leaders at all levels. In the words of Lesli Maxwell, editor of Education Week, the data issues are a “hot topic in the world of EL[s]” but struggle to gain traction more broadly. 

Now, in light of new flexibilities for setting EL outcomes, goals, and accountability metrics under the federal Every Student Succeeds Act (ESSA), it is critical to explain these issues and build data literacy among a wider audience. In the ESSA era, ensuring equity and transparency for ELs will require expanding the coalition of stakeholders who understand EL-related data beyond just technical experts. This paper shines a light on data challenges and sets a vision for how to improve the collection, use, and interpretation of EL data. Ultimately, EL data conversations must shift from the margins to the core of equity-minded education reform.

A companion case study to this report,
Pioneering Change: Leveraging Data to Reform English Learner Education in Oregon, illustrates what it looks like to put principles for EL data into action through policy changes. State leaders in Oregon passed legislation and revised policies to design more thoughtful EL data indicators, codify more transparent reporting requirements, and link data insights to additional support for ELs.