Across a variety of domains, states need better data to more equitably serve dual language learners (DLLs) in early care and education (ECE). When leaders cannot access high-quality, complete information about these children, they will struggle to make policy decisions and investments in ECE in strategic, effective ways.

To foster better insights in supporting policy-making for young DLLs, most states need to improve their policies for data collection in three key areas:

1. DLL Enrollment. 

Within state-funded pre-K programs, many states do not have a mechanism to identify and track the participation of DLLs, or the number of children speaking a language other than English at home. At the point of enrollment, states would also benefit from gauging the abilities of potential DLLs across the languages they use to better understand these children’s needs and assets.

States should:

• Adopt a uniform protocol, such as conducting a family interview and language screening, to identify DLLs and collect this data across state ECE programs.
• When identifying DLLs, screen for language abilities in both English and a child’s home language to collect more complete data.

2. ECE program quality for DLLs. 

In recent years, many states have implemented quality rating and improvement systems (QRIS) that help shine a light on the quality of a state’s ECE services for all children. However, most states are failing to include any criteria that specifically evaluate how providers are responsive to DLLs’ unique needs. Moreover, there are concerns related to barriers to participation in QRIS for immigrant and multilingual providers serving DLLs. The accessibility and clarity of public QRIS data for DLL families is also lacking.

States should:

• Adopt and prioritize DLL-related indicators in QRIS.
• Provide technical assistance and outreach to linguistically diverse providers to encourage their participation in QRIS.
• Translate state websites that publish QRIS ratings to increase accessibility for DLL parents.
• Publicly report a DLL subscore that bundles all DLL-related indicators into one rating.

3. DLLs’ kindergarten readiness.

The majority of states are now using or developing tools to assess children’s school readiness when they enter kindergarten. These kindergarten readiness assessments (KRAs) measure a child’s knowledge and abilities across multiple domains, including math, literacy, social skills, and physical development. However, most states currently test only in English, which creates major validity concerns for DLLs whose development is spread across two or more languages. More generally, leaders also need to clarify appropriate testing accommodations for DLLs on current tests and expand trainings to assist educators with the implementation of KRAs with DLLs.

States should:

• Assess DLLs bilingually on kindergarten readiness assessments (KRAs).
  – Invest in the development of valid bilingual assessment tools in home languages.
  – Invest in expanding access to bilingual assessors.
• Improve and increase professional development and guidance for teachers on administering KRAs with DLLs.
• If publicly reporting data by DLL status for KRAs, provide guidance and explain limitations of these data to users.

Through policy changes in these three areas, states can develop more equitable, inclusive data systems for DLLs in the early years. Better, more complete DLL data equips states leaders with more meaningful insights to drive public investments and supports. With one out of every four preschool-aged children considered a DLL, it is important—now more than ever—to design policies that work for this growing population of learners.

[Cross-posted at Ed Central]

The post Dual Language Learner Data Gaps: Takeaways for State Policy Leaders appeared first on Washington Monthly.

Around 5 million children in U.S. schools are learning English as a new language, about one in ten students. English learners represent the fastest-growing student group nationwide. More than ever, education leaders must design policies and instructional practices to best support these students.

Kentucky is at the forefront of this challenge. From 2000 to 2010, the demographics in the Bluegrass State shifted dramatically: the number of English learners grew a whopping 306 percent.

And yet, as a recent Hechinger Report article recently highlighted, the state’s data systems make it extremely difficult to distill a clear picture of what is working with English learners. You can’t diagnose or treat a problem that you can’t see.

Related: Closing gap for immigrant students under Common Core in Kentucky is a moving target

It’s not just Kentucky. This is par for the course across the U.S. Almost every state needs to be clearer and more transparent about the limitations of current data for these students.

Current data-tracking for English learners has two big problems. First, these students — by definition — have not yet mastered English. Their performance on English math, English reading, or English science tests is not always valid. If these students score poorly on math and reading tests, this doesn’t mean they — or their teachers — are fundamentally flawed or failing. They are learning (or teaching) a new language, a process that takes around 4 to 7 years based on a variety of factors. It’s unfair to be schizophrenic about this bottom-line reality. Based on federal definitions of English learners, these students are supposed to be those who can’t demonstrate content knowledge on exams because of language. We shouldn’t be surprised — or punish educators — when the students we’ve identified as such perform that way.

Secondly, English learners are a constantly overturning group of students. Once an English learner becomes proficient at English, her achievement data moves out of the English learner tributary into the “mainstream” designation of all K–12 students. Experts call this the “revolving door” phenomenon, “the gap that can’t go away.” As soon as students progress and reach English proficiency, they lose the English learner label. So, her hard work and achievement never boosts the “English learner” category overall. She usually leaves the group just as her achievement levels begin to resemble native-English speakers.

This problem is fairly intuitive. Yet all too often, policy leaders and analysts will broadcast misleading statements on English learners. Half-truths. For example, they will acknowledge that it takes years to learn academic English and, in the next breath, bluster over the “achievement gaps” of English learners with graduation rates and national reading and math assessments.

Related: In a conservative corner of Arkansas, schools welcome immigrants

There’s a helpful old Yiddish proverb here: half-truths are whole lies. So: the bottom line on interpreting current data on English learners? Proceed with caution.

To be clear, there certainly are real achievement gaps emerging for some English learners, and we are far from currently educating English learners in utopian, ideal, or even particularly effective ways. But, to pinpoint gaps honestly and target interventions, we have to respect what state data can accurately tell us — not what we wish it could.

Related: Schools are under federal pressure to translate for immigrant parents

Fortunately, some states are innovating with new data policies.

One important reform is the creation of an “ever English learner” category to track outcomes for these students over the course of their entire K–12 education. This is exactly what Oregon did in 2013. The initiative arose after state leaders recognized current data tracking made it impossible to comprehensively evaluate how they were doing with English learners. They partnered with researchers from Oregon State University to design and implement new data rules that would capture — and allow for comparisons between — the achievement for current, former, “never” and “ever” English learners. From this change, they found that graduation rates of twelfth-graders who were former English learners were virtually the same as native English speakers.

Washington State and New York state are two of only other states with similar “ever English learner” policies.

Another meaningful data point to track is “long-term” English learners. California has led on this issue, passing a groundbreaking law in 2012 to require a common definition and reporting of long-term English learners.

Related: How one Mississippi community copes with influx of Hispanic students

In 2014, advocates then used these data to determine that nearly three-quarters of the state’s English learners in grades 6-12 had languished in schools for more than seven years without achieving English proficiency. This is a data point that should elicit alarm. That is, our education system is most likely failing students if — after a fair, appropriate window of time for language learning — there are still large numbers of students not mastering English.

Now, the new federal education law, the Every Student Succeeds Act, also includes a similarly-promising requirement for tracking long-term English learners under Title III. States will have to report the number of English learners who have gone to school for five years or more without attaining English. But this requirement is not a part of Title I’s report card system, and so it is unclear if or how this data will be made usefully accessible to the public.

These ever- and long-term English learner policies represent important steps to get data-tracking for English learners right. As eager as we are to find new, promising ways to support these students, innovation and measurement go hand in hand. Picture a chemist in a lab with colorful, bubbling liquids in glass tubes. As she experiments, she carefully records results, reflects, tweaks, tries something new, and — through this process — distills key insights to push her field forward.

Education policymaking should work similarly. As they experiment with new strategies and interventions, leaders try to create such feedback loops and get temperature checks on what (and how) our schools are doing through important — if imperfect — data points. From an economic perspective: what return on we getting on our investment of public dollars? As a moral matter: are we doing right by these kids?

English learners — perhaps more than any other group of students, given the intersection of their unique linguistic and academic trajectories — present a unique data-gathering challenge. Policymakers need to be more forthright about this.

They have wide room to rethink data policies that would provide better information on how schools are supporting these kids.

Because if the data we have does not tell an accurate story about what we are trying to measure, that method is purely madness.

[Cross-posted at The Hechinger Report]

The post The Delusional Ways We Evaluate English Learners appeared first on Washington Monthly.