In recent years, public leaders have increasingly sought to invest in our nation’s youngest learners. Funding for early care and education (ECE) for children ages 0-5 has grown substantially.¹ From 2006-2016, state pre-K spending more than doubled nationwide from 3.3 to 7.4 billion, and pre-K enrollment is at an all-time high.² Federal dollars for Head Start and child care subsidies have also increased in the last decade.³

At the same time, another figure is rising: the number of young children learning English while developing another language at home. In the years before kindergarten, these students are typically referred to as dual language learners (DLLs).⁴ Nationwide, the National Institute for Early Education Research (NIEER) estimates that 23 percent of young children—nearly one in four—are DLLs, regardless of whether they are formally identified as such in early learning programs to receive extra supports.⁵

Research suggests that high-quality ECE opportunities are particularly impactful for these students.⁶ ECE offers systematic exposure to English at a key developmental window, even as DLLs benefit from continued development of their home language.⁷ Studies suggest that initial English proficiency at the point of kindergarten entry allows DLLs to keep pace with non-DLL peers throughout elementary school and that initial native language ability is also linked to English development.⁸

Because DLLs represent a growing segment of the U.S. population, and because the early years are so foundational to long-term success, it is important that education leaders have clear insights about these students: who and where they are, the services they receive, and how they are progressing.

Several advocates and researchers have drawn attention to the need for better ECE data systems for all children in general.⁹ As they argue, such information has potential to empower a variety of audiences: policymakers, families, educators, and other community stakeholders. But to be most effective, the leaders designing these data policies should pay particular attention to how they incorporate multilingual children and families, responding to their distinct needs and assets.

At present, there is wide room for improvement in this area. For example, NIEER recently found that only 24 states and Guam track the enrollment of DLL children in state-funded pre-K programs.¹⁰ Such glaring information gaps are problematic: without increasing the availability and quality of ECE data on DLLs, state policymakers cannot serve these children in intentional, equitable ways. The U.S. Department of Health and Human Services and U.S. Department of Education highlighted this reality in a 2016 policy statement. Officials stressed that better DLL data is vital to state leaders’ decision-making in allocating finite resources, developing educators’ competencies, and reaching out to and partnering with families in culturally and linguistically responsive ways.¹¹

How, then, can state leaders build data systems that more fully account for the growing population of DLLs and their distinct needs? This report highlights current practices and proposes how states can better collect and use ECE data in three areas: 1) tracking DLL enrollment, 2) evaluating program quality, and 3) assessing kindergarten readiness. Ultimately, better data across these domains has potential to empower leaders and families, addressing key information gaps to serve DLLs more strategically.

To serve young dual language learners equitably, ECE leaders must first have data on who these children are. As DLL researcher Alexandra Figueras-Daniel recently wrote, “Without consistency on even the identification of who is a DLL and who is not, states cannot determine clear-cut policies to support these children in a systematic way… Data on enrollment [are] crucial if states are to make sound decisions about how and where to allocate resources supporting DLLs.”¹²

At a system level, even this basic level of information is a challenge for many states to pin down. In part, this difficulty reflects the nature of ECE as a sector. In contrast to K-12 public education, ECE is fragmented across a variety of funding streams and settings, including child care centers, home-based care, Head Start, and state pre-K programs. This reality, which some have compared to a “patchwork quilt,” adds extra layers of complexity for streamlining and coordinating policy efforts.¹³

Federal policy, for example, has increasingly clarified expectations for states to identify and collect information about English learners (ELs) enrolled in elementary and secondary schools. Though far from perfect,¹⁴ every state must establish one policy to determine which students qualify to receive extra language services—ones that ELs are entitled to by civil rights law governing K-12 education.¹⁵ Typically, upon registration for public school, families receive a home language survey that enables schools to identify a pool of potential ELs. The school then screens these students using a standardized language assessment. If a student scores below a state’s benchmark on the test, they are formally classified as an EL.¹⁶ Federal policy also mandates that states track data on the number of classified ELs.

ECE presents an entirely different context. The K-12 federal mandates for ELs do not extend to pre-K, which students are not legally required to attend and is not universally available as a public good. Across a splintered ECE system—with a variety of policies, standards, and regulations in a variety of settings— there are more challenges to producing an aggregated count of DLLs. In some cases, researchers have attempted to overcome this data void by looking at Census data on 3- and 4-year-olds’ participation in a wide range of child care arrangements reported by families speaking a non-English language at home. This produces a helpful yet rough estimate of the total number of DLLs enrolled across both public and private ECE settings: 41.5 percent compared to 47.9 percent of non-DLLs, by one computation.¹⁷

Even within state-funded, public pre-K programs (where there is a relatively greater degree of control in setting cohesive policy), it is still hard to get a firm count of DLL children. State pre-Ks use various methods to identify DLLs, including teacher observation, developmental screenings or assessments, family member reports and surveys, or some combination of these strategies. But in the 2015 Preschool Yearbook published by the National Institute for Early Education Research (NIEER), 10 state programs reported having no policy for DLL identification and 13 states responded that these protocols were “locally determined.”¹⁸ It is not surprising, then, that most states do not have clear numbers on DLL enrollment: as previously noted, NIEER found only about half of state pre-Ks could report these figures.¹⁹

As states seek to standardize the process for DLL identification, the use of home language surveys is a key policy lever. As noted above, federal law in K-12 requires local leaders to give these questionnaires to parents or guardians when a student enrolls in kindergarten. The survey typically asks what language(s) the child learned first, understands, and uses, and in which contexts.²⁰

The home language survey is a practice that states should consider standardizing and extending into the early years, although perhaps with modifications. In the ECE context, several DLL researchers have stressed going beyond a one-dimensional survey sent home on paper. “‘Home language survey’ is a bit of a misnomer for what is ideal,” DLL expert Linda Espinosa explained in a recent interview. Instead, a protocol for conducting an in-person family interview, with a structured set of questions on DLLs’ language experiences, dominance, and social history, has potential to gather richer insights.²¹

A uniform protocol for surveying or interviewing families would help states collect better estimates on the number of DLLs they serve. However, only around a third of state-funded pre-K programs—23 out of the 60 nationally—report having policies to collect information about language use in the home, such as through a home language survey.²²

Developmental screeners, which test a child’s skills in language and other domains, are another approach to identifying DLLs in a more systemized way. When using screeners to assess language abilities, it is crucial to test not just in English but also the student’s native language. When programs do not screen the home language, they get an incomplete picture of students’ linguistic abilities, setting in a motion a “deficit perspective” that focuses on what DLLs cannot do with language versus what they can.²³ Screening bilingually also helps educators differentiate between typical development and language delays or other learning disability issues.²⁴

At present, few state pre-K programs screen to identify DLLs in English, let alone in their home languages.²⁵ As a bright spot, Head Start’s new regulations, updated in 2016, require screening DLLs in English and the home language.²⁶ This is an important practice that state ECE leaders should push for across state-funded programs to help guide resource allocation, instructional practices, and program staffing.

In addition to tracking the enrollment of DLLs in public ECE settings, state leaders should also collect data on the quality of programs serving DLLs. Indeed, it is not enough to increase DLL access to ECE generally; DLLs need access to high-quality services in order to reap the full benefits that early learning offers.

Defining and measuring “quality” has historically proved a challenge to the ECE field as a whole. This difficulty has resulted in information gaps for policymakers, program leaders, and families seeking to select the best providers for their children, including child care centers, home-based care, Head Start, and state pre-K programs. Parents often struggle to accurately assess the quality of their children’s program, suggesting a need for what researchers at University of Virginia’s EdPolicyWorks call “informational interventions” in ECE markets.”²⁷

In recent years, states have turned to quality rating and improvement systems (QRIS) as a data-driven strategy along these lines. First implemented in Oklahoma in 1998, QRIS have spread substantially in the last two decades.²⁸ From 2012-2016, the federal government incentivized states’ adoption of QRIS through Race to the Top-Early Learning Challenge competitive grants, administered by the U.S. Departments of Education and Health and Human Services.²⁹ Though some questions remain about their overall validity,³⁰ nearly every state now has such a system in place or is developing one, with 81,000 participating programs across the U.S. (see map below).³¹

With origins in the child care context, QRIS have evolved over the years into a “state-based framework to define and support high-quality ECE” more globally, according to the BUILD Initiative. QRIS is now a more unified, cross-sector approach; in addition to child care, many states include state pre-Ks and Head Start in these systems.³² Like rating systems for hotels or restaurants, QRIS evaluates providers on a continuum of multiple indicators (also referred to as standards) across various domains, such as health and safety, learning environment, staff qualifications, family partnership, and more, and then shares those results publicly.³³ In addition to posting ratings online for families and the general public, leaders also use QRIS data to support quality improvement efforts with providers, such as through coaching, professional development opportunities, and other financial incentives.³⁴

As QRIS gain prominence and undergo refinement, leaders should consider how they incorporate data on program quality for DLLs in these systems. Julie Sugarman and Maki Park, senior policy analysts at the Migration Policy Institute (MPI), explore this issue in depth in a recent report.³⁵ They stress that states should consider how they are explicitly incorporating DLLs’ needs into the indicators used by QRIS to evaluate providers. For example, as suggested by federal guidance and the Center For Law and Social Policy, DLL-specific criteria in QRIS might address whether ECE programs:

1. Establish a process to identify DLLs when initially enrolled;
2. Require program materials to reflect and value DLLs’ home cultures and languages;
3. Provide written plans for best practices in working with DLLs;
4. Communicate with families in their home language;
5. Support children’s home language in addition to English development;
6. Require professional development on culturally and linguistically responsive practices; and,
7. Require bilingual staff proficient in the language of the majority of DLLs in a program.³⁶

However, not all states include DLL criteria in their QRIS ratings. In 2017, the National Center on Early Childhood Quality Assurance found that only 40 percent of QRIS currently include any DLL-related indicators.³⁷ The most frequent example of such an indicator involves providing resources in families’ home languages. And yet, the overall incidence of even this basic standard was low, present in only 5 QRIS, or 12 percent, nationally.³⁸

Example of DLL-Related Indicator in New York’s QRIS, QUALITYstarsNY³⁹

Domain: Family Involvement and Support

The structuring of QRIS ratings can also de-prioritize the significance of DLL indicators even when such indicators are present. For example, seventeen states use a “building block” approach to award higher ratings; quality indicators must be fully met at one level before a provider can apply to earn the next highest rating level.⁴⁰ Some states only offer credit for these DLL indicators at higher levels, such that DLL practices are not a foundational concern in QRIS ratings. Moreover, these DLL indicators often exist (at best) as a few scattered among many others. In some QRIS, a program can receive high ratings even if it fails to earn any DLL-related points.⁴¹

Beyond indicators, there are other challenges to building equitable QRIS for DLLs. In general, getting providers to participate in QRIS presents a major issue. Provider participation levels, which vary considerably and are relatively low in many states, often correspond to the degree that state policies mandate participation for certain providers.⁴² States strive to incentivize participation through various means, but the bureaucratic process remains costly to providers in terms of both time and money.

For immigrant and linguistically diverse ECE providers—ones that disproportionately serve DLLs—QRIS participation often comes with extra burdens. For example, these providers may have limited English proficiency themselves and/or lack familiarity with U.S. business procedures and formal jargon. They can also face additional costs for translating materials for submission to state officials for evaluation, such as parent handbooks.⁴³ To foster equitable QRIS, states must recognize these additional strains and proactively partner with diverse providers serving DLLs, offering additional technical assistance.

In addition, since one of the main goals of states’ QRIS is to facilitate parent awareness of ECE quality,⁴⁴ states should pay attention to how truly accessible this data is for non-English-speaking families. Most critically, translation of states’ online platforms to communicate QRIS ratings and other documents is severely lacking. According to Char Goodreau, senior technical assistance specialist with the National Center on Early Childhood Quality Assurance, several states rely on Google Translate. Although better than nothing, this approach does not consistently and reliably convert all information. Some states have translated and posted key documents while others have relatively little to no translated materials online.⁴⁵ “Translation is not only expensive but there are often limits on which documents can legally be translated,” Goodreau said, referring to copyright issues.⁴⁶

Finally, even with translation, the way QRIS ratings are reported do not make it immediately obvious for families which programs will specifically serve DLLs well. State leaders have access to the data on DLL-specific indicators (when they exist) to drive improvement efforts internally, but they typically do not report out this granular data to the public, indicator by indicator.⁴⁷ For the DLL population, states should consider bundling the data on all DLL-related indicators and reporting out a DLL “subscore.”⁴⁸ Maryland has also created an “additional achievement” badge for cultural and linguistic competency, which providers can apply for and parents use to filter results when using the QRIS online search. Similarly, Illinois has an “Award of Excellence for Linguistic and Culturally Appropriate Practice” that programs can earn as part of their QRIS.⁴⁹ These approaches can help convey a clearer, quicker sense of a program’s commitment to serving DLLs. Although, again, if the core QRIS website is not translated, this feature will be unhelpful for many DLL parents.

In addition to gaps in tracking the enrollment of DLLs and rating the quality of services for these learners, there is also a lack of meaningful assessment data to validly capture the full range of DLLs’ development in ECE.

Age-appropriate testing of students’ proficiencies can serve many purposes in ECE, including formative assessment for instruction, screening for special needs, or program-wide research or evaluations.⁵⁰ State policy leaders are increasingly focused on student outcomes through more standardized assessment data, collected and aggregated at the systems level, to inform decision-making and the allocation of ECE resources.

Kindergarten readiness assessments (KRAs), in particular, have gained traction as a strategy to provide educators, families, and district and state leaders with more standardized data on the status of children’s abilities when they enter kindergarten—a “snapshot on development,” according to the BUILD Initiative.⁵¹ KRAs are intended to both support instruction in the early elementary years and provide information that can help policy leaders support school readiness, not to prevent children from enrolling in school.⁵²

More than 40 states are currently developing or implementing KRAs, up from just seven in 2010.⁵³ The Obama administration promoted states’ adoption of KRAs through the federal Race to the Top – Early Learning Challenge, a discretionary grant program launched in 2011. In part, the grant program encouraged states to measure children’s outcomes in a range of developmental domains in tandem with “implementing comprehensive data systems and using data to improve instruction, practices, services, and policies.”⁵⁴ At least 25 states now mandate KRA use by state law.⁵⁵

States are rolling out their KRA systems in a variety of ways. Some use a commercial assessment tool, such as Teaching Strategies GOLD®, while others created their own state tool or participated in one of three interstate consortia supported by federal grants.⁵⁶ The tools can involve direct assessment (requiring a direct interaction between the test administrator and the child), observation of the child in authentic classroom activities, or a combination of these two approaches.⁵⁷

KRAs have weathered a fair amount of concerns and pushback in their roll-out. Some teachers experienced the testing as an extra burden.⁵⁸ Researchers cautioned against drawing inferences from a one-time assessment of young children when development is in great flux. As a National Education Goals Panel report asserted, “the younger the child, the more difficult it is to obtain reliable and valid assessment data. It is particularly difficult to assess children’s cognitive abilities accurately before age 6.”⁵⁹ Due to questions of validity and reliability, policy experts have also stressed that KRAs should not be used punitively as an accountability measure for ECE providers.⁶⁰

These broader concerns over KRAs have implications for all children—including DLLs. But states also need to specifically consider how to incorporate DLLs in KRAs as a special population. The development of bilingual children looks fundamentally different than their monolingual peers given that DLLs’ knowledge and skills are spread across two languages. For example, research suggests that DLLs have smaller vocabularies in English and their home language when taken separately, but their total vocabulary sizes—the sum of what they know in both languages—are similar to monolingual peers.⁶¹ As such, it is critical that young DLLs have an opportunity to show what they know and can do in their home language.

And yet, at present, almost all state KRAs assess children only in English. “If we want equitable assessments for DLLs, we have to assess across both languages,” said DLL expert Linda Espinosa, who works with states and districts across the country and served on the National Technical Advisory Committee for KRAs.⁶² She said that nearly all states are failing “to take on the challenge of creating truly equivalent [bilingual] forms of these test items,” a complicated, costly endeavor in the context of tight state budgets.⁶³ According to the Migration Policy Institute, New Jersey, Oregon, and Texas have developed KRAs entirely in Spanish while Illinois and Washington state allow DLLs to be assessed in their home language for some test items.⁶⁴ Bilingual KRA testing also goes hand-in-hand with a need for more bilingual, bicultural assessors, which requires additional investments.

While most states have not even attempted to do so, even those pursuing bilingual KRA assessments tend to use direct translation of the English version, Espinosa said. This method can produce tests that are psychometrically unsound (e.g. not normed or validated on DLLs), irrelevant in content for linguistically and culturally diverse children (e.g. asking a DLL in Alaska to recognize a beach umbrella on a vocabulary test), or both.⁶⁵ For these reasons, truly bilingual KRA testing for DLLs will likely remain a long-term challenge.

Still, KRA implementation needs major improvements even in English-only testing contexts to collect higher-quality DLL data. The U.S. Department of Education highlighted this reality in a 2016 case study of four states’ initial implementation of KRAs. Teachers reported that they did not fully understand guidelines for assessing DLLs, and a majority were unsure about testing procedures for DLLs. For example, some states allow certain testing accommodations for DLLs, such as accepting correct answers in non-verbal forms like pointing or gesturing.⁶⁶ Teachers also voiced a desire for greater support, such as more explicit training on administering KRAs with DLLs and on-site assistance from bilingual staff.⁶⁷

In addition to serving DLLs more equitably at the stage of assessment, state leaders should consider how to share and disseminate data on DLLs’ KRA results. Decision-making around how to publish KRA results often reflects the different ways states view KRAs and their purpose. “Whether to publicly report [KRA] data and what should be included…is often a complicated discussion, involving many stakeholders,” concluded ECE researcher G.G. Weisenfeld in a 2017 report.⁶⁸

Some states, like Maryland and Oregon, publish KRA data publicly on state websites.⁶⁹ Washington state also uses KRA data on its state report card, but officials “recognized the paradox of reporting formative assessment data in a summative presentation, and landed on multiple ways to accurately portray the data…opt[ing] not to use a single, composite ‘readiness’ score.”⁷⁰ Others states have resisted aggregating or reporting out results. New Jersey, for example, emphasizes that its voluntary KRA is a formative tool to inform instruction and professional development—not to publicize achievement gaps and trends. Michigan similarly states that it finds such summative use “inappropriate.”⁷¹

For DLLs in states that do decide to publicly report, state leaders must also decide whether to disaggregate the data results by DLL status for public users. In the K-12 context, federal law requires the disaggregation of academic data by English learner (EL) status in grades 3-8 (as well as by race and ethnicity, family income, and disability status). Civil rights groups stress the importance of these mandates for ensuring that disadvantaged students do not get ignored or masked in data systems. At the same time, for ELs at lower proficiencies, language barriers will definitionally interfere with their academic performance and thereby drive down the subgroups’ results.

Similarly, KRA systems leaders can use subgroups to expose achievement gaps in ECE, but they also must consider if data is reliable and valid enough to do so.⁷² As New America stressed in a 2017 report on K-12 EL data,⁷³ below a certain threshold of English proficiency, it is impossible to make valid claims about academic proficiencies in English. In the case of English-only KRAs, DLLs’ scores in literacy and math development may reflect English proficiencies rather than true knowledge of concepts and skills. If states publicly report KRA data without this context, and rely on “native English speakers as the norm against which all students are compared, the unique characteristics of DLLs are likely to be misinterpreted, or worse, determined to be delays,” according to DLL expert Espinosa. Again, this is why testing young DLLs bilingually is so critical—to capture an accurate, complete picture of their development.

Above: Maryland disaggregates by DLL/EL status in its public reporting of KRA data. The gaps between EL and English proficient kindergarteners are significantly greater in literacy and math than in physical and social domains (which are less language-dependent), perhaps indicating the extent to which language interferes with DLL data’s validity. As the report notes, “Because the KRA is not given in the student’s home language, the knowledge and skills of ELs may not be fully captured.”

Across a variety of domains, states need better data to more equitably serve DLLs in the early years. 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 language screening and administration of a family interview/survey, 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 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 ECE 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 gauge 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 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 young learners.