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In addition to tracking the enrollment of DLLs in public early care and education (ECE)—as explored in the previous blog of this series—state leaders should also collect data on the quality of ECE 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. For example, parents often struggle to accurately assess the quality of their children’s program, suggesting a need for what at University of Virginia’s EdPolicyWorks call “informational interventions in ECE markets.”

In recent years, states have turned to quality rating and improvement systems (QRIS) as a data-driven strategy along these lines. implemented in Oklahoma in 1998, QRIS have spread substantially in the last two decades. From 2012-2016, the federal government 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. Though some questions linger about their overall validity, now has such a system in place or is developing one, with 81,000 participating programs across the U.S. (see the map below from the ).

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, 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. Like rating systems for hotels or restaurants, QRIS evaluates providers on a continuum of multiple indicators (also referred to as standards) across various , such as health and safety, learning environment, staff qualifications, family partnership, and more, and then shares those results publicly. In addition to posting ratings online for families and the general public, leaders also use QRIS data to support quality with providers, such as through coaching, professional development opportunities, and other financial incentives.

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 . 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 and the For Law and Social Policy, DLL-specific criteria in QRIS might address whether ECE programs:

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

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

However, not all states are including DLL criteria in their QRIS ratings. In 2017, the National Center on Early Childhood Quality Assurance that only 40 percent of QRIS currently include any DLL-related indicators. 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.

The structuring of QRIS ratings can also de-prioritize the significance of DLL indicators even when such indicators are present. Seventeen states use a “” 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. 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.  

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 that state policies mandate participation for certain providers. 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 providers—ones that disproportionately serve DLLs—QRIS participation often comes with . 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. 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 of states’ QRIS is to facilitate parent awareness of ECE quality, 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. “Translation is not only expensive but there are often limits on which documents can legally be translated,” Goodreau said, referring to copyright issues.

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. 

For the DLL population, states should bundling the data on all DLL-related indicators and reporting out a DLL “subscore.” 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, has an “Award of Excellence for Linguistic and Culturally Appropriate Practice” that programs can earn as part of their QRIS. 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.

On Maryland’s QRIS website, users can search by “Cultural and Linguistic Competency” achievement for DLLs. However, the is not translated for DLL families.

Recommendations for State Leaders:

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

Stay tuned for the fourth blog in this series, which will examine gaps in assessing kindergarten readiness outcomes for DLLs.